the feasibility of launching and detecting jamming attacks ... · the feasibility of launching and...

The Feasibility of Launching and DetectingJamming Attacks in Wireless Networks

Wenyuan Xu, Wade Trappe, Yanyong Zhang, Timothy Wood,WINLAB, Rutgers University

Mobihoc 2005

IAB, June 8th, 2005

2

Roadmap Motivation and Introduction

Jammer Models– Four models– Their effectiveness

Basic Statistics for Detecting

Improved Jamming Detection Strategy

Conclusions & Future works

3

Jamming Style DoS

Bob AliceHello … Hi …

4

Jamming Style DoS

Bob AliceHello … Hi …

@#$%)$*#@&…

Mr. X

5

Jammers

Jamming style DoS Attack:– Behavior that prevents other nodes from using the

channel to communicate by occupying the channel that they are communicating on

A jammer– An entity who is purposefully trying to interfere with

the physical transmission and reception of wireless communications.

Is it hard to build a jammer?

Mr. X

No! Haha…

Bob Alice

Hello … Hi …@#$%%$#

@&…

Mr. X

6

Jammers – Hardware Cell phone jammer unit:– Intended for blocking all mobile phone

types within designated indoor areas – 'plug and play' unit

Waveform GeneratorTune frequency to what ever you want

MAC-layer Jammer (our focus)Mica2 Motes (UC Berkeley)

8-bit CPU at 4MHz,128KB flash, 4KB RAM916.7MHz radioOS: TinyOS

Disable the CSMAKeep sending out the preamble

7



Waveform Generator– Tune frequency to what ever you want

MAC-layer Jammer (our focus)Mica2 Motes (UC Berkeley)


Disable the CSMAKeep sending out the preamble

8



Waveform Generator– Tune frequency to what ever you want

MAC-layer Jammer (our focus)– Mica2 Motes (UC Berkeley)


– Disable the CSMA– Keep sending out the preamble

The Jammer Models and Their Effectiveness

10

Jammer Attack Models

Need tosend m

Is channel

idle?Backoff

start tosend m

No

Yes

Is channel

idle?Backoff

No

Yes

Normal MAC protocol:

Need tosend m

start tosend m

Jammer:

11


Constant jammer:– Continually emits a radio signal– It can prevent legitimate nodes from getting hold of channel, if the

underlying MAC protocol determines whether a channel is idle or not by comparing the signal strength measurement with a fixed threshold.

Deceptive jammer:– Constantly injects regular packets to the channel without any gap

between concatenated packet transmissions– A normal communicator will be deceived into the receive state

&F*(SDJFFD(*MC*(^%&^*&(%*)(*)_*^&*FS…….

Payload …

Preamble CRC

PayloadPayload Payload Payload

12


Random jammer:– Alternates between sleeping and jamming

Sleeping period: turn off the radioJamming period: either a constant jammer or deceptive jammer

– Good for those jammers that do not have unlimited power supply

Reactive jammer:– No need to jam the channel if nobody is communicating– Stays quiet when the channel is idle, starts transmitting a radio

signal as soon as it senses activity on the channel.– Targets the reception of a message– Harder to detect

&F*(SDJF ^F&*D( D*KC*I^ …

…

Underling normal traffic

&F*(SDJ

Payload

^%^*&

Payload

CD*(&FG

Payload

13

Metrics & ImplementationGoal of jammer:– Interfere with legitimate wireless communications– Prevent a sender from sending out packets– Prevent a receiver from receiving a legitimate packets

Packet Send Ratio (PSR)– The ratio of packets that are successfully sent out by a legitimate

traffic source compared to the number of packets it intends to send out in MAC layer

Packet Delivery Ratio (PDR)– The ratio of packets that are successfully delivered to a destination

compared to the number of packets that have been sent out by thesender

Implementation platform:– Mica2 Motes– Disabled channel sensing and backoff operation in TinyOS MAC

protocol

14

Experiment SetupInvolving three parties:– Normal nodes:

Sender AReceiver B

– Jammer X

Parameters – Four jammers model– Distance

Let dXB = dXA

Fix dAB at 30 inches– Power

PA = PB = P X = -4dBm– MAC

Fix MAC thresholdAdaptive MAC threshold (BMAC)

Sender A

Receiver B

Jammer X

dXB

dAB

dXA

15

Experimental ResultsInvolving three parties:– Normal nodes:

Sender AReceiver B

– Jammer X

Parameters – Four jammers models– Distance

Let dXB = dXA




3.260.9293.5799.5772.0

2.911.020.5377.1754.0

1.941.000.4374.3738.6

PDR(%)PSR(%)PDR(%)PSR(%)

