Attack prediction using Hidden Markov Model

Shuvalaxmi Dass, Prerit Datta, Akbar Siami Namin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

It is important to predict any adversarial attacks and their types to enable effective defense systems. Often it is hard to label such activities as malicious ones without adequate analytical reasoning. We propose the use of Hidden Markov Model (HMM) to predict the family of related attacks. Our proposed model is based on the observations often agglomerated in the form of log files and from the target or the victim’s perspective. We have built an HMM-based prediction model and implemented our proposed approach using Viterbi algorithm, which generates a sequence of states corresponding to stages of a particular attack. As a proof of concept and also to demonstrate the performance of the model, we have conducted a case study on predicting a family of attacks called Action Spoofing.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
EditorsW. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1695-1702
Number of pages8
ISBN (Electronic)9781665424639
DOIs
StatePublished - Jul 2021
Event45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 - Virtual, Online, Spain
Duration: Jul 12 2021Jul 16 2021

Publication series

NameProceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021

Conference

Conference45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021
Country/TerritorySpain
CityVirtual, Online
Period07/12/2107/16/21

Keywords

  • Action spoofing
  • Attack family
  • Attack prediction
  • Hidden markov model
  • Viterbi algorithm

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