Visible to the public Biblio

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2022-08-02
Yeboah-Ofori, Abel, Agbodza, Christian Kwame, Opoku-Boateng, Francisca Afua, Darvishi, Iman, Sbai, Fatim.  2021.  Applied Cryptography in Network Systems Security for Cyberattack Prevention. 2021 International Conference on Cyber Security and Internet of Things (ICSIoT). :43—48.
Application of cryptography and how various encryption algorithms methods are used to encrypt and decrypt data that traverse the network is relevant in securing information flows. Implementing cryptography in a secure network environment requires the application of secret keys, public keys, and hash functions to ensure data confidentiality, integrity, authentication, and non-repudiation. However, providing secure communications to prevent interception, interruption, modification, and fabrication on network systems has been challenging. Cyberattacks are deploying various methods and techniques to break into network systems to exploit digital signatures, VPNs, and others. Thus, it has become imperative to consider applying techniques to provide secure and trustworthy communication and computing using cryptography methods. The paper explores applied cryptography concepts in information and network systems security to prevent cyberattacks and improve secure communications. The contribution of the paper is threefold: First, we consider the various cyberattacks on the different cryptography algorithms in symmetric, asymmetric, and hashing functions. Secondly, we apply the various RSA methods on a network system environment to determine how the cyberattack could intercept, interrupt, modify, and fabricate information. Finally, we discuss the secure implementations methods and recommendations to improve security controls. Our results show that we could apply cryptography methods to identify vulnerabilities in the RSA algorithm in secure computing and communications networks.
2022-06-06
Yeboah-Ofori, Abel, Ismail, Umar Mukhtar, Swidurski, Tymoteusz, Opoku-Boateng, Francisca.  2021.  Cyberattack Ontology: A Knowledge Representation for Cyber Supply Chain Security. 2021 International Conference on Computing, Computational Modelling and Applications (ICCMA). :65–70.
Cyberattacks on cyber supply chain (CSC) systems and the cascading impacts have brought many challenges and different threat levels with unpredictable consequences. The embedded networks nodes have various loopholes that could be exploited by the threat actors leading to various attacks, risks, and the threat of cascading attacks on the various systems. Key factors such as lack of common ontology vocabulary and semantic interoperability of cyberattack information, inadequate conceptualized ontology learning and hierarchical approach to representing the relationships in the CSC security domain has led to explicit knowledge representation. This paper explores cyberattack ontology learning to describe security concepts, properties and the relationships required to model security goal. Cyberattack ontology provides a semantic mapping between different organizational and vendor security goals has been inherently challenging. The contributions of this paper are threefold. First, we consider CSC security modelling such as goal, actor, attack, TTP, and requirements using semantic rules for logical representation. Secondly, we model a cyberattack ontology for semantic mapping and knowledge representation. Finally, we discuss concepts for threat intelligence and knowledge reuse. The results show that the cyberattack ontology concepts could be used to improve CSC security.
2020-08-24
Yeboah-Ofori, Abel, Islam, Shareeful, Brimicombe, Allan.  2019.  Detecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network. 2019 International Conference on Cyber Security and Internet of Things (ICSIoT). :37–42.

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.