Data Mining Approaches for Intrusion Detection

In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra- and inter- audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents. Click here for free

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related to ours is their cooperative intrusion detection system in [5]. They developed an intrusion detection system based on [4] which is not able to detection intrusions, but also can detect the type 5] Yian Huang and Wenke Lee, A Cooperative Intrusion Detection System for to higher dimensional feature space through non- linear mapping have been used for detecting intrusions in [21]. We use the benchmark KDD cup 1999 Intrusion Detec- tion data-set for our experiments [3 Random Fields are far better than the Naive Bayes for the detection of all

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detect, and although a successful multi-stage attack normally requires that each phase of the In: 2nd International Workshop on Recent Advances in Intrusion Detection. ASSP Magazine, 1986 7. Warrender, C., Forrest S., and Pearlmutter, B.: Detecting Intrusions Using System In particular, the detection rate of DOS attack is almost 100%. DOS (Denial of Service): intrusions are designed to disnrpt a host or network service, eg SYN flood; Satan 1,633 Nman TABLE II. DETECTIION RATES (%) USING NORMAL-INTRUSION DEcrsioN TREE

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Detection rate of the method is high, and it is more likely to detect un-known attacks, but mis-judgment rate is also high. False negative (FN): The amount of normal detected when it is actually attack, namely the attacks which can be detected by intrusion detection system.

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and Probing attacks, the R2L and U2R attacks don't have any intrusion frequent sequential attacks, and the data instances in the 8th subset were almost entirely neptune intrusions. learning have attempted to devise complex learners to optimize accuracy and detection rate over

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Another distributed agent-based IDS called Distributed Hybrid Agent Based Intrusion Detection and Real Service (DoS) and data theft attacks, in addition to analyzing intrusion signa- tures out alerts to the designated network ad- ministrator when network intrusions are detected SJ Stolfo, K. Mok, A Data Mining Framework for Building Intrusion Detection Models, Proceedings D. Anderson, Statistical Methods for Computer Usage Anomaly Detection Using NIDES J. Wanken, F. Charron, Detecting Anomalous and Unknown Intrusions Against Programs

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