An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection
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Transcript of An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection
An Analysis of the 1999 DARPA/Lincoln LaboratoryEvaluation Data for Network
Anomaly Detection
Matthew V. Mahoney and Philip K. Chan
Data Mining for Computer Security Workshop at ICDM03
Melbourne, FLNov 19, 2003
www.cs.fit.edu/~pkc/dmsec03/
Outline• DARPA/Lincoln Laboratory IDS evaluation
(IDEVAL)
• Analyze IDEVAL with respect to network anomaly detection
• Propose a remedy for identified simulation artifacts
• Measure effects on anomaly detection algorithms
1999 IDEVAL
Solaris SunOS Linux NT
Router
SimulatedInternet
Inside Sniffer201 Attacks
Outside Sniffer
BSM Audit Logs, Directory and File System Dumps
Importance of 1999 IDEVAL• Comprehensive
– signature or anomaly– host or network
• Widely used (KDD cup, etc.)• Produced at great effort• No comparable benchmarks are available• Scientific investigation
– Reproducing results– Comparing methods
1999 IDEVAL ResultsTop 4 of 18 systems at 100 false alarms
System Attacks detected/in spec
Expert 1 85/169 (50%)
Expert 2 81/173 (47%)
Dmine 41/102 (40%)
Forensics 15/27 (55%)
Partially Simulated Net Traffic
• tcpdump records sniffed traffic on a testbed network
• Attacks are “real”—mostly from publicly available scripts/programs
• Normal user activities are generated based on models similar to military users
Related Work
• IDEVAL critique (McHugh, 00) mostly based on methodology of data generation and evaluation– Did not include “low-level” analysis of
background traffic
• Anomaly detection algorithms– Network based: SPADE, ADAM, LERAD– Host based: t-stide, instance-based
Problem Statement
• Does IDEVAL have simulation artifacts?
• If so, can we “fix” IDEVAL?
• Do simulation artifacts affect the evaluation of anomaly detection algorithms?
Simulation Artifacts?
• Comparing two data sets:– IDEVAL: Week 3 – FIT: 623 hours of traffic from a university
departmental server
• Look for features with significant differences
# of Unique Values & % of Traffic
Inbound client packets IDEVAL FITClient IP addresses 29 24,924
HTTP user agents 5 807
SSH client versions 1 32
TCP SYN options 1 103
TTL values 9 177
Malformed SMTP None 0.1%
TCP checksum errors None 0.02%
IP fragmentation None 0.45%
Growth Rate in Feature Values
Number ofvalues observed
Time
IDEVAL
FIT
Conditions for Simulation Artifacts
1. Are attributes easier to model in simulation (fewer values, distribution fixed over time)?
• Yes (to be shown next).
2. Do simulated attacks have idiosyncratic differences in easily modeled attributes?
• Not examined here
Exploiting Simulation Artifacts
• SAD – Simple Anomaly Detector
• Examines only one byte of each inbound TCP SYN packet (e.g. TTL field)
• Training: record which of 256 possible values occur at least once
• Testing: any value never seen in training signals an attack (maximum 1 alarm per minute)
SAD IDEVAL Results• Train on inside sniffer week 3 (no attacks)• Test on weeks 4-5 (177 in-spec attacks)• SAD is competitive with top 1999 results
Packet Byte Examined Attacks Detected
False Alarms
IP source third byte 79/177 (45%) 43
IP source fourth byte 71 16
TTL 24 4
TCP header size 15 2
Suspicious Detections
• Application-level attacks detected by low-level TCP anomalies (options, window size, header size)
• Detections by anomalous TTL (126 or 253 in hostile traffic, 127 or 254 in normal traffic)
Proposed Mitigation
1. Mix real background traffic into IDEVAL
2. Modify IDS or data so that real traffic cannot be modeled independently of IDEVAL traffic
Mixing Procedure
• Collect real traffic (preferably with similar protocols and traffic rate)
• Adjust timestamps to 1999 (IDEVAL) and interleave packets chronologically
• Map IP addresses of real local hosts to additional hosts on the LAN in IDEVAL (not necessary if higher-order bytes are not used in attributes)
• Caveats:– No internal traffic between the IDEVAL hosts and the
real hosts
IDS/Data Modifications
• Necessary to prevent independent modeling of IDEVAL– PHAD: no modifications needed– ALAD: remove destination IP as a conditional
attribute– LERAD: verify rules do not distinguish
IDEVAL from FIT– NETAD: remove IDEVAL telnet and FTP rules– SPADE: disguise FIT addresses as IDEVAL
Evaluation Procedure
• 5 network anomaly detectors on IDEVAL and mixed (IDEVAL+FIT) traffic
• Training: Week 3• Testing: Weeks 4 & 5 (177 “in-spec” attacks)• Evaluation criteria:
– Number of detections with at most 10 false alarms per day
– Percentage of “legitimate” detections (anomalies correspond to the nature of attacks)
Criteria for Legitimate Detection
• Anomalies correspond to the nature of attacks
• Source address anomaly: attack must be on a password protected service (POP3, IMAP, SSH, etc.)
• TCP/IP anomalies: attack on network or TCP/IP stack (not an application server)
• U2R and Data attacks: not legitimate
Mixed Traffic: Fewer Detections, but More are Legitimate
Detections out of 177 at 100 false alarms
0
20
40
60
80
100
120
140
PHAD ALAD LERAD NETAD SPADE
Total
Legitimate
Concluding Remarks
• Values of some IDEVAL attributes have small ranges and do not continue to grow continuously. Lack of “crud” in IDEVAL.
• Artifacts can be “masked/removed” by mixing in real traffic.
• Anomaly detection models from the mixed data achieved fewer detections, but a higher percentage of legitimate detections.
Limitations
• Traffic injection requires careful analysis and possible IDS modification to prevent independent modeling of the two sources.
• Mixed traffic becomes proprietary. Evaluations cannot be independently verified.
• Protocols have evolved since 1999.• Our results do not apply to signature detection.• Our results may not apply to the remaining
IDEVAL data (BSM, logs, file system).
Future Work
• One data set of real traffic from a university--analyze headers in publicly available data sets
• Analyzed features that can affect the evaluated algorithms--more features for other AD algorithms
Final Thoughts
• Real data– Pros: Real behavior in real environment– Cons: Can’t be released because of privacy concerns
(i.e., results can’t be reproduced or compared)
• Simulated data– Pros: Can be released as benchmarks– Cons: Simulating real behavior correctly is very
difficult
• Mixed data– A way to bridge the gap
Tough Questions fromJohn & Josh?