Enhancing IDS Through Multi-Objective Optimization: A Focus on Attack Graph under Worst-Parent Attack
Abstract
This study focuses on the evolution of network security models for RPL based LLNs under worst-parent attack. The multi-objective optimization approach integrates additional metrics: i) The amount of change in the maximum path length within the graph (∆L) and ii) the amount of change in the number of nodes connected to the attacker node(∆W) alongside the conventional accuracy criterion. The analysis of these metrics plays a crucial role in the development of Intrusion Detection Systems (IDS), particularly through the examination of attack graphs. Attack graphs are small, distinct network substructures used to model potential attacks on the network. Analyzing these graphs is considered essential for identifying security vulnerabilities in the network and determining the spread and impact of attacks. The findings of the study demonstrate a progressive improvement in model performance across tasks, starting from a single-objective task and advancing towards multi-objective tasks. This improvement signifies the effectiveness of integrating additional metrics in enhancing predictive accuracy and understanding network structure and resilience.
Keywords
Ethical Statement
References
- Almazrouei, O. S. M. B. H., Magalingam, P., Hasan, M. K., Shanmugam, M., A review on attack graph analysis for IoT vulnerability assessment: Challenges, open issues, and future directions, IEEE Access, (2023).
- Almutairi, H., Zhang, N., A survey on routing solutions for low-power and lossy networks: Toward a reliable path-finding approach, Network, 4(1), 1–32, (2024).
- Bang, A. O., Rao, U. P., Kaliyar, P., Conti, M., Assessment of routing attacks and mitigation techniques with RPL control messages: A survey, ACM Computing Surveys, 55(2), 1–36, (2022).
- Bhardwaj, S., Dave, M., Attack detection and mitigation using intelligent attack graph model for forensic in IoT networks, Telecommunication Systems, 1–21, (2024).
- Cai, X. Q., Zhang, P., Zhao, L., Bian, J., Sugiyama, M., Llorens, A., Distributional Pareto-optimal multi-objective reinforcement learning, Advances in Neural Information Processing Systems, 36, (2024).
- Deveci, A., Multi-objective approach for intrusion detection in RPL-based Internet of Things (Master’s thesis), Hacettepe University, Graduate School of Natural and Applied Sciences, Department of Computer Engineering, Ankara, Türkiye, (2023).
- ECJ, Retrieved June 3, 2024, from https://cs.gmu.edu/~eclab/projects/ecj/ (n.d.).
- Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., Wahler, A., Fensel, D., Introduction: What is a knowledge graph?, In Knowledge graphs: Methodology, tools and selected use cases (pp. 1–10), (2020).
Details
Primary Language
English
Subjects
Cyberphysical Systems and Internet of Things
Journal Section
Research Article
Authors
Ali Deveci
*
0000-0002-4990-0785
Türkiye
Mehmet Ali Erkan
0009-0007-5760-1914
Türkiye
Ali Uzkafali
0009-0005-0940-5885
Türkiye
Ahmet Özkan
0009-0008-9338-757X
Türkiye
Publication Date
June 30, 2026
Submission Date
March 11, 2026
Acceptance Date
June 17, 2026
Published in Issue
Year 2026 Volume: 68 Number: 1
