faster, more effective flowgraph -based malware classification
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Faster, More Effective Flowgraph-based Malware
ClassificationSilvio Cesare silvio.cesare@gmail.com
http://www.foocodechu.comPh.D. Candidate, Deakin University
Ph.D. Candidate at Deakin University. Research
◦ Malware detection.◦ Automated vulnerability discovery (check out my
other talk in the main conference). Did a Masters by research in malware
◦ “Fast automated unpacking and classification of malware”.
◦ Presented last year at Ruxcon 2010. This current work extends last year’s work.
Who am I and where did this talk come from?
Traditional AV works well on known samples.
Doesn’t detect unknown samples.
Doesn’t detect “suspiciously similar” samples.
Uses strings as a signature or “birthmark”.
Compares birthmarks by equality.
Motivation
Birthmarks can be program structure.
More static among malware variants.
Birthmarks can be compared using “approximate similarity”.
Able to detect unknown samples that are suspiciously similar to known malware.
Vastly reduce number of required signatures.
What can be done?
The Software Similarity Problem
Program p
Program q
Birthmark
Birthmark
Similar?
MATCH!
Different
Control flow is more invariant among polymorphic and metamorphic malware.
A directed graph representing control flow.
A control flow graph for every procedure.
One call graph per program.
The Control Flow Birthmark
Graphs
movl $0x4020a0,(%esp)call 4011b8 <_puts>addl $0x1,-0x8(%ebp)
lea 0x4(%esp),%ecxand $0xfffffff0,%esppushl -0x4(%ecx)push %ebpmov %esp,%ebppush %ecxsub $0x24,%espcall 4011b0 <___main>movl $0x0,-0x8(%ebp)jmp 40115f <_main+0x2f>
add $0x24,%esppop %ecxpop %ebplea -0x4(%ecx),%espret
cmpl $0x9,-0x8(%ebp)jle 40114f <_main+0x1f>
Proc_0
Proc_2
Proc_1
Proc_4
Proc_3
Known as the “Graph Isomorphism” problem.
Identifies equivalent “structure”.
Not proven to be in NP, but no polynomial time algorithm known.
Graph Equality
The number of basic operations applied to a graph to transform it to another graph.
If you know the distance between two objects, you know the similarity.
Complexity in NP and infeasible.
Graph Edit Distance
Decompilation
L_0
L_3
L_6
L_7L_1
L_2 L_4
L_5
true
true
true
true
true
W|IEH}Rproc(){L_0: while (v1 || v2) {L_1: if (v3) {L_2: } else {L_4: }L_5: }L_7: return;}
Input is a string.
Extract all substrings of fixed size Q.
Substrings are known as q-grams.
Let’s take q-grams of all decompiled graphs.
Q-Grams
W|IEH}R
W|IE|IEHIEH}EH}R
An array <E1,...,En>
A feature vector describes the number of occurrences of each feature.
En is the number of times feature En occurs.
Let’s make the 500 most common q-grams as features.
We use feature vectors as birthmarks.
Feature Vectors
A vector is an n-dimensional point. E.g. 2d vector is <x,y> Fast.
Vector Distance
Software similarity problem extended to similarity search over a database.
Find nearest neighbours (by distance) of a query.
Or find neighbours within a distance of the query.
Nearest Neighbour Search
The Software Similarity Search
q
Query Malicious
Query Benign
d(p,q)
p
r
Malware
Query
Vector distances here are “metric”.
It has the mathematical properties of a metric.
This means you can do a nearest neighbour search without brute forcing the entire database!
Metric Trees
System is 100,000 lines of code of C++.
The modules for this work < 3000 lines of code.
System translates x86 into an intermediate language (IL).
Performs analysis on architecture independent IL.
Unpacks malware using an application level emulator.
Implementation
Database of 10,000 malware.
Scanned 1,601 benign binaries.
10 false positives. Less than 1%.
Using additional refinement algorithm, reduced to 7 false positives.
Very small binaries have small signatures and cause weak matching.
Evaluation – False Positives
Calculated similarity between Roron malware variants.
Compared results to Ruxcon 2010 work.
In tables, highlighted cells indicates a positive match.
The more matches the more effective it is.
Evaluation - Effectiveness
Malware Variant Detectionao b d e g k m q a
ao0.4
40.2
80.2
70.2
80.5
50.4
40.4
40.4
7
b0.4
40.2
70.2
70.2
70.5
11.0
01.0
00.5
8
d0.2
80.2
70.4
80.5
60.2
70.2
70.2
70.2
7
e0.2
70.2
70.4
80.5
90.2
70.2
70.2
70.2
7
g0.2
80.2
70.5
60.5
90.2
70.2
70.2
70.2
7
k0.5
50.5
10.2
70.2
70.2
70.5
10.5
10.7
5
m0.4
41.0
00.2
70.2
70.2
70.5
11.0
00.5
8
q0.4
41.0
00.2
70.2
70.2
70.5
11.0
00.5
8
a0.4
70.5
80.2
70.2
70.2
70.7
50.5
80.5
8
ao b d e g k m q aao 0.70 0.28 0.28 0.27 0.75 0.70 0.70 0.75b 0.74 0.31 0.34 0.33 0.82 1.00 1.00 0.87d 0.28 0.29 0.50 0.74 0.29 0.29 0.29 0.29e 0.31 0.34 0.50 0.64 0.32 0.34 0.34 0.33g 0.27 0.33 0.74 0.64 0.29 0.33 0.33 0.30k 0.75 0.82 0.29 0.30 0.29 0.82 0.82 0.96m 0.74 1.00 0.31 0.34 0.33 0.82 1.00 0.87q 0.74 1.00 0.31 0.34 0.33 0.82 1.00 0.87a 0.75 0.87 0.30 0.31 0.30 0.96 0.87 0.87
ao b d e g k m q a
ao 0.8
60.5
30.6
40.5
90.8
60.8
60.8
60.8
6
b0.8
8 0.6
60.7
60.7
10.9
71.0
01.0
00.9
7
d0.6
50.7
2 0.8
80.9
30.7
30.7
20.7
20.7
3
e0.7
20.8
00.8
7 0.9
30.8
00.8
00.8
00.8
0
g0.6
90.7
70.9
30.9
3 0.7
70.7
70.7
70.7
7
k0.8
80.9
70.6
70.7
70.7
2 0.9
70.9
70.9
9
m0.8
81.0
00.6
60.7
60.7
10.9
7 1.0
00.9
7
q0.8
81.0
00.6
60.7
60.7
10.9
71.0
0 0.9
7
a0.8
70.9
70.6
70.7
70.7
20.9
90.9
70.9
7
Exact Matching (Ruxcon 2010)
Heuristic Approximate Matching (Ruxcon 2010)
Q-Grams
Faster than Ruxcon 2010. Median benign processing time is 0.06s. Median malware processing time is 0.84s. Slowest result may be memory thrashing.
Evaluation - Efficiency
% Samples
Benign Time(s)
Malware Time(s)
10 0.02 0.1620 0.02 0.2830 0.03 0.3040 0.03 0.3650 0.06 0.8460 0.09 0.9470 0.13 0.9780 0.25 1.0390 0.56 1.31
100 8.06 585.16
Improved effectiveness and efficiency compared to Ruxcon 2010.
Runs in real-time in expected case. Large functional code base and
years of development time. Happy to talk to vendors.
Conclusion
Full academic paper at IEEE Trustcom.
Research page http://www.foocodechu.com
Book on “Software similarity and classification” available in 2012.
Wiki on software similarity and classification http://www.foocodechu.com/wiki
Further Information
top related