A comprehensive analysis on different machine learning and data mining techniques for anomaly intrusion detection

• Autores: V. Sandeep, K. Saravanan
• Localización: Sustainable development in engineering and technology, 2022, ISBN 978-84-124943-4-1, págs. 373-390
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • In this modern era, Network security draws a major attention in all areas across theworld. Over the past decade, due to the high amount of internet usage and a quickdevelopment of internet, the weakness in network security have become major issuesin every growing concern. Therefore, to detect unauthorized usage and unexpectedattacks over the network security, Intrusion detection system (IDS) is used.There has been a vast amount of study done by many researchers in Intrusion DetectionSystems in the last few years. Hence, this paper focuses on framing a surveyon various IDS techniques that have been made use of over several years, which hasnot been mentioned previously. This paper does a short assessment on the variousfunctions and detection strategies present in the anomaly intrusion detection system.It also includes a numerical comparison of several classification algorithms, thedatasets used for processing and considering feature selection as an essential stepfor detection before classifying. This main objective here is to carry out an entirestudy of the importance and description of anomaly intrusion of detection, varioustypes of anomaly intrusion of detection techniques, work done previously by differentpeople, their outcomes and comparison, and the future work proposed by them.