PID1233274.pdf (172.05 kB)

Contemporary sequential network attacks prediction using hidden Markov model

Download (172.05 kB)
conference contribution
posted on 12.07.2019, 12:26 by Timothy Chadza, Kostas Kyriakopoulos, Sangarapillai Lambotharan
Intrusion prediction is a key task for forecasting network intrusions. Intrusion detection systems have been primarily deployed as a first line of defence in a network, however; they often suffer from practical testing and evaluation due to unavailability of rich datasets. This paper evaluates the detection accuracy of determining all states (AS), the current state (CS), and the prediction of next state (NS) of an observation sequence, using the two conventional Hidden Markov Model (HMM) training algorithms, namely, Baum Welch (BW) and Viterbi Training (VT). Both BW and VT were initialised using uniform, random and count-based parameters and the experiment evaluation was conducted on the CSE-CICIDS2018 dataset. Results show that the BW and VT countbased initialisation techniques perform better than uniform and random initialisation when detecting AS and CS. In contrast, for NS prediction, uniform and random initialisation techniques perform better than BW and VT count-based approaches.

Funding

This work has been supported by the Gulf Science, Innovation and Knowledge Economy Programme of the UK Government under UK-Gulf Institutional Link grant IL 279339985.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

17th International Conference on Privacy, Security, and Trust (PST 2019)

Citation

CHADZA, T.A., KYRIAKOPOULOS, K.G. and LAMBOTHARAN, S., 2019. Contemporary sequential network attacks prediction using hidden Markov model. Presented at the 17th International Conference on Privacy, Security, and Trust (PST 2019), Fredericton, NB, Canada, 26-28th August.

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

21/06/2019

Publication date

2020-01-06

Copyright date

2019

ISBN

9781728132655

ISSN

2643-4202

Language

en

Location

Fredericton, NB, Canada

Event dates

26th August 2019 - 28th August 2019

Exports