Showing papers by "Richard P. Lippmann published in 2000"

The 1999 DARPA off-line intrusion detection evaluation

Abstract: Eight sites participated in the second Defense Advanced Research Projects Agency (DARPA) off-line intrusion detection evaluation in 1999. A test bed generated live background traffic similar to that on a government site containing hundreds of users on thousands of hosts. More than 200 instances of 58 attack types were launched against victim UNIX and Windows NT hosts in three weeks of training data and two weeks of test data. False-alarm rates were low (less than 10 per day). The best detection was provided by network-based systems for old probe and old denial-of-service (DoS) attacks and by host-based systems for Solaris user-to-root (U2R) attacks. The best overall performance would have been provided by a combined system that used both host- and network-based intrusion detection. Detection accuracy was poor for previously unseen, new, stealthy and Windows NT attacks. Ten of the 58 attack types were completely missed by all systems. Systems missed attacks because signatures for old attacks did not generalize to new attacks, auditing was not available on all hosts, and protocols and TCP services were not analyzed at all or to the depth required. Promising capabilities were demonstrated by host-based systems, anomaly detection systems and a system that performs forensic analysis on file system data.

Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation

Abstract: An intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100's of users on 1000's of hosts. More than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data. Six research groups participated in a blind evaluation and results were analyzed for probe, denial-of-service (DoS) remote-to-local (R2L), and user to root (U2R) attacks. The best systems detected old attacks included in the training data, at moderate detection rates ranging from 63% to 93% at a false alarm rate of 10 false alarms per day. Detection rates were much worse for new and novel R2L and DoS attacks included only in the test data. The best systems failed to detect roughly half these new attacks which included damaging access to root-level privileges by remote users. These results suggest that further research should focus on developing techniques to find new attacks instead of extending existing rule-based approaches.

Improving intrusion detection performance using keyword selection and neural networks

Abstract: The most common computer intrusion detection systems detect signatures of known attacks by searching for attack-specific keywords in network traffic. Many of these systems suffer from high false-alarm rates (often hundreds of false alarms per day) and poor detection of new attacks. Poor performance can be improved using a combination of discriminative training and generic keywords. Generic keywords are selected to detect attack preparations, the actual break-in, and actions after the break-in. Discriminative training weights keyword counts to discriminate between the few attack sessions where keywords are known to occur and the many normal sessions where keywords may occur in other contexts. This approach was used to improve the baseline keyword intrusion detection system used to detect user-to-root attacks in the 1998 DARPA Intrusion Detection Evaluation. It reduced the false-alarm rate required to obtain 80% correct detections by two orders of magnitude to roughly one false alarm per day. The improved keyword system detects new as well as old attacks in this database and has roughly the same computation requirements as the original baseline system. Both generic keywords and discriminant training were required to obtain this large performance improvement.

Analysis and Results of the 1999 DARPA Off-Line Intrusion Detection Evaluation

Abstract: Eight sites participated in the second DARPA off-line intrusion detection evaluation in 1999. Three weeks of training and two weeks of test data were generated on a test bed that emulates a small government site. More than 200 instances of 58 attack types were launched against victim UNIX and Windows NT hosts. False alarm rates were low (less than 10 per day). Best detection was provided by network-based systems for old probe and old denial-of-service (DoS) attacks and by host-based systems for Solaris user-to-root (U2R) attacks. Best overall performance would have been provided by a combined system that used both host- and network-based intrusion detection. Detection accuracy was poor for previously unseen new, stealthy, and Windows NT attacks. Ten of the 58 attack types were completely missed by all systems. Systems missed attacks because protocols and TCP services were not analyzed at all or to the depth required, because signatures for old attacks did not generalize to new attacks, and because auditing was not available on all hosts.