Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

Article


Yeboah-Ofori, A., Islam, S., Lee, S. W., Shamszaman, Z. U., Muhammad, K., Altaf, M. and Al-Rakhami, M. S. 2021. Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security. IEEE Access. 9, pp. 94318-94337. https://doi.org/10.1109/ACCESS.2021.3087109
AuthorsYeboah-Ofori, A., Islam, S., Lee, S. W., Shamszaman, Z. U., Muhammad, K., Altaf, M. and Al-Rakhami, M. S.
Abstract

Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement.

JournalIEEE Access
Journal citation9, pp. 94318-94337
ISSN2169-3536
Year2021
PublisherIEEE
Publisher's version
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File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2021.3087109
Publication dates
Online07 Jun 2021
Publication process dates
Accepted10 May 2021
Deposited18 Aug 2021
Copyright holder© 2021 The Authors
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