Data Mining for Security Applications:

Detecting Malicious Executables

Mr. Mehedy M. Masud (PhD Student)Prof. Latifur Khan

Prof. Bhavani Thuraisingham

Department of Computer ScienceThe University of Texas at Dallas

Outline and Acknowledgement● Vision for Assured Information Sharing● Handling Different Trust levels● Defensive Operations between Untrustworthy

Partners– Detecting Malicious Executables using Data Mining

● Research Funded by Air Force Office of Scientific Research and Texas Enterprise Funds

Vision: Assured Information Sharing

PublishData/Policy

ComponentData/Policy for

Agency A

Data/Policy for Coalition

PublishData/Policy

ComponentData/Policy for

Agency C

ComponentData/Policy for

Agency B

PublishData/Policy

1. Trustworthy Partners

2. Semi-Trustworthy partners

3. Untrustworthy partners

4. Dynamic Trust

Our Approach● Integrate the Medicaid claims data and mine the data; next

enforce policies and determine how much information has been lost by enforcing policies

– Prof. Khan, Dr. Awad (Postdoc) and Student Workers (MS students)

● Apply game theory and probing techniques to extract information from semi-trustworthy partners

– Prof. Murat Kantarcioglu and Ryan Layfield (PhD Student)

● Data Mining for Defensive and offensive operations– E.g., Malicious code detection, Honeypots– Prof. Latifur Khan and Mehedy Masud

● Dynamic Trust levels, Peer to Peer Communication– Prof. Kevin Hamlen and Nathalie Tsybulnik (PhD student)

Introduction: Detecting Malicious Executables using Data Mining

0 What are malicious executables?- Harm computer systems- Virus, Exploit, Denial of Service (DoS), Flooder, Sniffer,

Spoofer, Trojan etc.- Exploits software vulnerability on a victim - May remotely infect other victims- Incurs great loss. Example: Code Red epidemic cost $2.6

Billion

0 Malicious code detection: Traditional approach- Signature based- Requires signatures to be generated by human experts- So, not effective against “zero day” attacks

State of the Art: Automated

Detection

OAutomated detection approaches:●Behavioural: analyse behaviours like source, destination address, attachment type, statistical anomaly etc.

●Content-based: analyse the content of the malicious executable– Autograph (H. Ah-Kim – CMU): Based on automated

signature generation process– N-gram analysis (Maloof, M.A. et .al.): Based on mining

features and using machine learning.

New Ideas

✗Content -based approaches consider only machine-codes (byte-codes).✗Is it possible to consider higher-level source codes for malicious code detection?✗Yes: Diassemble the binary executable and retrieve the assembly program✗Extract important features from the assembly program✗Combine with machine-code features

Feature Extraction

✗Binary n-gram features– Sequence of n consecutive bytes of binary executable

✗Assembly n-gram features– Sequence of n consecutive assembly instructions

✗System API call features– DLL function call information

The Hybrid Feature Retrieval Model

● Collect training samples of normal and malicious executables.

● Extract features

● Train a Classifier and build a model

● Test the model against test samples

Hybrid Feature Retrieval (HFR)

● Training

Hybrid Feature Retrieval (HFR)

● Testing

Binary n-gram features– Features are extracted from the byte codes in the form of

n-grams, where n = 2,4,6,8,10 and so on.

Example: Given a 11-byte sequence:

0123456789abcdef012345, The 2-grams (2-byte sequences) are: 0123, 2345, 4567,

6789, 89ab, abcd, cdef, ef01, 0123, 2345The 4-grams (4-byte sequences) are: 01234567, 23456789,

456789ab,...,ef012345 and so on....

Problem: – Large dataset. Too many features (millions!).

Solution: – Use secondary memory, efficient data structures – Apply feature selection

Feature Extraction

Assembly n-gram features– Features are extracted from the assembly programs in

the form of n-grams, where n = 2,4,6,8,10 and so on.

Example:

three instructions “push eax”; “mov eax, dword[0f34]” ; “add ecx, eax”;

2-grams(1) “push eax”; “mov eax, dword[0f34]”;

(2) “mov eax, dword[0f34]”; “add ecx, eax”;

Problem: – Same problem as binary

Solution: – Same solution

Feature Extraction

● Select Best K features

● Selection Criteria: Information Gain● Gain of an attribute A on a collection of

examples S is given by

Feature Selection

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AValuesVv

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SSEn trop yASG a in

Experiments

0 Dataset– Dataset1: 838 Malicious and 597 Benign executables– Dataset2: 1082 Malicious and 1370 Benign executables– Collected Malicious code from VX Heavens

(http://vx.netlux.org)0 Disassembly

– Pedisassem ( http://www.geocities.com/~sangcho/index.html )

0 Training, Testing– Support Vector Machine (SVM)– C-Support Vector Classifiers with an RBF kernel

Results

● HFS = Hybrid Feature Set● BFS = Binary Feature Set● AFS = Assembly Feature Set

Results

● HFS = Hybrid Feature Set● BFS = Binary Feature Set● AFS = Assembly Feature Set

Results

● HFS = Hybrid Feature Set● BFS = Binary Feature Set● AFS = Assembly Feature Set

Future Plans

● System call: – seems to be very useful. – Need to Consider Frequency of call– Call sequence pattern (following program path) – Actions immediately preceding or after call

● Detect Malicious code by program slicing– requires analysis

Data Mining to Detect Buffer Overflow Attack

Mohammad M. Masud, Latifur Khan,

Bhavani Thuraisingham

Department of Computer ScienceThe University of Texas at Dallas

Introduction

● Goal– Intrusion detection. – e.g.: worm attack, buffer overflow attack.

