Malware attack predictive analytics in a cyber supply chain context using machine learning

Yeboah-Ofori, Abel ORCID: https://orcid.org/0000-0001-8055-9274 and Boachie, Charles (2019) Malware attack predictive analytics in a cyber supply chain context using machine learning. In: 2019 International Conference on Cyber Security and Internet of Things (ICSIoT), 29-31 May 2019, Accra, Ghana.

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Abstract

Due to the invincibility nature of cyber attacks on the cyber supply chain (CSC), and the cascading effects ofmalware infections, we use machine learning to predictattacks. As organizations have become more reliant on CSC systems for business continuity, so are the increase invulnerabilities and the threat landscapes. Some traditionalapproach to detecting and defending malware attack haslargely been antimalware or antivirus software such as spam filters, firewall, and IDS/IPS. These tools largelysucceed, however, as threat actors get more intelligent, theyare able to circumvent and affect nodes on systems which then propagates. In our previous work, we characterizedthreat actor activities, including presumed intent and historically observed behaviour, for the purpose of ascertaining the current threats that could be exploited. Inthis paper, we use ML techniques to learn dataset and predict which CSC nodes have detection or no detection. The purpose is to predict which modes are venerable to cyberattacks and for predicting future trends. Todemonstrate the applicability of our approach, we used adataset from Microsoft Malware Prediction website. Further, an ensemble is used to link Logistic Regression, and Decision Tree and SVM algorithms in Majority Votingand run on the training data and then use 10-fold crossvalidation to test the parameter estimation, accurate results and predictions. The results show that ML algorithms in Decision Trees methods can be used in cyber supply chainpredict analytics to detect and predict future cyber attacktrends.

Item Type: Conference or Workshop Item (Paper)
ISBN: 9781728174174
Identifier: 10.1109/ICSIoT47925.2019.00019
Page Range: pp. 66-73
Additional Information: © 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.
Uncontrolled Keywords: Machine Learning, Cyber Security, Cyber Supply Chain, Predictive Analytics, Cyberattack
Subjects: Computing > Information security > Cyber security
Related URLs:
Depositing User: Dr Abel Yeboah-Ofori
Date Deposited: 24 Jun 2021 14:55
Last Modified: 29 Jun 2021 15:16
URI: http://repository.uwl.ac.uk/id/eprint/8028

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