A presentation of my minor research project at Politecnico di Milano, Dec 2007. It uses a finite queue model to describe IDS performances when subject to a performance attack and shows a practical example with a backtracking algorithmic complexity attack.

1. Performance Attacks
on Intrusion Detection Systems
Davide Eynard
eynard@elet.polimi.it
Dipartimento di Elettronica e Informazione
Politecnico di Milano
2007/12/06
Performance Attacks on Intrusion Detection Systems

2. Intro
 Intrusion Detection Systems
 Open problems and vulnerabilities
 The queueing model
 Algorithmic complexity attacks
 Tests and evaluations
 Conclusions
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3. Intrusion Detection Systems
 As the Internet grows, the number of
• vulnerabilities
• attacks
• attackers!
 increases: what kind of protections can we use for
our systems?
 IDS are used to detect unauthorized access
attempts to computers or local networks
 They work as alarms in apartments
• they do not prevent attackers to break in the system...
• but they allow administrators to know when an attack is
taking place
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4. Intrusion Detection Systems
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5. IDS Performance
 Measures:
• coverage
• probability of false alarms
• probability of detection
• resistance to attacks directed at the IDS
• ability to handle high bandwidth traffic
• ability to correlate events
• ability to detect new attacks
• ability to identify an attack
• ...
 Traffic generation:
• background
• attacks
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6. IDS Vulnerabilities
 Insertion
• an IDS accepts packets that an end system rejects
 Evasion
• an IDS rejects packets accepted by the end system
 Denial of Service
• compromises the availability of the IDS, either
consuming its resources or targeting at bugs in
software
• fail-closed vs fail-open systems
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7. Model
K=L+1
L
λ λa X
...
λr
S = 1/μ
Queue size: K Service time: S
Incoming packet rate: λ pkt/sec Throughput: X
λa accepted
λr rejected
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8. Model
Markov Chain:
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9. Model behavior
Drop probability as a function
of λ/μ, plotted with four
different queue sizes
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10. Model behavior
P(K)
Packet frequency
Service time
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11. Model behavior
Drop probability as a function of S, seen for different values of λ
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12. What if I have a 56Kbps?
 Gigabit Ethernet: ~ 1.6Mpps (frame size: 78B)
 100MB Ethernet: ~ 148Kpps (frame size: 84B)
 10MB Ethernet: ~ 14.8Kpps
 2MB ADSL: ~ 3Kpps
 56Kbps modem: ~ 80 pps
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13. Algorithmic complexity attacks
 S. Crosby, D. Wallach: “Denial of Service via
Algorithmic Complexity Attacks”, 2003
 They exploit algorithmic deficiencies in many
common applications' data structures
• ie. both hash tables and binary trees can degenerate to
linked list with carefully chosen input
 One particular case: backtracking algorithmic
complexity attacks
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14. Backtracking attacks
 A vulnerable rule:
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15. Backtracking attacks
 every triple (x, y, z) contains:
• x: the match name
• y: where the parsing started
• z: where the next parsing will start
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16. Backtracking attacks
 IDS behavior (left: normal, right: under attack)
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17. Tests and evaluations
 Backtracking attacks seem a good way to create
high service times
 The plan:
• install Snort on a test machine
• generate background traffic on the network
• attack Snort with backtracking attacks
• see/measure its behavior
 Test machine
• 2.4GHz Athlon, 1GB RAM, Linux kernel 2.6.22.14
• Snort 2.4.3 and 2.8.0
 Attacker machine
• 1.86GHz Pentium M, 1GB RAM, Linux kernel 2.6.22.14
• blabla tool to replay the DARPA 1999 dataset
• a perl script to generate attack packets
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18. Test attack
alert tcp $EXTERNAL_NET any ­> $SMTP_SERVERS 25 (
msg:quot;SMTP spoofed MIME­Type auto­execution attemptquot;;
flow:to_server,established;
content:quot;Content­Type|3A|quot;; nocase;content:quot;audio/quot;; nocase;
pcre:quot;/Content­Typex3As+audio/(x­wav|mpeg|x­midi)/iquot;;
content:quot;filename=quot;; distance:0; nocase;
pcre:quot;/filename=[x22x27]?.{1,221}.(vbs|exe|scr|pif|bat)/iquot;;
reference:bugtraq,2524; reference:cve,2001­0154;
classtype:attempted­admin; sid:3682; rev:2;)
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19. Test attack
alert tcp $EXTERNAL_NET any ­> $SMTP_SERVERS 25 (
msg:quot;SMTP spoofed MIME­Type auto­execution attemptquot;;
flow:to_server,established;
content:quot;Content­Type|3A|quot;; nocase;content:quot;audio/quot;; nocase;
pcre:quot;/Content­Typex3As+audio/(x­wav|mpeg|x­midi)/iquot;;
content:quot;filename=quot;; distance:0; nocase;
pcre:quot;/filename=[x22x27]?.{1,221}.(vbs|exe|scr|pif|bat)/iquot;;
reference:bugtraq,2524; reference:cve,2001­0154;
classtype:attempted­admin; sid:3682; rev:2;)
Match example:
Content­Type: audio/x­wav;
filename=”virus.scr”
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20. Test attack
alert tcp $EXTERNAL_NET any ­> $SMTP_SERVERS 25 (
msg:quot;SMTP spoofed MIME­Type auto­execution attemptquot;;
flow:to_server,established;
content:quot;Content­Type|3A|quot;; nocase;content:quot;audio/quot;; nocase;
pcre:quot;/Content­Typex3As+audio/(x­wav|mpeg|x­midi)/iquot;;
content:quot;filename=quot;; distance:0; nocase;
pcre:quot;/filename=[x22x27]?.{1,221}.(vbs|exe|scr|pif|bat)/iquot;;
reference:bugtraq,2524; reference:cve,2001­0154;
classtype:attempted­admin; sid:3682; rev:2;)
Match example:
Content­Type: audio/x­wav;
filename=”virus.scr”
Attack example:
...
Content­Type: audio/x­wav; filename=filename=filename=filename=
Content­Type: audio/x­wav; filename=filename=filename=filename=
...
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21. Results
 Snort 2.8.0 is not affected by the attacks
 Snort 2.4.3 experiences serious slowdowns
• normal service time: ~100μsec
• normal attack: 500~1000μsec
• backtracking attack: 1500000μsec
 With such service time, just few packets are able
to make the queue fill up and the IDS drop
packets => other attacks are undetected!
 Results comparable with paper: real behavior
seems worse than in the model
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22. Conclusions
 The incoming packet rate and the service time
are interchangeable
 The model is useful not just to plan attacks
• it explains why backtracking attacks work
• it allows to study an IDS as a black box
 Limits
• test suffers the classical problems of IDS evaluations
• bursts not taken into account
 Possible future work
• take bursts into account
• multiclass model
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23. That's All, Folks
Thank you!
Questions are welcome
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