● Main Contribution– 'Worm' code detection by data mining coupled

with 'reverse engineering'.– Buffer overflow detection by combining data

mining with static analysis of assembly code.

Background

● What is 'buffer overflow'?– A situation when a fixed sized buffer is overflown

by a larger sized input.

● How does it happen?– example:

........char buff[100];gets(buff);........

buff Stackmemory

Input string

Background (cont...)

● Then what?

........char buff[100];gets(buff);........

buff Stackmemory

Stack

Return address overwritten

buff Stackmemory

New return address points to this memory location

Attacker's code

buff

Background (cont...)

● So what?– Program may crash or– The attacker can execute his arbitrary code

● It can now– Execute any system function– Communicate with some host and download

some 'worm' code and install it!– Open a backdoor to take full control of the victim

● How to stop it?

Background (cont...)● Stopping buffer overflow

– Preventive approaches– Detection approaches

● Preventive approaches– Finding bugs in source code. Problem: can only

work when source code is available.– Compiler extension. Same problem.– OS/HW modification

● Detection approaches– Capture code running symptoms. Problem: may

require long running time.– Automatically generating signatures of buffer

overflow attacks.

CodeBlocker (Our approach)

● A detection approach

● Based on the Observation:– Attack messages usually contain code while

normal messages contain data.

● Main Idea– Check whether message contains code

● Problem to solve:– Distinguishing code from data

Severity of the problem

● It is not easy to detect actual instruction sequence from a given string of bits

Our solution

● Apply data mining.

● Formulate the problem as a classification problem (code, data)

● Collect a set of training examples, containing both instances

● Train the data with a machine learning algorithm, get the model

● Test this model against a new message

CodeBlocker Model

Feature Extraction

Disassembly

● We apply SigFree tool – implemented by Xinran Wang et al. (PennState)

Feature extraction

● Features are extracted using– N-gram analysis– Control flow analysis

● N-gram analysis

Assembly program Corresponding IFG

What is an n-gram? -Sequence of n instructions

Traditional approach: -Flow of control is ignored

2-grams are: 02, 24, 46,...,CE

Feature extraction (cont...)

● Control-flow Based N-gram analysis

Assembly program Corresponding IFG

What is an n-gram? -Sequence of n instructions

Proposed Control-flow based approach -Flow of control is considered

2-grams are: 02, 24, 46,...,CE, E6

Feature extraction (cont...)● Control Flow analysis. Generated features

– Invalid Memory Reference (IMR)– Undefined Register (UR)– Invalid Jump Target (IJT)

● Checking IMR– A memory is referenced using register

addressing and the register value is undefined– e.g.: mov ax, [dx + 5]

● Checking UR– Check if the register value is set properly

● Checking IJT– Check whether jump target does not violate

instruction boundary

Feature extraction (cont...)

● Why n-gram analysis?– Intuition: in general,

disassembled executables should have a different pattern of instruction usage than disassembled data.

● Why control flow analysis?– Intuition: there should be no invalid memory

references or invalid jump targets.

Putting it together

● Compute all possible n-grams

● Select best k of them

● Compute feature vector (binary vector) for each training example

● Supply these vectors to the training algorithm

Experiments

● Dataset– Real traces of normal messages– Real attack messages – Polymorphic shellcodes

● Training, Testing– Support Vector Machine (SVM)

Results

● CFBn: Control-Flow Based n-gram feature● CFF: Control-flow feature

Novelty / contribution

● We introduce the notion of control flow based n-gram

● We combine control flow analysis with data mining to detect code / data

● Significant improvement over other methods (e.g. SigFree)

Advantages

● 1) Fast testing

● 2) Signature free operation

3) Low overhead

● 4) Robust against many obfuscations

Limitations

● Need samples of attack and normal messages.

● May not be able to detect a completely new type of attack.

Future Works

● Find more features

● Apply dynamic analysis techniques

● Semantic analysis

Reference / suggested readings

– X. Wang, C. Pan, P. Liu, and S. Zhu. Sigfree: A signature free buffer overflow attack blocker. In USENIX Security, July 2006.

– Kolter, J. Z., and Maloof, M. A. Learning to detect malicious executables in the wild Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining Seattle, WA, USA Pages: 470 – 478, 2004.

Email Worm Detection (behavioural approach)

Training data

Feature extraction

Clean or Infected ?

Outgoing Emails

ClassifierMachine Learning

Test data

The Model

Feature Extraction

Per email features=Binary valued Features

Presence of HTML; script tags/attributes; embedded images; hyperlinks;

Presence of binary, text attachments; MIME types of file attachments

=Continuous-valued FeaturesNumber of attachments; Number of words/characters in the

subject and bodyPer window features

=Number of emails sent; Number of unique email recipients; Number of unique sender addresses; Average number of words/characters per subject, body; average word length:; Variance in number of words/characters per subject, body; Variance in word length

=Ratio of emails with attachments

Feature Reduction & Selection

Principal Component Analysis=Reduce higher dimensional data into lower dimension=Helps reducing noise, overfitting

Decesion Tree=Used to Select Best features

Experiments

0 Data Set - Contains instances for both normal and viral emails.– Six worm types:

● bagle.f, bubbleboy, mydoom.m, mydoom.u, netsky.d, sobig.f

- Collected from UC Berkeley

● Training, Testing:

- Decision Tree: C4.5 algorithm (J48) on Weka Systems

- Support Vector Machine (SVM) and Naïve Bayes (NB).

Results

Conclusion & Future Work

● Three approaches has been tested– Apply classifier directly – Apply dimension reduction (PCA) and then

classify– Apply feature selection (decision tree) and then

classify

● Decision tree has the best performance● Future Plans

– Combine content based with behavioral approaches

● Offensive Operations– Honeypots, Information operations