Comparative analysis of Machine Learning algorithms for Intrusion Detection

Vasudeva Pai, Devidas, N. D. Adesh

Research output: Contribution to journalConference articlepeer-review

7 Citations (SciVal)


In this modern era, the network related applications, programs and services are growing enormously but the network security issues also grow along with them. Keeping the network secure is a challenging and a crucial task. To maintain the secure network there must be some system which can detect and identify any malicious activity happening in network. This system is called as Intrusion Detection System. There are many traditional network security tools and techniques of preventing intrusion like firewalls, anti-virus, encryption-decryption, access control etc. But all are not effective in protecting network from increasing attacks. The network traffic can be categories into normal and intrusive traffic using Machine Learning (ML) algorithms. Here, the preliminary comparative study regarding which type of machine learning algorithm performs better in identifying the attacks namely Denial of Service, Probe, User to Root and Remote to Local. The NSL-KDD dataset is used to study features and behavior of malicious attacker using machine learning techniques. This study can be taken as reference for mechanical engineers for developing a safe automation in industrial atmosphere and automation in automobile.

Original languageEnglish
Article number012038
JournalIOP Conference Series: Materials Science and Engineering
Issue number1
Publication statusPublished - 06-01-2021
Event2020 International Conference on Futuristic Trends in Mechanical Engineering, ICOFTIME 2020 - Bengaluru, India
Duration: 24-04-202025-04-2020

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Engineering(all)


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