Contemporary sequential network attacks prediction using hidden Markov model
conference contributionposted on 12.07.2019 by Timothy Chadza, Kostas Kyriakopoulos, Sangarapillai Lambotharan
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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.
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.
- Mechanical, Electrical and Manufacturing Engineering