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Reactively Adaptive MalwareWhat is it?
How do we detect it?
Dr. Bhavani ThuraisinghamCyber Security Research and Education Institute
https://csi.utdallas.edu
The University of Texas at Dallas
April 19, 2013
1FEARLESS engineering
FEARLESS engineering
Outline
• Analogies
• Malware: What is it?
• Our Solutions– Profs. Thuraisingham, Khan, Hamlen, Lin, Makris,
Cardenas, Kantarcioglu
• Directions – Holistic Interdisciplinary Treatment
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Analogies: The Human Body• Humans infected with virus and
bacteria
• Virus replicates itself and spreads throughout the body
• Attacks vital organs
• Doctor conducts tests and detects the problem
• Medicine is given to slow the progress of the disease
• Patient’s condition may improve or the patient may die
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Analogies: An Organization
• Bad person joins the organization and pretends to be a good person
• He/she monitors what is going on and spies on the organization
• Conveys vital information to the adversary – insider threat
• Builds a network of bad people
• Takes over the organization
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What is a Malware?• It’s a piece of software that is malicious and
carries out bad things
• It infects a vulnerable and neglected machine
• It attacks the various components of the machine– the operating system (vital organs), applications (limbs) and hardware (bone)
• It spreads across a network of machines
• It cripples the machines and the network
• It conveys vital information to the enemy – the hacker
• It takes over the network and carries out its agenda
Victim Network
What does it look like?Example: Melissa Virus March 26, 1999
The Virus-Antivirus Arms Race• Malware (e.g., viruses)
– Rogue programs that carry out malicious actions on victim machines
• Vandalism (delete files, carry out phishing scams, etc.)• reconnaissance & secret exfiltration (cyber-warfare /
hacktivism)• Sabotage (e.g., attacks against power grids)
– Randomly mutate themselves automatically as they propagate
• Harder to detect since no two samples look identical• Antivirus defenses
– Defenders manually reverse-engineer many malware samples
– Find mutation patterns– Build defenses to automatically detect & quarantine all
mutants
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FEARLESS engineering
Incidents Reported 1990-2001
Incidents Reported to Computer Emergency Response Team/Coordination Center (CERT/CC)
0
10000
20000
30000
40000
50000
60000
90 91 92 93 94 95 96 97 98 99 00 01
Everything changed with Code Red attack in 2001
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Problem is much worse now!
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Our Malware Team
Adversarial Mining SolutionsProfessor Murat Kantarcioglu
Data Mining Solutionsfor MalwareProfessor Latifur Khan
Reactively Adaptive Malware and SolutionsProfessor Kevin Hamlen
Android Malware andSolutionsProfessor Zhiqiang Lin
Hardware Malwareand SolutionsProfessor Yiorgos Makris
Smart Grid Malwareand SolutionsProfessor Alvaro Cardenas
Data Mining Solutions
Data Mining
Knowledge Discoveryin Databases
Knowledge Extraction
Data Pattern Processing
The process of discovering meaningful new correlations, patterns, trends and nuggets by sifting through large amounts of attack data, often previously unknown, using pattern recognition technologies and machine learning statistical and mathematical techniques.
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Thuraisingham, Data Mining: Technologies, Techniques, Tools and Trends, CRC Press 1998
Training and Testing
Testing Data
DGSOT: Dynamically Growing Self-Organizing Tree Our novel solution
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TrainingData
Enhancements to current
data mining approachesHierarchical Clustering (DGSOT)
Testing
Data Mining Classification
ModelTraining
GoodClass
BadClass
• Supported by US Air Force 2005-2008
– PI: Thuraisingham, Co-PI: Khan
• Extract features
✗Binary n-gram features
✗Assembly n-gram features
Report Results: Example
• HFS = Hybrid Feature Set (Binary and Assembly)• BFS = Binary Feature Set• AFS = Assembly Feature Set
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Reactively Adaptive Malware: What is it?
