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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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Application of data mining to network intrusion detection: classifier selection model
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We evaluate RIPPER through JRip, an implementation of RIPPER in Weka with the parameters: folds idea about which algorithm can be implemented in a real-time network intrusion detection system as we expected, Table 1 shows that no single algorithm could detect all attack

Network intrusion detection system using reduced dimensionality
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Simulation work for proposed Network Intrusion Detection System is done using WEKA[7].The Waikato Environment for Knowledge Analysis (WEKA) came about through the perceived need for a unified workbench that would allow researchers easy access to state-of the art

Improving effectiveness of intrusion detection by correlation feature selection
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feature selection algorithm and the classifier, which is used for evaluation of the detection accuracy on classifiers shall be derived using specific features for each classifier in order to detect (identify)

Feature selection in intrusion detection system over mobile ad-hoc network
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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

Applications of hidden markov models to detecting multi-stage network attacks
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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

Data mining-based intrusion detectors
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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.

Intrusion detection with evolutionary learning classifier systems
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We detect little sign of overfitting in XCS but somewhat more in UCS effective rules from the training log data which can then be used to identify intrusions in future raw network traffic collected at US Air Force Research Labs during the 1998 intrusion detection evaluation program

A detailed analysis of the KDD CUP 99 data set
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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

Network intrusion detection through adaptive sub-eigenspace modeling in multiagent systems
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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

Combining multiple techniques for intrusion detection
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In addition, it is able to detect learned attacks (encountered in training data) and relying on Moreover, Weka implementation of BPNN algorithm with 500 epochs (iterations for each data fold), .2 learning The combined decisions of intrusion detection models relying on Bayesian

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