FixMACBMACdxa (inch)

Constant Jammer

99.53100.098.0099.2554.0

87.26100.058.0599.0044.0

0.00100.00.0099.0038.6m =

33bytes

99.87100.099.35100.072.0

99.87100.099.24100.054.0

0.00100.00.0099.0038.6m =

7bytes



Reactive Jammer

16


Sender AReceiver B

– Jammer X


Let dXB = dXA




3.260.9293.5799.5772.0

2.911.020.5377.1754.0

1.941.000.4374.3738.6



Constant Jammer

99.53100.098.0099.2554.0

87.26100.058.0599.0044.0

0.00100.00.0099.0038.6m =

33bytes

99.87100.099.35100.072.0

99.87100.099.24100.054.0

0.00100.00.0099.0038.6m =

7bytes



Reactive Jammer

17


Sender AReceiver B

– Jammer X


Let dXB = dXA




3.260.9293.5799.5772.0

2.911.020.5377.1754.0

1.941.000.4374.3738.6



Constant Jammer

99.53100.098.0099.2554.0

87.26100.058.0599.0044.0

0.00100.00.0099.0038.6m =

33bytes

99.87100.099.35100.072.0

99.87100.099.24100.054.0

0.00100.00.0099.0038.6m =

7bytes



Reactive Jammer

18


Sender AReceiver B

– Jammer X


Let dXB = dXA




3.260.9293.5799.5772.0

2.911.020.5377.1754.0

1.941.000.4374.3738.6



Constant Jammer

99.53100.098.0099.2554.0

87.26100.058.0599.0044.0

0.00100.00.0099.0038.6m =

33bytes

99.87100.099.35100.072.0

99.87100.099.24100.054.0

0.00100.00.0099.0038.6m =

7bytes



Reactive Jammer

Basic Statistics for Detecting Jamming Attacks

20

Signal Strength P.1

Idea:– The signal strength distribution may be affected by the

presence of a jammer

Assume– Network devices can gather enough noise level

measurements during a time period prior to jamming and build a statistical model describing normal energy levels in the network.

Statistical model– Average signal value or the total signal energy over a

window– Signal strength spectral discrimination

Experiment platform:– Mica2 Motes (UC Berkeley) – Use RSSI ADC to measure the signal strength

21

Signal Strength P.2

-100

-80

-60CBR

-100

-80

-60MaxTraffic

-100

-80

-60Constant Jammer

-100

-80

-60

R

SS

I (dB

m)

Deceptive Jammer

-100

-80

-60Reactive Jammer

0 200 400 600 800 1000 1200 1400 1600-100

-80

-60

sample sequence number

Random Jammer

Normal traffic

Jammers

Basic average

detection doesn’t work !

Congested traffic

22

Signal Strength P.3

Basic Average and Energy Detection don’t work!How about spectral discrimination mechanism?– Higher Order Crossing (HOC)

The idea is to combine zero-crossing counts in stationary time series with linear filters .We calculated the first two higher order crossings for the time series.Window size: 240 samples

0 50 100 150 2000

50

100

150

200

HOC

D1

D2

CBRMaxTrafficConstant JammerDeceptive Jammer

0 50 100 150 2000

50

100

150

200

HOC

D1

D2

CBRMaxTrafficReactive JammerRandom Jammer

SS spectral discrimination doesn’t work !

23

Packet Delivery Ratio P.1

Carrier sensing time cannot detect reactive jammer.

Idea:– Determine whether the communication node can receive

packets in the way it should have had the jammer not been present.

– A non-aggressive jammer, which only marginally affects the PDR, does not need to be detected or defended against.

How much PDR degradation can be caused by non-jamming, normal network dynamics, such as congestion?

Experiment– Setup

3 MaxTraffic sources– Raw offered traffic rate: 19.38Kbps– Max allowed bandwidth: 12.364kbps

Measure PDR at receiver side– Result

PDR: 78%

MaxTrafficSender

Receiver

24

Packet Delivery Ratio P.2

The PDRs are low in the presence of jammers

PDR is effective in discriminating jamming from congested network scenario.

Low PDR can be caused by network dynamics:– Sender battery failure– Sender moving out of the

communication range

PDR cannot differentiate jamming attacks from other scenarios, such as, poor link quality.

