Usenix Security 2004 Autograph Toward Automated, Distributed Worm Signature Detection Hyang-Ah Kim Brad Karp Carnegie Mellon University Intel Research & Carnegie Mellon University
Jan 03, 2016
Usenix Security 2004
AutographToward Automated, Distributed Worm Signature Detection
Hyang-Ah Kim Brad KarpCarnegie Mellon University Intel Research &
Carnegie Mellon University
Usenix Security 2004 2
Internet Worm Internet Worm
Large costs due to lost productivity Code Red: $2.6 billion, Slammer: $1 billion
Vulnerabilities still plentiful Smarter, faster, and more malicious worms easily possib
le [Staniford et al., 2002]
Internet Worm Quarantine Techniques Destination port blocking Infected source host IP blocking Content-based blocking Content-based blocking [Moore et al., 2003]
Usenix Security 2004 3
Content-based Blocking
05:45:31.912454 90.196.22.196.1716 > 209.78.235.128.80: . 0:1460(1460) ack 1 win 8760 (DF)0x0000 4500 05dc 84af 4000 6f06 5315 5ac4 16c4 [email protected] d14e eb80 06b4 0050 5e86 fe57 440b 7c3b .N.....P^..WD.|;0x0020 5010 2238 6c8f 0000 4745 5420 2f64 6566 P."8l...GET./def0x0030 6175 6c74 2e69 6461 3f58 5858 5858 5858 ault.ida?XXXXXXX0x0040 5858 5858 5858 5858 5858 5858 5858 5858 XXXXXXXXXXXXXXXX . . . . .0x00e0 5858 5858 5858 5858 5858 5858 5858 5858 XXXXXXXXXXXXXXXX0x00f0 5858 5858 5858 5858 5858 5858 5858 5858 XXXXXXXXXXXXXXXX0x0100 5858 5858 5858 5858 5858 5858 5858 5858 XXXXXXXXXXXXXXXX0x0110 5858 5858 5858 5858 5825 7539 3039 3025 XXXXXXXXX%u9090%0x0120 7536 3835 3825 7563 6264 3325 7537 3830 u6858%ucbd3%u7800x0130 3125 7539 3039 3025 7536 3835 3825 7563 1%u9090%u6858%uc0x0140 6264 3325 7537 3830 3125 7539 3039 3025 bd3%u7801%u9090%0x0150 7536 3835 3825 7563 6264 3325 7537 3830 u6858%ucbd3%u7800x0160 3125 7539 3039 3025 7539 3039 3025 7538 1%u9090%u9090%u80x0170 3139 3025 7530 3063 3325 7530 3030 3325 190%u00c3%u0003%0x0180 7538 6230 3025 7535 3331 6225 7535 3366 u8b00%u531b%u53f0x0190 6625 7530 3037 3825 7530 3030 3025 7530 f%u0078%u0000%u00x01a0 303d 6120 4854 5450 2f31 2e30 0d0a 436f 0=a.HTTP/1.0..Co
. . . . .Signature: A Payload Content String Specific To A Worm
Signature for CodeRed II
Usenix Security 2004 4
Content-based Blocking
Our networkX
Traffic Filtering
Internet
Signature for CodeRed II
Can be used by Bro, Snort, Cisco’s NBAR, ...
