Abstract
Intrusion detection system (IDS) is playing crucial role to provide the security in the fastest world by protecting the internet applications such as healthcare applications, government secret information, secret banking data and intellectual properties of various scientists. In this paper, we propose new intrusion detection system for improving the detection rate. The proposed system is the combination of feature selection, outlier detection and classification. First, a newly proposed feature selection algorithm called intelligent flawless feature selection algorithm (IFLFSA) is used for selecting optimal number of features which are most useful for identifying the attacks. Second, the proposed entropy-based weighted outlier detection (EWOD) technique is used to identify the outliers from the data set. Third, the existing classification algorithm called intelligent layered approach for effective classification is used. The experiments have been conducted for evaluating the proposed model using the KDD data set. The proposed system achieved better detection accuracy in terms of high detection accuracy and low error rate.
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Kovarasan, R.K., Rajkumar, M. (2019). An Effective Intrusion Detection System Using Flawless Feature Selection, Outlier Detection and Classification. In: Pati, B., Panigrahi, C., Misra, S., Pujari, A., Bakshi, S. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-13-1708-8_19
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DOI: https://doi.org/10.1007/978-981-13-1708-8_19
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