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Machine Learning (ML) methods to identify data breaches

  • H. L. Gururaj*
  • , M. R. Pooja
  • , Pavan S.P. Kumar
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this digitized world, everything is changing from offline to online. Data plays a vital role in this digital network. The theft or loss of USB devices, computers, or mobile devices by an unauthorized person who gains access to your mobile or laptop devices, email account, or network is generally termed as a data breach. Securing data from theft and breaches is a challenging issue. It is very hard to identify data breaches in complex networks. Adding extra intelligence using machine learning (ML) approaches will be efficient in identifying such attackers. In this chapter, various ML techniques to identify data breaches such as malware attack, man in the middle (MIM), spearphishing attack, eavesdropping attack, password attack, cross-site scripting attack will be depicted with suitable case studies.

Original languageEnglish
Title of host publicationMethods, Implementation, and Application of Cyber Security Intelligence and Analytics
PublisherIGI Global
Pages52-64
Number of pages13
ISBN (Electronic)9781668439937
ISBN (Print)9781668439913
DOIs
Publication statusPublished - 17-06-2022

All Science Journal Classification (ASJC) codes

  • General Computer Science

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