A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Intrusion detection using sequences of system calls
Journal of Computer Security
RT-MOVICAB-IDS: Addressing real-time intrusion detection
Future Generation Computer Systems
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Traditionally, the application of data mining in intrusion detection systems (IDS) concentrates on the construction of operational IDSs. The main emphasis is on data mining steps, and other KDD (Knowledge Discovery in Databases) are largely ignored. The present study investigates the applicability of Spectral Analysis technique - singular value decomposition (SVD) as a preprocessing step to reduce the dimensionality of the data. This reduction highlights the most prominent features in the data by removing the noise. This preprocessing step not only makes the data noise-free, but also reduces the dimensionality of the data, thereby minimizing computational time. The proposed technique can be applied to other existing methods to improve their performance. We perform experiments on various data sets like DARPA'98, UNM sendmail, inetd, and login-ps data sets to show that reduction in the dimension of the data does not degrade the performance of the IDS. In fact, in case of single application monitoring like sendmail, by applying reduction techniques we get very encouraging results.