Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Abstract

Project report on Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm using web mining Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data.

In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection.

Conclusion

Project report on Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm Using Web mining Recent studies have shown that two main components are essential to build an IDS. They are a robust classification method and an efficient feature selection algorithm. In Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm project report in web mining paper, a supervised filter-based feature selection algorithm has been proposed, namely Flexible Mutual Information Feature Selection (FMIFS). FMIFS project report in web mining is an improvement over MIFS and MMIFS. FMIFS project report in web mining suggests a modification to Battiti’s algorithm to reduce the redundancy among features.

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