IJARSAI International Journal of Advanced Research Scientific Analysis & Inferences
Volume 1, Issue 1, January 2025
Exploring the Role of Explainable AI in Attack Modeling for IoT Networks
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IJARSAI Int. J. Adv. Res. Sci. Anal. Infer.
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Article Dates: Received: 02 Aug 2024| Revised Submission: 08 Sep 2024 | Accepted: 03 Dec 2024| Available Online: 15 Mar 2025
Abstract
Keywords: Machine Learning, Deep Learning, Explainable AI, IoT Network Security, Vulnerability Analysis, Attack Modelling
How to Cite:Blessy Thomas. (2025). Exploring the role of explainable AI in attack modeling for IoT networks. IJARSAI International Journal of Advanced Research Scientific Analysis & Inferences, 1(1), 3–21. https://doi.org/10.5281/zenodo.16191480
Blessy Thomas
Connected Systems and Intelligence Lab
School of Computer Science and Engineering
Digital University Kerala, Technopark Phase IV
Thiruvananthapuram, Kerala, India
Email: blessy.res21@duk.ac.in
Article Dates: Received: 02 Aug 2024| Revised Submission: 08 Sep 2024 | Accepted: 03 Dec 2024| Available Online: 15 Mar 2025
Abstract
In an era marked by the pervasive presence of Internet of Things (IoT) networks, it is critical to ensure the strong security of these autonomous systems. The integrity, confidentiality, and availability of IoT networks are threatened by new, complex security concerns that have arisen as a result of the rapid expansion of these networks. Vulnerabilities in the IoT are often exploited by the attacker to gain unauthorized access endangering privacy and security of the network. Identifying potential attack paths in the connected ecosystem is essential for implementing strong security measures. With attack modeling, security experts can evaluate the potential attack paths in the network. We propose a new framework for attack modeling in IoT networks by incorporating Explainable AI(XAI). The XAI algorithm LIME ( Local Interpretable Model Agnostic Explanations) has been included in the proposed framework to enhance the explanation and comprehension of the critical attack paths predicted. A use case is discussed and various graphs with explanations are provided to evaluate the performance of the attack model against the evolving dynamics of the IoT network. XAI provides a crucial layer of defense for protecting IoT networks from cyber hazards by offering interpretable insights into strong security decisions.
Keywords: Machine Learning, Deep Learning, Explainable AI, IoT Network Security, Vulnerability Analysis, Attack Modelling
How to Cite:Blessy Thomas. (2025). Exploring the role of explainable AI in attack modeling for IoT networks. IJARSAI International Journal of Advanced Research Scientific Analysis & Inferences, 1(1), 3–21. https://doi.org/10.5281/zenodo.16191480
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Creative Commons Attribution 4.0 International
The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited.
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