Abstract
Computer security is defined as the defense of computing systems against external threats in order to protect resource confidentiality, integrity, and availability. When an intrusion occurs, both network resources and the victim server are put at risk. When an intrusion occurs in a computer system or network, the Intrusion Detection System (IDS) tracks it and notifies the system administrator, allowing the appropriate action to be taken. People’s faith in the Internet has declined as the frequency of cyber-attacks has increased. A security attack known as Denial of Service (DoS) is quite successful (DoS). When an intrusion detection system (IDS) detects external attacks as well as system abuse or internal attacks, it sends a signal to a central monitoring station. In terms of functioning, an intrusion detection system is similar to a burglar alarm. This article provides a machine learning based intrusion detection system. NSL KDD data set is used as input for experimental work. ANN, SVM and ID3 algorithms are used in analytical investigation.
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Dilip, R., Samanvita, N., Pramodhini, R., Vidhya, S.G., Telkar, B.S. (2022). Performance Analysis of Machine Learning Algorithms in Intrusion Detection and Classification. In: Balas, V.E., Sinha, G.R., Agarwal, B., Sharma, T.K., Dadheech, P., Mahrishi, M. (eds) Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT. ICETCE 2022. Communications in Computer and Information Science, vol 1591. Springer, Cham. https://doi.org/10.1007/978-3-031-07012-9_25
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