Comparison study of machine learning classifiers to detect anomalies

Nisha P. Shetty, Jayashree Shetty, Rohil Narula, Kushagra Tandona

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this era of Internet ensuring the confidentiality, authentication and integrity of any resource exchanged over the net is the imperative. Presence of intrusion prevention techniques like strong password, firewalls etc. are not sufficient to monitor such voluminous network traffic as they can be breached easily. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database.Thus, the need for real-time detection of aberrations is observed. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database. Machine learning classifiers are implemented here to learn how the values of various fields like source bytes, destination bytes etc. in a network packet decides if the packet is compromised or not . Finally the accuracy of their detection is compared to choose the best suited classifier for this purpose. The outcome thus produced may be useful to offer real time detection while exchanging sensitive information such as credit card details.

Original languageEnglish
Pages (from-to)5445-5452
Number of pages8
JournalInternational Journal of Electrical and Computer Engineering
Volume10
Issue number5
DOIs
Publication statusPublished - 10-2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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