Transcript Lecture4 - The University of Texas at Dallas

Reactively Adaptive Malware
What is it?
How do we detect it?
Dr. Bhavani Thuraisingham
Cyber Security Research and Education Institute
https://csi.utdallas.edu
The University of Texas at Dallas
April 19, 2013
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1
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
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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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Incidents Reported 1990-2001
Incidents Reported to Computer Emergency
Response Team/Coordination Center (CERT/CC)
60000
50000
40000
30000
20000
10000
0
90
91
92
93
94
95
96
97
98
99
00
Everything changed with Code Red attack in 2001
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01
Problem is much worse now!
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Our Malware Team
Data Mining Solutions
for Malware
Professor Latifur Khan
Android Malware and
Solutions
Professor Zhiqiang Lin
Reactively Adaptive Malware
and Solutions
Professor Kevin Hamlen
Hardware Malware
and Solutions
Professor Yiorgos Makris
Adversarial Mining Solutions
Smart Grid Malware
Professor Murat Kantarcioglu
and Solutions
Professor Alvaro Cardenas
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Data Mining Solutions
Data Mining
Knowledge Discovery
in Databases
Data Pattern Processing
Knowledge Extraction
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.
Thuraisingham, Data Mining: Technologies, Techniques, Tools and Trends, CRC Press 1998
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Training and Testing
•
Extract features
✗Binary n-gram features
✗Assembly n-gram features
Training
Data
Enhancements
to current
data mining
approaches
Hierarchical
Clustering
(DGSOT)
Data Mining
Classification
Model
Training
Good
Class
Testing
Bad
Class
DGSOT: Dynamically Growing Self-Organizing Tree
Our novel solution
•
Supported by US Air Force 2005-2008
– PI: Thuraisingham, Co-PI: Khan
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Testing Data
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
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Data Mining-based Anti-antivirus
[Hamlen & Khan]
Signature Query Interface
Antivirus
Signature
Database
Signature
Inference
Engine
Signature
Approximation
Model
Obfuscation
Generation
Malware
Binary
Obfuscation
Function
Testing
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Obfuscated
Binary
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
Stream 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
Note: Di may contain data points from different classes
Labeled chunk
Data
chunks
D1
Classifiers
C1
Ensemble
D2
D543
D654
Unlabeled chunk
C1
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C2
C42
C53
C543
Addresses infinite length
and concept-drift
Prediction
Smartphones can also be
infected with malware!
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Our Solution – Combine Static Analysis
with Dynamic Analysis
• Static Analysis
– Data mining solutions
• Dynamic Analysis
– Platform
– Android & I-Phone
– Reverse engineering Mal App
• Level
App
– System call
Behavior
– Operating systems
– Network
• Supported by US Air Force 2012-2016
– Technical Leads Lin and Khan
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Network
Behavior
Remote
Server
The Hunt for the Kill Switch
Adee, IEEE Spectrum, 2008
We cannot
forget about
Hardware
Do you
Trust
Your Chips?
Yiorgos Makris
([email protected])
Research Supported by:
2012 Phobos-Grunt Mission Fails Due
to Counterfeit Non Space-Rated Chips
The Hacker in Your Hardware,
Villasenor, Scientific American 2010
Our Solution to Hardware Trojan
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That’s not all –
Attacks to Critical Infrastructures
 Attacks
 Maroochy Shire 2000
 HVAC 2012
 Stuxnet 2010
 Smart Meters 2012
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 Threats
Obama administration
demonstrates attack to
power grid in Feb. 2012
DHS and INL study impact of cyberattacks on generator
New Attack-Detection Mechanisms by Incorporating
“Physical Constraints” of the System
• 1st Step: Model the Physical World
Physical
World
• 2nd Step: Detect Attacks
– Compare received signal from
expected signal
Model
System of
Differential Equations
• 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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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.
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