Usenix Security 2004 5
Signature derivation is too slow Current Signature Derivation Process
New worm outbreak Report of anomalies from people via phone/email/newsg
roup Worm trace is captured Manual analysis by security experts Signature generation
Labor-intensive, Human-mediated
Usenix Security 2004 6
Goal
Automatically generate signatures of previou
sly unknown Internet worms
as quickly as possible as accurately as possible
Usenix Security 2004 7
Our Work We focus on TCP worms that propagate
via scanning
Actually, any transport in which spoofed sources cannot communicate
successfully in which transport framing is known to monitor
Worm’s payloads share a common substring Vulnerability exploit part is not easily mutable
Usenix Security 2004 8
Outline Problem and Motivation Automated Signature Detection
Desiderata Technique Evaluation
Distributed Signature Detection Tattler Evaluation
Related Work Conclusion
Usenix Security 2004 9
Desiderata Automation: Minimal manual intervention
Signature quality: Sensitive & specific Sensitive: match all worms low false negative
rate Specific: match only worms low false positive
rate
Timeliness: Early detection
Application neutrality Broad applicability
Usenix Security 2004 10
Automated Signature Generation
Step 1: Select suspicious flows using heuristics Step 2: Generate signature using content-
prevalence analysis
Our network
Traffic Filtering
Internet Autograph Monitor
Signature
X
Usenix Security 2004 11
Heuristic: Flows from scanners are suspicious Focus on the successful flows from IPs who made unsuccessful con
nections to more than s destinations for last 24hours Suitable heuristic for TCP worm that scans network
Suspicious Flow Pool Holds reassembled, suspicious flows captured during the last time p
eriod t Triggers signature generation if there are more than flows
S1: Suspicious Flow SelectionReduce the work by filtering out vast amount of innocuous flows
Autograph (s=2)
Non-existent
Non-existentThis flow will be
selected
Usenix Security 2004 12
S1: Suspicious Flow Selection
Heuristic: Flows from scanners are suspicious Focus on the successful flows from IPs who made unsuccessful con
nections to more than s destinations for last 24hours Suitable heuristic for TCP worm that scans network
Suspicious Flow Pool Holds reassembled, suspicious flows captured during the last time p
eriod t Triggers signature generation if there are more than flows
Reduce the work by filtering out vast amount of innocuous flows
Usenix Security 2004 13
S2: Signature Generation
All instances of a worm have a common byte pattern specific to the worm
Rationales Worms propagate by duplicating themselves Worms propagate using vulnerability of a service
Use the most frequent byte sequences across suspicious flows as signatures
How to find the most frequent byte sequences?
Usenix Security 2004 14
Worm-specific Pattern Detection Use the entire payloads
Brittle to byte insertion, deletion, reordering
XXXXXXXX9025753930399025YYYYFlow 1
Flow 2 XXXXXXX9025753930399025YYYYY
Usenix Security 2004 15
Worm-specific Pattern Detection
Partition flows into non-overlapping small blocks and count the number of occurrences
Fixed-length Partition Still brittle to byte insertion, deletion, reordering
XXXXXXXX9025753930399025YYYYFlow 1
Flow 2 XXXXXXX9025753930399025YYYYY
Usenix Security 2004 16
Worm-specific Pattern Detection Content-based Payload Partitioning (COPP)
Determine boundaries of block using LBFS style Partition if Rabin fingerprint of a sliding window matches Breakmark Configurable parameters: content block size (minimum, average, ma
ximum), breakmark, sliding window Content Blocks
Breakmark = last 8bits of fingerprint (9025)
XXXXXXXX9025753930399025YYYYFlow 1
Flow 2 XXXXXXX9025753930399025YYYYY
Usenix Security 2004 17
Why Prevalence?
Worm flows dominate in the suspicious flow pool Content-blocks from worms are highly ranked
Nimda
CodeRed2
Nimda (16 different payloads)
WebDAV exploit
Innocuous, misclassified
< Prevalence Distribution in Suspicious Flow Pool>
Usenix Security 2004 18
Select Most Frequent Content Block
A B D
A B E
A C E
A D
C F
C D G
B
f0
f1
f2
f3
f4
f5
H I Jf6
I H Jf7
G I Jf8
Usenix Security 2004 19
A
A
A
E
E
A
FC
C
C
D
D
DB
B
B H
H
G
G
I
I
I
J
J
J
Select Most Frequent Content Block
D
C
E
E
A
A
A
A D
FC
C D G
B
B
B
H
H
G
I
I
I
J
J
J
f0
f1
f2
f3
f4
f5
f6
f7
f8
f0 C F
f1 C D G
f2 A B D
f3 A C E
f4 A B E
f5 A B D
f6 H I J
f7 I H J
f8 G I J
Usenix Security 2004 20
Select Most Frequent Content Block
A
B
D
A
B E
A
C
E
A
D
C
F
C
D
GB H
I J
I
H
J
GI J
f0 C F
f1 C D G
f2 A B D
f3 A C E
f4 A B E
f5 A B D
f6 H I J
f7 I H J
