Cyber threat ontology and adversarial machine learning attacks: analysis and prediction perturbance

Yeboah-Ofori, Abel ORCID:, Ismail, Umar Makhtar, Swidurski, Tymoteusz and Opoku-Boateng, Francisca (2021) Cyber threat ontology and adversarial machine learning attacks: analysis and prediction perturbance. In: 2021 International Conference on Computing, Computational Modelling and Applications (ICCMA), 14-16 Jul 2021, Brest, France.

Cyber Threat Ontology and Adversarial Machine Learning Attacks - IEEE - ICCMA - 38.pdf - Accepted Version

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Machine learning has been used in the cybersecurity domain to predict cyberattack trends. However, adversaries can inject malicious data into the dataset during training and testing to cause perturbance and predict false narratives. It has become challenging to analyse and predicate cyberattack correlations due to their fuzzy nature and lack of understanding of the threat landscape. Thus, it is imperative to use cyber threat ontology (CTO) concepts to extract relevant attack instances in CSC security for knowledge representation. This paper explores the challenges of CTO and adversarial machine learning (AML) attacks for threat prediction to improve cybersecurity. The novelty contributions are threefold. First, CTO concepts are considered for semantic mapping and definition of relationships for explicit knowledge of threat indicators. Secondly, AML techniques are deployed maliciously to manipulate algorithms during training and testing to predict false classifications models. Finally, we discuss the performance analysis of the classification models and how CTO provides automated means. The result shows that analysis of AML attacks and CTO concepts could be used for validating a mediated schema for specific vulnerabilities.

Item Type: Conference or Workshop Item (Paper)
ISBN: 9781665425674
Identifier: 10.1109/ICCMA53594.2021.00020
Page Range: pp. 71-77
Identifier: 10.1109/ICCMA53594.2021.00020
Additional Information: © 2021 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.
Subjects: Computing > Information security > Cyber security
Related URLs:
Depositing User: Dr Abel Yeboah-Ofori
Date Deposited: 26 Nov 2021 04:12
Last Modified: 01 Dec 2021 13:09


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