Transcript Lecture3 - The University of Texas at Dallas

Data Mining for
Malware Detection
Dr. Mehedy Masud
Dr. Latifur Khan
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
May 29, 2015
4/12/2017 09:34
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Outline
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Data mining overview
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Intrusion detection, Malicious code detection, Buffer
overflow detection, Email worm detection (worms and
virus)
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Novel Class Detection for polymorphic malware
0
Reference:
-
Data Mining Tools for Malware Detection
-
Masud, Khan and Thuraisingham
-
CRC Press/Taylor and Francis, 2011
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What is Data Mining?
Information Harvesting
Knowledge Mining
Data Mining
Knowledge Discovery
in Databases
Data Dredging
Data Archaeology
Data Pattern Processing
Database Mining
Knowledge Extraction
Siftware
The process of discovering meaningful new correlations, patterns, and trends by
sifting through large amounts of data, often previously unknown, using pattern
recognition technologies and statistical and mathematical techniques
(Thuraisingham, Data Mining, CRC Press 1998)
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What’s going on in data mining?
0 What are the technologies for data mining?
- Database management, data warehousing, machine learning,
statistics, pattern recognition, visualization, parallel processing
0 What can data mining do for you?
- Data mining outcomes: Classification, Clustering, Association,
Anomaly detection, Prediction, Estimation, . . .
0 How do you carry out data mining?
- Data mining techniques: Decision trees, Neural networks,
Market-basket analysis, Link analysis, Genetic algorithms, . . .
0 What is the current status?
- Many commercial products mine relational databases
0 What are some of the challenges?
- Mining unstructured data, extracting useful patterns, web
mining, Data mining, security and privacy
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Data Mining for Intrusion Detection: Problem
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An intrusion can be defined as “any set of actions that attempt to
compromise the integrity, confidentiality, or availability of a resource”.
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Attacks are:
- Host-based attacks
- Network-based attacks
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Intrusion detection systems are split into two groups:
- Anomaly detection systems
- Misuse detection systems
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Use audit logs
- Capture all activities in network and hosts.
- But the amount of data is huge!
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Misuse Detection
0 Misuse Detection
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Problem: Anomaly Detection
0 Anomaly Detection
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Our Approach: Overview
Training
Data
Class
Hierarchical
Clustering (DGSOT)
SVM Class Training
Testing
DGSOT: Dynamically growing self organizing tree
Testing Data
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Our Approach: Hierarchical Clustering
Our Approach
Hierarchical clustering with SVM flow chart
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Results
Training Time, FP and FN Rates of Various Methods
Average
FP
Average
FN
Rate
(%)
Rate
(%)
Accuracy
Total
Training
Time
Random
Selection
52%
0.44 hours
40
47
Pure SVM
57.6%
17.34 hours
35.5
42
SVM+Rocchio
Bundling
51.6%
26.7 hours
44.2
48
SVM + DGSOT
69.8%
13.18 hours
37.8
29.8
Methods
Average
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Introduction: Detecting Malicious Executables using Data Mining
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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
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Malicious code detection: Traditional approach
- Signature based
- Requires signatures to be generated by human experts
- So, not effective against “zero day” attacks
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State of the Art in Automated Detection and
Our new ideas
OAutomated detection approaches:
0Behavioural: analyse behaviours like source, destination address, attachment
type, statistical anomaly etc.
0Content-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.
✗Our approach
✗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
-
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Feature Extraction and Hybrid Model
✗Features
✗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
Collect training samples of normal and malicious
executables.
0 Extract features
0 Train a Classifier and build a model
0 Test the model against test samples
0
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Hybrid Feature Retrieval (HFR): Training and
Testing
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Feature Extraction
Binary n-gram features
- Features are extracted from the byte codes in the form of ngrams, 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, 2345
The 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
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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
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Feature Selection
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Select Best K features
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Selection Criteria: Information Gain
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Gain of an attribute A on a collection of examples S is given by