• Next-generation Malware Technology
– Malware that mutates NON-randomly
– LEARNS and ADAPTS to antivirus defenses fully automatically in the wild
– Immune to conventional antivirus defenses
– Supported by the U.S. Air Force; 2010-2013
• PI: Hamlen, Co-PI: Khan
FEARLESS engineering
FEARLESS engineering
Data Mining-based Anti-antivirus[Hamlen & Khan]
Antivirus Signature Database
Signature Q
uery Interface
Signature Inference
Engine
Signature Approximation
Model
Obfuscation Generation
Obfuscation Function
Malware Binary
Obfuscated Binary
Testing propagate
“Frankenstein”[Mohan & Hamlen, USENIX WOOT, 2012]
• Stitch together code harvested from benign binaries to re-implement malware on each propagation.
• Many offensive advantages:– resulting malware is 100% metamorphic
• no common features between mutants
– statistically indistinguishable from benign-ware• everything is plaintext code (no cyphertexts)
– no runtime unpacking• evades write-then-execute protections
– obfuscation is targeted and directed• evolves to match infected system’s notion of
“benign”
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Frankenstein Press Coverage• Presented at USENIX Offensive Technologies (WOOT) mid-August 2012• Thousands of news stories in August/September
– The Economist, New Scientist, NBC News, Wired UK, The Verge, Huffington Post, Live Science, …
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Solution we are exploring: SNODMAL Solution we are exploring: SNODMAL Stream Based Novel Class DetectionStream Based Novel Class Detection
• Divide the data stream into equal sized chunks– Train a classifier from each data chunk– Keep the best L such classifier-ensemble
Data chunks
Classifiers
D1
C1
D2
C2
D3
C3
Ensemble C1 C2 C3
D4
Prediction
D4
C4C4
C4
D5D5
C5C5
C5
D6
Labeled chunk
Unlabeled chunk
Addresses infinite lengthand concept-drift
Note: Di may contain data points from different classes
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Smartphones can also beinfected with malware!
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Our Solution – Combine Static Analysis with Dynamic Analysis
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• Static Analysis– Data mining solutions
• Dynamic Analysis– Platform– Android & I-Phone– Reverse engineering
• Level– System call– Operating systems– Network
• Supported by US Air Force 2012-2016– Technical Leads Lin and Khan
Remote Server
Mal App
Network Behavior
App Behavior
We cannot forget about
HardwareDo you Trust
Your Chips?Yiorgos Makris
The Hacker in Your Hardware, Villasenor, Scientific American 2010
The Hunt for the Kill SwitchAdee, IEEE Spectrum, 2008 3500 counterfeit Cisco networking
components recovered
2012 Phobos-Grunt Mission Fails Due to Counterfeit Non Space-Rated Chips
Research Supported by:
Our Solution to Hardware Trojan
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That’s not all – Attacks to Critical Infrastructures
Attacks Maroochy Shire 2000
Threats
HVAC 2012
Stuxnet 2010
Smart Meters 2012
Obama administrationdemonstrates attack to power grid in Feb. 2012
DHS and INL study impact of cyber-attacks on generator
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New Attack-Detection Mechanisms by Incorporating “Physical Constraints” of the System
• 1st Step: Model the Physical World • 2nd Step: Detect Attacks– Compare received signal from
expected signalPhysical World
System ofDifferential Equations
Model
• 3rd Step: Response to Attacks • 4th Step: Security Analysis Missed Detections
Study stealthy attacks False Positives
Ensure safety of automated response
[Alvaro Cárdenas, et.al. AsiaCCS, 2011]
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It never ends!We need to mine the adversary
• Adversary changes its behavior to avoid being detected
• Data Miner and the Adversary are playing games
• Remember, malware detection is a two class problem?
•Good class (e.g., benign program)
•Bad class (e.g., malware)
• Adapt your classifier to changing adversary behavior
• Questions?–How to model this game? Does this game ever end?–Is there an equilibrium point in the game?
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Our Solution: Game Playing• Adversarial Stackelberg Game
– Adversary chooses an action
– After observing the action, data miner chooses a counteraction
– Game ends with payoffs to each player
• Adversary may use malware obfuscation
• Change has some cost to the adversary
• We need data mining techniques to handle the changes by the adversary
• Funded by the US Army; 2012-2015
– PI: Kantarcioglu, Co-PI: Thuraisingham
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FEARLESS engineering
Where do we go from here:Holistic Treatment
Three actors interacting with each other:
•The Doctor
– The Defender/Analyst
•The Patient
– The User /Soldier
•The Virus/Bacteria
– The Malware/Attacker
Together with ECS, SOM, EPPS and BBS, we are proposing an Interdisciplinary approach.