f8 G I Jp≥3
W≥90%Signature:
Usenix Security 2004 21
Signature: A
Select Most Frequent Content Block
A
B
D
A
B E
A
C
E
A
D
C
F
C
D
GB H
I J
I
H
J
GI J
f0 C F
f1 C D G
f2 A B D
f3 A C E
f4 A B E
f5 A B D
f6 H I J
f7 I H J
f8 G I Jp≥3
W≥90%
Usenix Security 2004 22
Select Most Frequent Content Block
B
DBA
A
A
C E
E
A
D
F
C
C
D
GB H
I J
I
H
J
GI J
p≥3
W≥90%Signature: A
f0 C F
f1 C D G
f2 A B D
f3 A C E
f4 A B E
f5 A B D
f6 H I J
f7 I H J
f8 G I J
Usenix Security 2004 23
Select Most Frequent Content Block
F
C
C D
G H
I J
I
H
J
GI J
p≥3
W≥90%Signature: A
f0 C F
f1 C D G
f2 A B D
f3 A C E
f4 A B E
f5 A B D
f6 H I J
f7 I H J
f8 G I J
I
Usenix Security 2004 24
Select Most Frequent Content Block
F
C
C DG
p≥3
W≥90%Signature: A
f0 C F
f1 C D G
f2 A B D
f3 A C E
f4 A B E
f5 A B D
f6 H I J
f7 I H J
f8 G I J
IUSignature:
Usenix Security 2004 25
Outline Problem and Motivation Automated Signature Detection
Desiderata Technique Evaluation
Distributed Signature Detection Tattler Evaluation
Related Work Conclusion
Usenix Security 2004 26
Behavior of Signature Generation
Objectives Effect of COPP parameters on signature quality
Metrics Sensitivity = # of true alarms / total # of worm
flows false negatives Efficiency = # of true alarms / # of alarms
false positives Trace
Contains 24-hour http traffic Includes 17 different types of worm payloads
Usenix Security 2004 27
Signature Quality
Larger block sizes generate more specific signatures A range of w (90-95%, workload dependent)
produces a good signature
Usenix Security 2004 28
Outline Problem and Motivation Automated Signature Detection
Desiderata Technique Evaluation
Distributed Signature Detection Tattler Evaluation
Related Work Conclusion
Usenix Security 2004 29
Problem: Slow Payload Accumulation Before signature generation,
Detect scanners (possibly infected hosts) Aggressiveness (s ) of flow selection heuristics Accumulate payloads enough for content analysis Earliness ( ) of signature generation trigger
Infected vulnerable hosts ( =5, 63 monitors)
Info Sharing
Autograph Monitor
Aggressiveness of flow selection
s = 1 s = 4
NoneLuckiest 2% 60%Average 25% --
Scanners, Signatures Average <1% 15%
Usenix Security 2004 30
Faster Signature Detection
Share the scanner information with others
Our network
Traffic FilteringInternet
Autograph Monitor
Network A
Network C
Netw
ork
B
tattler
Usenix Security 2004 31
Benefit from tattler
Objective Measure the detection speed and the signature quality
Methodology Trace generation
Background noise flows from the real 24hr trace Captured worm flows from simulation (63 monitors)
Signature generation with COPP varying suspect flow pool size Metrics
Percentage of infected hosts Number of unspecific signatures (that causes false positives)
Usenix Security 2004 32
Tradeoff: Speed vs. Quality
Decreasing s and parameters faster signature generation more false positives
x
x
= 15, s = 2,< 2% infected
Usenix Security 2004 33
Attacks Overload due to flow reassembly
Multiple instances of Autograph on separate HW (port-disjoint) Suspicious flow sampling under heavy load
Abuse Autograph for DoS: pollute suspicious flow pool Port scan and then send innocuous traffic Distributed verification of signatures at many monitors Source-address-spoofed port scan Reply with SYN/ACK on behalf of non-existent hosts/services
Usenix Security 2004 34
Related Work
EarlyBird [Singh et al. 2003] Pure content-based approach first, then address dispersion heuristic Targets a single high speed link; no flow reassembly
HoneyComb [Kreibich et al. 2003] Signature detection by Honeypot & LCS algorithm Targets host-based deployment; small number of flows
Honeyd [Provos 2003] Use distributed honeypots to gather worm payloads & infected IP ad
dresses quickly Focus on harvesting malicious traffic
DOMINO [Yegneswaran et al. 2004] Scanner IP information sharing among distributed nodes Distributed monitoring assures earlier & more accurate detection
Usenix Security 2004 35
Future Work Online evaluation with diverse traces Deployment on distributed sites Broader set of suspicious flow selection
heuristics Non-scanning worms (ex. hit-list worms,
topological worms, email worms) UDP worms
Distributed agreement for signature quality testing
Usenix Security 2004 36
Conclusion Stopping spread of novel worms requires
early generation of signatures Autograph: automated signature detection
system Suspicious flow selection→ Content prevalence
analysis COPP: robustness against payload variability Distributed monitoring: faster signature
generation Autograph finds sensitive & specific
signatures early in real network traces