2025
doi: 10.12720/jait.16.8.1083-1099
Enhancing Intrusion Detection in IoT Systems through Simulated Attack Scenarios
Marwa Neily
,
Farah Jemili* ,
Ouajdi Korbaa
- University of Sousse, ISITCom, MARS Research Laboratory, LR17ES05,4011, Hammam Sousse, Tunisia
- Manuscript receivedJanuary 21, 2025
- revisedMarch 20, 2025
- acceptedApril 15, 2025
- publishedAugust 8, 2025
Abstract
The Internet of Things (IoT) landscape is fraught with vulnerabilities, making it a prime target for various types of attacks. While existing literature has extensively explored IoT attacks through studies and simulations, this paper introduces a fresh perspective by proposing a new methodology for testing attacks in IoT environments. Focusing on six prominent attack vectors, we conduct comprehensive tests using both the Cooja and OMNET++ simulators. Our research delves into the underlying factors driving these attacks, analyzing data based on the attackers’ chosen target addresses. Through our novel approach, we aim to deepen the understanding of IoT vulnerabilities and provide insights into the behavior of attackers, ultimately paving the way for more effective defense mechanisms in IoT ecosystems. Our experiments demonstrate high detection accuracy, with Random Forest achieving up to 99.86% accuracy in Cooja and 98.66% in OMNET++. Despite these results, the study acknowledges potential limitations in real-world generalization due to the simulated nature of the dataset.
Keywords
- attacks
- Internet of Things (IoT)
- Intrusion Detection Systems (IDS)
- Cooja
- OMNET++
- security
- simulations
How to Cite
Marwa Neily, Farah Jemili, and Ouajdi Korbaa, "Enhancing Intrusion Detection in IoT Systems through Simulated Attack Scenarios," Journal of Advances in Information Technology, vol. 16, no. 8, pp. 1083-1099, 2025. https://doi.org/10.12720/jait.16.8.1083-1099
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