| Sv |
Gain ( S, A)  Entropy ( S) 
Entropy ( Sv )
|
S
|
VValues ( A)
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Experiments
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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)
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Disassembly
- Pedisassem ( http://www.geocities.com/~sangcho/index.html )
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Training, Testing
- Support Vector Machine (SVM)
- C-Support Vector Classifiers with an RBF kernel
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Results
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0
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HFS = Hybrid Feature Set
BFS = Binary Feature Set
AFS = Assembly Feature Set
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Results
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0
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HFS = Hybrid Feature Set
BFS = Binary Feature Set
AFS = Assembly Feature Set
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Results
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0
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HFS = Hybrid Feature Set
BFS = Binary Feature Set
AFS = Assembly Feature Set
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Data Mining for Buffer Overflow Introduction
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Goal
- Intrusion detection.
- e.g.: worm attack, buffer overflow attack.
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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.
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Buffer Overflow
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What is 'buffer overflow'?
- A situation when a fixed sized buffer is overflown by a
larger sized input.
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How does it happen?
- example:
........
char buff[100];
gets(buff);
........
memory
Input
string
buff
Stack
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Problem with Buffer Overflow
buff
memory
buff
........
char buff[100];
gets(buff);
........
Stack
Stack
Return address
overwritten
Attacker's code
memory
buff
Stack
New return address points
to this memory location
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Handling Buffer Overflow
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Stopping buffer overflow
- Preventive approaches
- Detection approaches
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Preventive approaches
- Finding bugs in source code. Problem: can only work
when source code is available.
- Compiler extension. Same problem.
- OS/HW modification
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Detection approaches
- Capture code running symptoms. Problem: may require
long running time.
- Automatically generating signatures of buffer overflow
attacks.
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CodeBlocker (Our approach with Penn State)
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Detection Based on the Observation: Attack messages usually
contain code while normal messages contain data.
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Main Idea
- Check whether message contains code
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Problem to solve:
- Distinguishing code from data
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Formulate the problem as a classification problem (code, data)
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Collect a set of training examples, containing both instances
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Train the data with a machine learning algorithm, get the model
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Test this model against a new message
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Enhanced Penn State’s earlier model SigFree
-
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CodeBlocker Model
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Feature extraction
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0
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Features are extracted using
- N-gram analysis
- Control flow analysis
N-gram analysis
What is an n-gram?
-Sequence of n instructions
Traditional approach:
-Flow of control is ignored
2-grams are: 02, 24, 46,...,CE
Assembly program
Corresponding IFG
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Feature extraction (cont...)
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Control-flow Based N-gram analysis
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
Assembly program
Corresponding IFG
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Feature extraction (cont...)
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Control Flow analysis. Generated features
- Invalid Memory Reference (IMR)
- Undefined Register (UR)
- Invalid Jump Target (IJT)
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Checking IMR
- A memory is referenced using register addressing and
the register value is undefined
- e.g.:
mov ax, [dx + 5]
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Checking UR
- Check if the register value is set properly
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Checking IJT
- Check whether jump target does not violate instruction
boundary
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Putting it together
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Why n-gram analysis?
- Intuition: in general,
disassembled executables should have a different pattern
of instruction usage than disassembled data.
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Why control flow analysis?
- Intuition: there should be no invalid memory references or
invalid jump targets.
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Approach
- 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
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Experiments
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Dataset
- Real traces of normal messages
- Real attack messages
- Polymorphic shellcodes
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Training, Testing
- Support Vector Machine (SVM)
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Results
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CFBn: Control-Flow Based n-gram feature
CFF: Control-flow feature
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Novelty, Advantages, Limitations, Future
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0
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Novelty
- 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
- Fast testing
- Signature free operation
- Low overhead
- 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
- Find more features
- Apply dynamic analysis techniques
- Semantic analysis
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Email Worm Detection using Data Mining
Task:
given some training instances of both
“normal” and “viral” emails,
induce a hypothesis to detect “viral” emails.
We used:
Naïve Bayes
SVM
Outgoing
Emails
The Model
Test data
Feature
extraction
Machine
Learning
Classifier
Training data
Clean or Infected ?
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Assumptions
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Features are based on outgoing emails.
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Different users have different “normal” behaviour.
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Analysis should be per-user basis.
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Two groups of features
- Per email (#of attachments, HTML in body,
text/binary attachments)
- Per window (mean words in body, variable words
in subject)
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Total of 24 features identified
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Goal: Identify “normal” and “viral” emails based on
these features
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Feature sets
- 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 Features
Number of attachments; Number of words/characters in
the subject and body
- Per 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
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Data set
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Collected from UC Berkeley.
- Contains instances for both normal and viral emails.
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Six worm types:
- bagle.f, bubbleboy, mydoom.m,
- mydoom.u, netsky.d, sobig.f
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Originally Six sets of data:
- training instances: normal (400) + five worms (5x200)
- testing instances: normal (1200) + the sixth worm (200)
0 Problem: Not balanced, no cross validation reported
0 Solution: re-arrange the data and apply cross-validation
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Our Implementation and Analysis
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Implementation
- Naïve Bayes: Assume “Normal” distribution of numeric and real
data; smoothing applied
- SVM: with the parameter settings: one-class SVM with the radial basis
function using “gamma” = 0.015 and “nu” = 0.1.
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Analysis
-
NB alone performs better than other techniques
-
SVM alone also performs better if parameters are set correctly
mydoom.m and VBS.Bubbleboy data set are not sufficient (very low detection
accuracy in all classifiers)
-
The feature-based approach seems to be useful only when we have
identified the relevant features
gathered enough training data
Implement classifiers with best parameter settings
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Directions
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Malware is evolving continuously
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Example: RAMAL; Reactively Adaptive Malware
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Solution: Novel Class Detection
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Our Tool: Stream-based Novel Class Detection (SNOD)
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Applying for Malware: SNODMAL
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The Problem
0 Signature-based antivirus protection is increasingly challenged
- By polymorphic malware
- By potential self-mutating malware* to be emerged in near
future
0 Antivirus must adapt itself to the changing environment
- For example, attackers’ strategies change over time
- Therefore, characteristics of malware also change
continuously
0 Signature must be generated automatically
- To protect against polymorphic, self-mutating malware
0 New type of attacks should be detectable by the antivirus
- To guard against zero-day attacks
* Kevin W. Hamlen, Vishwath Mohan, Mohammad M. Masud, Latifur Khan, Bhavani M.
Thuraisingham.“Exploiting an antivirus interface.” Computer Standards & Interfaces 31(6), p.p. 1182-1189,
2009
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Our Approach (UTD/UIUC, Patent pending)
0 Data stream classification and novel class detection (SNOD)
- Addresses the infinite-length, concept-drift, and featureevolution problem
- Automatically detects novel classes in stream
0 We are developing SNODMAL, a malware detector using SNOD
Table 1: Differences among different malware detectors
Functionality
Signature-based
Traditional Data
mining based
SNODMAL
Automated signature generation
X


