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Prevention of cyber attacks on the critical network resources has become animportant issue as the traditional Intrusion Detection Systems (IDSs) are nolonger effective due to the high volume of network traffic and the deceptivepatterns of network usage employed by the attackers. Lack of sufficient amountof labeled observations for the training of IDSs makes the semi-supervised IDSsa preferred choice. We propose a semi-supervised IDS by extending a dataanalysis technique known as Logical Analysis of Data, or LAD in short, whichwas proposed as a supervised learning approach. LAD uses partially definedBoolean functions (pdBf) and their extensions to find the positive and thenegative patterns from the past observations for classification of futureobservations. We extend the LAD to make it semi-supervised to design an IDS.The proposed SSIDS consists of two phases: offline and online. The offlinephase builds the classifier by identifying the behavior patterns of normal andabnormal network usage. Later, these patterns are transformed into rules forclassification and the rules are used during the online phase for the detectionof abnormal network behaviors. The performance of the proposed SSIDS is farbetter than the existing semi-supervised IDSs and comparable with thesupervised IDSs as evident from the experimental results.

SSIDS: Semi-Supervised Intrusion Detection System by Extending the Logical Analysis of Data