@inproceedings{247022aa08974d28bb37f2e179bc6b1f,
title = "Attack prediction using Hidden Markov Model",
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{\textquoteright}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.",
keywords = "Action spoofing, Attack family, Attack prediction, Hidden markov model, Viterbi algorithm",
author = "Shuvalaxmi Dass and Prerit Datta and Namin, {Akbar Siami}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
month = jul,
doi = "10.1109/COMPSAC51774.2021.00253",
language = "English",
series = "Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1695--1702",
editor = "Chan, {W. K.} and Bill Claycomb and Hiroki Takakura and Ji-Jiang Yang and Yuuichi Teranishi and Dave Towey and Sergio Segura and Hossain Shahriar and Sorel Reisman and Ahamed, {Sheikh Iqbal}",
booktitle = "Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",
}