Addresses zero-day attack
X


Addresses polymorphism and
metamorphism
Addresses the evolution of
malware and benign executables
over time
Designed to detect new kind of
attack
X


X
X

X
X

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Ensemble Classification of Data Streams
0 Divide the data stream into equal sized chunks
- Train a classifier from each data chunk
- Keep the best L such classifier-ensemble
- Example: for L= 3
Note: Di may contain data points from different classes
Labeled chunk
Data
chunks
Classifiers
D1
C1
D2
C2
Ensemble C1 C42 C53
D543
C543
D654
Unlabeled
chunk
Addresses infinite
length
and concept-drift
Prediction
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Architecture of the SNODMAL Framework
Stream of benign &
malicious
executables
Unknown
executable
Feature
extraction
Temporary
training buffer
Feature extraction
and selection
Train new
model
Classify
Ensemble of L
models
Ensemble
update
Malware/Benign/Novel
Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham.
“Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams”. In
Proceedings of 2009 European Conf. on Machine Learning and Principles and Practice of
Knowledge Discovery in Databases (ECML/PKDD’09), Bled, Slovenia, 7-11 Sept, 2009, pp
79-94 (extended version appeared in IEEE Transaction on Knowledge and Data
Engineering (TKDE)).
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Usefulness of SNODMAL
0 Capable of handling massive volumes of training data
- Also handles concept-drift
0 Capable of detecting novel classes (new type of malware)
- Existing techniques may fail to detect new type of malware
- SNODMAL should be able to detect the new type as a “novel class”
- SNODMAL will then quarantine the malware and raise alarm
- The quarantined binary would be analyzed by human experts
- The classification model would be updated with the new malware
0 Therefore, reduces damage caused by zero-day attacks
0 Use of cloud computing for feature extraction
- Makes it more applicable to large volumes of data and optimizes running
time