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.
Highlights
Aramco power station attack halted its operation due to aCyber Supply Chain (CSC) security is critical for reliable massive cyberattack [1]
This paper aims to improve the cybersecurity of CSC by focusing on integrating Cyber Threat Intelligence (CTI) and Machine Learning (ML) techniques
Our prediction reveals a total accuracy of 85% for the true positive rate (TPR) and False positive rate (FPR)
Summary
Cyber Supply Chain (CSC) security is critical for reliable massive cyberattack [1]. CSC systems by its inherently is complex and vulnerabilities within CSC system environment can cascade from a source node to a number of target nodes of the overall intelligence properties for the overall cyber security improvement. It is essential to predict the cyberattack trends so that the organization can take the cyber physical system (CPS). This paper aims to improve the cybersecurity of CSC by focusing on integrating Cyber Threat Intelligence (CTI) and Machine Learning (ML) techniques. The rest of the paper is organised as follows: Section 2 presents an overview of related works including CSC security, cyber threat intelligence and Machine Learning for CSC.
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