A Direct Collaborative Network Intrusion Detection System for IoT Networks Integration
Resumo
Integrating thousands of smart devices over the various IoT domains will require the devices to deliver services free of threats. Although intrusion detection systems (IDS) offer a multi-layer of protection to IoT networks, they commonly operate in isolation, thus restraining their application in integrated environments. In this context, collaboration among IDS emerges as an alternative to enhance intrusion detection, relying on their knowledge about faced threats. However, collaborative IDS (CIDS) generally exchange messages through centralized entities, disregarding direct communication among IDS. This work proposes a collaborative network IDS (C-NIDS) that integrates standalone NIDS for sharing information about detected and mitigated threats, improving overall intrusion detection. Evaluation results showed that C-NIDS achieved an attack detection rate of 99%, enhancing the attack mitigation by up to 50% compared to non-collaborative scenarios.Referências
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Heidari, A. and Jabraeil Jamali, M. A. (2022). Internet of things intrusion detection systems: a comprehensive review and future directions. Cluster Computing, pages 1–28.
Javadpour, A., Pinto, P., Ja’fari, F., and Zhang, W. (2023). DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments. Cluster Computing, 26(1):367–384.
Kheddar, H., Himeur, Y., and Awad, A. I. (2023). Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review. Journal of Network and Computer Applications, 220:103760.
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Nguyen, G. L., Dumba, B., Ngo, Q.-D., Le, H.-V., and Nguyen, T. N. (2022). A collaborative approach to early detection of IoT Botnet. Computers & Electrical Engineering, 97:107525.
Nguyen, T. G., Phan, T. V., Nguyen, B. T., So-In, C., Baig, Z. A., and Sanguanpong, S. (2019). Search: A collaborative and intelligent NIDS architecture for SDN-based cloud IoT networks. IEEE access, 7:107678–107694.
Pandey, B. K., Saxena, V., Barve, A., Bhagat, A. K., Devi, R., and Gupta, R. (2023). Evaluation of soft computing in intrusion detection for secure social Internet of Things based on collaborative edge computing. Soft Computing, pages 1–11.
Putra, G. D., Dedeoglu, V., Pathak, A., Kanhere, S. S., and Jurdak, R. (2021). Decentralised Trustworthy Collaborative Intrusion Detection System for IoT. In 2021 IEEE International Conference on Blockchain (Blockchain), pages 306–313. IEEE.
Quincozes, S. E., Raniery, C., Ceretta Nunes, R., Albuquerque, C., Passos, D., and Mossé, D. (2021). Counselors network for intrusion detection. International Journal of Network Management, 31(3):e2111.
Sachdeva, R. and Dev, A. (2021). Review of opportunistic network: Assessing past, present, and future. International Journal of Communication Systems, 34(11):e4860.
Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M. (2023). Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. Journal of Network and Systems Management, 31(1):23.
Spyropoulos, T., Psounis, K., and Raghavendra, C. S. (2004). Single-copy routing in intermittently connected mobile networks. In 2004 IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004., pages 235–244.
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Alkadi, O., Moustafa, N., Turnbull, B., and Choo, K.-K. R. (2020). A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet of Things Journal, 8(12):9463–9472.
Feige, U., Fiat, A., and Shamir, A. (1988). Zero-knowledge proofs of identity. Journal of cryptology, 1(2):77–94.
Goldstein, M. (2023). BoNeSi - the DDoS Botnet Simulator. [link].
Hara, K. and Shiomoto, K. (2020). Intrusion detection system using semi-supervised learning with adversarial auto-encoder. In NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, pages 1–8.
Heidari, A. and Jabraeil Jamali, M. A. (2022). Internet of things intrusion detection systems: a comprehensive review and future directions. Cluster Computing, pages 1–28.
Javadpour, A., Pinto, P., Ja’fari, F., and Zhang, W. (2023). DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments. Cluster Computing, 26(1):367–384.
Kheddar, H., Himeur, Y., and Awad, A. I. (2023). Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review. Journal of Network and Computer Applications, 220:103760.
Lilien, L., Kamal, Z., Bhuse, V., Gupta, A., et al. (2006). Opportunistic networks: The concept and research. In the NSF International Workshop on Research Challenges in Security and Privacy for Mobile and Wireless Networks (WSPWN 2006), Miami, FL, USA, pages 15–16.
Luo, K. (2023). A distributed SDN-based intrusion detection system for IoT using optimized forests. Plos one, 18(8):21.
Mehedi, S. T., Anwar, A., Rahman, Z., Ahmed, K., and Rafiqul, I. (2022). Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach. IEEE Transactions on Industrial Informatics.
Nguyen, G. L., Dumba, B., Ngo, Q.-D., Le, H.-V., and Nguyen, T. N. (2022). A collaborative approach to early detection of IoT Botnet. Computers & Electrical Engineering, 97:107525.
Nguyen, T. G., Phan, T. V., Nguyen, B. T., So-In, C., Baig, Z. A., and Sanguanpong, S. (2019). Search: A collaborative and intelligent NIDS architecture for SDN-based cloud IoT networks. IEEE access, 7:107678–107694.
Pandey, B. K., Saxena, V., Barve, A., Bhagat, A. K., Devi, R., and Gupta, R. (2023). Evaluation of soft computing in intrusion detection for secure social Internet of Things based on collaborative edge computing. Soft Computing, pages 1–11.
Putra, G. D., Dedeoglu, V., Pathak, A., Kanhere, S. S., and Jurdak, R. (2021). Decentralised Trustworthy Collaborative Intrusion Detection System for IoT. In 2021 IEEE International Conference on Blockchain (Blockchain), pages 306–313. IEEE.
Quincozes, S. E., Raniery, C., Ceretta Nunes, R., Albuquerque, C., Passos, D., and Mossé, D. (2021). Counselors network for intrusion detection. International Journal of Network Management, 31(3):e2111.
Sachdeva, R. and Dev, A. (2021). Review of opportunistic network: Assessing past, present, and future. International Journal of Communication Systems, 34(11):e4860.
Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M. (2023). Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. Journal of Network and Systems Management, 31(1):23.
Spyropoulos, T., Psounis, K., and Raghavendra, C. S. (2004). Single-copy routing in intermittently connected mobile networks. In 2004 IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004., pages 235–244.
Tanwar, S., Gupta, N., Iwendi, C., Kumar, K., and Alenezi, M. (2022). Next Generation IoT and Blockchain Integration. Journal of Sensors, 2022.
Yates, R. D., Sun, Y., Brown, D. R., Kaul, S. K., Modiano, E., and Ulukus, S. (2021). Age of information: An introduction and survey. IEEE Journal on Selected Areas in Communications, 39(5):1183–1210.
Publicado
20/05/2024
Como Citar
PEDROSO, Carlos; BATISTA, Agnaldo; BRISIO, Samuel; R., Rodrigues S.; SANTOS, Aldri.
A Direct Collaborative Network Intrusion Detection System for IoT Networks Integration. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 309-322.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2024.1354.