3.260.9293.5799.5772.0

2.911.020.5377.1754.0

1.941.000.4374.3738.6



Constant Jammer

99.53100.098.0099.2554.0

87.26100.058.0599.0044.0

0.00100.00.0099.0038.6m =

33bytes

99.87100.099.35100.072.0

99.87100.099.24100.054.0

0.00100.00.0099.0038.6m =

7bytes



Reactive Jammer

0.000.000.000.0072.0

0.000.000.000.0054.0

0.000.000.000.0038.6



Deceptive Jammer

Jamming Detection with Consistency Checks

26

Signal Strength Consistency Checks P.1Goal — to discriminate jamming attacks from,– normal congested scenarios– other cases caused by poor link quality, sudden failures of nodes

Observation:– PDR is a relative good statistic, we can build some strategies upon

PDR to achieve enhanced jammer detection.– Normal scenarios:

High signal strength a high PDR Low signal strength a low PDR

– Low PDR:Hardware failure or poor link quality low signal strengthJamming attack high signal strength

Idea:– Node A checks whether all its neighbors share low PDRs with itself. – If at least one neighbor has high PDR, Node A is not jammed. – Otherwise, check whether the low PDR is consistent with the

ambient signal strength Node A measures. – If the PDR is low but signal strength is high, node A is jammed.– If both are low, probably there are other reasons.

27

Signal Strength Consistency Checks P.2Assumption:– A node is only responsible for detecting whether it is

jammed, and not its neighbors– The network is sufficiently dense, each node has several

neighbors– Each node maintains a neighbor list – All normal nodes in the network will send out heartbeat

beacons, such as routing updates.

Algorithm:{PDR(N): N Є Neighbors} = Mearsure_PDR()MaxPDR = max{PDR(N): N Є Neighbors}if MaxPDR < PDRThresh then

SS = Sample_Signal_Strength()CCheck = SS_ConsistencyCheck(MaxPDR, SS)if CCheck == False then

post NodeIsJammed() end

end

28

Signal Strength Consistency Checks P.3Sample_Signal_Strength() returns the maximum value of the signal strengths during the sampling window.

SS_ConsistencyCheck(MaxPDR, SS) performs a consistent checking to see whether the low MaxPDRvalues are consistent with SS, the signal strength measurements.

How does a consistency checking work?

Algorithm:

{PDR(N): N Є Neighbors} = Mearsure_PDR()MaxPDR = max{PDR(N): N Є Neighbors}if MaxPDR < PDRThresh then

SS = Sample_Signal_Strength()CCheck = SS_ConsistencyCheck(MaxPDR, SS)if CCheck == False then

post NodeIsJammed() end

end

29

Signal Strength Consistency Checks P.4Build a (PDR,SS) look-up table empirically– Measure (PDR, SS) during a guaranteed time of non-interfered

network.– Divide the data into PDR bins, calculate the mean and variance for

the data within each bin.– Get the upper bound for the maximum SS that world have

produced a particular PDR value during a normal case.– Partition the (PDR, SS) plane into a jammed-region and a non-

jammed region.

Experiment setup:– The sender power: -

5dBm– Data rate: 20packets/sec– Average PDR over 200

packets– SS were sampled every

1msec for 200msecs– Vary the DSR– PDR bins: (0,40) (40,90)

(90, 100)– PDR threshold 65%– 99% confidence bar

Jammed Region

PDR %

PDR VS. SS

SS

(dB

m)

30

Signal Strength Consistency Checks P.5Jammer setup:– Transmission power: -4dBm– The reactive jammer injects 20-byte long packets– The random jammer turns on for tj = U[0,31] and turns off for ts =

U[0,31]

The (PDR, SS) values for all jammers distinctively fall within the jammed-region

The more aggressive the jammer is, the more likely it will be detected.

The less aggressive the jammer is, the less damage it causes to the network.

Similarly, we can deploy a location information based consistency check to achieve an enhanced jamming detection.

Jammed Region

PDR %

PDR VS. SS

SS

(dB

m)

31

Conclusions:Due to the shared nature of the wireless medium, it is an easy feat for adversaries to perform a jamming-style denial of service against wireless networks.

We presented four different jammer attack models. We have studies the effectiveness of them by constructing prototypes using the MICA2 Mote platform and measured the PSR and PDR.

We have studied the issue of detecting jamming attacks.– We showed that a single measurement statistic is not enough to

identify the presence of a jammer. – We introduced the notion of consistency checks– We presented two enhanced jamming detection algorithms:

Employing signal strength as a consistency checkEmploying location information as a consistency check

32

Future Works:Investigate the effectiveness of different jamming attack models in other wireless devices, e.g. 802.11 devices, and study their effectiveness in different wireless network topology.– Infrastructured network– Ad-hoc network

Study the jamming detection mechanism in other scenarios: – Highly mobile jammers– Highly mobile network nodes

Validate the jamming detection mechanism in a large scale sensor network

We are building a large scale jamming resistant wireless sensor network (approximately 50 nodes)

the feasibility of launching and detecting jamming attacks ... · the feasibility of launching and...

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