Abstract
Evolving technological paradigms direct information society’s developments like Internet of Things (IoT), pervasive technologies. These technologies are built on networks that integrate with others for meeting end user needs. These networks are also susceptible to attacks. Technological knowledge is also used by cyber attackers for developing attacks and their numbers have increased exponentially. Hence, to safeguard networks from attackers, cybersecurity experts have become a fundamental pillar in cybersecurity and especially in Intrusion Detection Systems (IDS) which have grown into becoming the fundamental tool for cybersecurity in its provision of services on the internet. Though IDSs monitor networks for doubtful activities and send alerts on encountering such items, they are confided in real-time analytics. A new model of automated feature selections for network IDS parameters that are pre-prpocessed for efficieny of classifications is presented. This paper’s proposed methodology combines multiple techniques for improving automated feature selections. The proposed technique is experimented on the KDD Cup 1999 dataset, a common source for examining IDS systems. The technique is also evaluated for efficiency in feature selection by three classifiers in terms of their test and train scores.
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Ramachandran, P., Balasubramian, R. (2022). An Automatic Correlated Recursive Wrapper-Based Feature Selector (ACRWFS) for Efficient Classification of Network Intrusion Features. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_51
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