Conference paper Open Access

Cyber Supply Chain Threat Analysis and Prediction using Machine Learning and Ontology

Abel Yeboah-Ofori; Haralambos Mouratidis; Umar Ismail; Shareeful Islam; Spyridon Papastergiou

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Abel Yeboah-Ofori</dc:creator>
  <dc:creator>Haralambos Mouratidis</dc:creator>
  <dc:creator>Umar Ismail</dc:creator>
  <dc:creator>Shareeful Islam</dc:creator>
  <dc:creator>Spyridon Papastergiou</dc:creator>
  <dc:description>Cyber Supply Chain (CSC) security requires a secure integrated network among the sub-systems of the inbound and outbound chains. Adversaries are deploying various penetration and manipulation attacks on an CSC integrated network’s node. The different levels of integrations and inherent system complexities pose potential vulnerabilities and attacks that may cascade to other parts of the supply chain system. Thus, it has become imperative to implement systematic threats analyses and predication within the CSC domain to improve the overall security posture. This paper presents a unique approach that advances the current state of the art on CSC threat analysis and prediction by combining work from three areas: Cyber Threat Intelligence (CTI ), Ontologies, and Machine Learning (ML). The outcome of our work shows that the conceptualization of cybersecurity using ontological theory provides clear mechanisms for understanding the correlation between the CSC security domain and enables the mapping of the ML prediction with 80% accuracy of potential cyberattacks and possible countermeasures.</dc:description>
  <dc:subject>Cyber Security, Ontology, Cyber Supply Chain, Machine Learning, Threat Prediction, Cyber Threat Intelligence</dc:subject>
  <dc:title>Cyber Supply Chain Threat Analysis and Prediction using Machine Learning and Ontology</dc:title>
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