Dergiler / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2026 / Volume: 68 Issue: 1

Enhancing IDS Through Multi-Objective Optimization: A Focus on Attack Graph under Worst-Parent Attack

Ali Deveci(HACETTEPE ÜNİVERSİTESİ)0000-0002-4990-0785
Mehmet Ali Erkan(ORTA DOĞU TEKNİK ÜNİVERSİTESİ)0009-0007-5760-1914
İhsan Tolga Medeni(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0002-0642-7908
Tunç Durmuş Medeni(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0002-2964-3320
Ali Uzkafali(Uzkafalı Hukuk Bürosu)0009-0005-0940-5885
Ahmet Özkan(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0009-0008-9338-757X
Sayfa
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Yayın Tarihi
2026-06-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This improvement signifies the effectiveness of integrating additional metrics in enhancing predictive accuracy and understanding network structure and resilience.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study focuses on the evolution of network security models for RPL based LLNs under worst-parent attack.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı:This improvement signifies the effectiveness of integrating additional metrics in enhancing predictive accuracy and understanding network structure and resilience.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Attack graphs · eccentricity · multi-objective optimization

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.

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Bu Makaleye Atıf Yap

Deveci, A., Erkan, M. A., Medeni, İ. T., Medeni, T. D., Uzkafali, A., Özkan, A. (2026). Enhancing IDS Through Multi-Objective Optimization: A Focus on Attack Graph under Worst-Parent Attack. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Volume: 68 Issue: 1. https://doi.org/10.33769/aupse.1907258

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Kaynakça (25)

  1. 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).
  2. 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).
  3. 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).
  4. Bhardwaj, S., Dave, M., Attack detection and mitigation using intelligent attack graph model for forensic in IoT networks, Telecommunication Systems, 1–21, (2024).
  5. 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).
  6. 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).
  7. ECJ, Retrieved June 3, 2024, from https://cs.gmu.edu/~eclab/projects/ecj/ (n.d.).
  8. 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).
  9. Garg, S., Mehrotra, D., Pandey, S., A study on RPL protocol with respect to DODAG formation using objective function, In Soft Computing: Theories and Applications: Proceedings of SoCTA 2020, Volume 1 (pp. 633–644), Springer, (2022).
  10. Ghanbari, Z., Navimipour, N. J., Hosseinzadeh, M., Shakeri, H., Darwesh, A., A new lightweight routing protocol for Internet of mobile things based on low power and lossy network using a fuzzy-logic method, Pervasive and Mobile Computing, 97, 101872, (2024).
  11. Hua, Y., Liu, Q., Hao, K., Jin, Y., A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts, IEEE/CAA Journal of Automatica Sinica, 8(2), 303–318, (2021).
  12. Internet Engineering Task Force (IETF), Retrieved May 31, 2024, from https://www.ietf.org/ (n.d.).
  13. Lallie, H. S., Debattista, K., Bal, J., A review of attack graph and attack tree visual syntax in cyber security, Computer Science Review, 35, 100219, (2020).
  14. Lv, Z., Chang, H., Chen, C., Li, N., Network security enhancement strategies for power distribution station based on attack graphs, In Proceedings of the Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023) (Vol. 13073, p. 1307302), SPIE, (2024).
  15. Madakam, S., Ramaswamy, R., Tripathi, S., Internet of Things (IoT): A literature review, Journal of Computer and Communications, 3(5), 164–173, (2015).
  16. Mercado, R., Bjerrum, E. J., Engkvist, O., Exploring graph traversal algorithms in graph-based molecular generation, Journal of Chemical Information and Modeling, 62(9), 2093–2100, (2021).
  17. Neumann, F., Wegener, I., Can single-objective optimization profit from multi-objective optimization?, In Multiobjective problem solving from nature: From concepts to applications (pp. 115–130), Springer, (2008).
  18. Presekal, A., Ştefanov, A., Rajkumar, V. S., Palensky, P., Attack graph model for cyber-physical power systems using hybrid deep learning, IEEE Transactions on Smart Grid, 14(5), 4007–4020, (2023).
  19. Sennan, S., Balasubramaniyam, S., Luhach, A. K., Ramasubbareddy, S., Chilamkurti, N., Nam, Y., Energy and delay aware data aggregation in routing protocol for Internet of Things, Sensors, 19(24), 5486, (2019).
  20. George Mason University, ECJ manual, Retrieved June 7, 2024, from https://cs.gmu.edu/~sean/temp/ecjmanual.pdf (n.d.).
  21. Uriot, T., Virgolin, M., Alderliesten, T., Bosman, P. A., On genetic programming representations and fitness functions for interpretable dimensionality reduction, In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 458–466), (2022).
  22. Verma, A., Verma, S. K., Pandey, A. C., Grover, J., Sharma, G., Li-MSD: A lightweight mitigation solution for DAO insider attack in RPL-based IoT, Future Generation Computer Systems, (2024).
  23. Weis, S. A., RFID (radio frequency identification): Principles and applications, System, 2(3), 1–23, (2007).
  24. Yazdandoost, E., Son, Y. J., Shafae, M., Taxonomy-driven graph-theoretic framework for manufacturing cybersecurity risk modeling and assessment, Journal of Computing and Information Science in Engineering, 24, 071003–1, (2024).
  25. Yücesan, E., Erkan, M. A., Deveci, A., & Medeni, İ. T. (2024). Bekenbey AI: Innovative solutions at the intersection of deep learning and law. Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi, 2(2), 185.