Abstract
In this contemporary world of computer networks, network security plays a predominant role in enabling a secured network communication infrastructure into existence. In order to enforce a high protection levels against the existing network threats, number of software tools are currently employed. Intrusion Detection Systems (IDS) aims to detect the intruders or anomalies in the computer network architecture. Software model protects the computer networks from unauthorized users by detecting the network intruders at the right time. In this a machine learning classifier is built and trained the model with the NSL-KDD dataset. After training the model, the remaining data-set is tested to detect or classify the attacks into categories like normal or attack. The scholar has built State Preserving Extreme Learning Machine (SPELM) algorithm as machine learning classifier and compared its performance accuracy with the Deep Belief Network (DBN) model.
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The authors are grateful to acknowledge the support of NSL KDD Data set.
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Singh, K., James Mathai, K. (2020). Implement Improved Intrusion Detection System Using State Preserving Extreme Learning Machine (SPELM). In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_29
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DOI: https://doi.org/10.1007/978-3-030-24643-3_29
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