Joint opportunistic approaches for building resilient information sharing toward heterogeneous networks integration
Resumo
Massive networks demand resilient critical infrastructures as a means to achieve a safe operation against threats to their continuous and correct functioning. As a novelty, this thesis argues that the application of multi-opportunistic approaches can aid more robust information sharing on networks in the face of anomalies, and timeand space-sensitive data, thus enhancing overall resilience. To validate the hypothesis, we applied two opportunistic approaches jointly in a fixed environment on a collaborative network intrusion detection system (C-NIDS) for IoT networks against distributed denial-of-service (DDoS) attacks. Next, we employed two approaches on a location service in a network of unmanned aerial vehicles (UAVs) against false data injection (FDI) attacks. Thus, we evinced that a number of opportunistic approaches when employed jointly has great potential to provide resilient services across various network critical structures.
Referências
Batista, A. and Santos, A. (2026). Analyzing The FlySafe System Face to Malicious UAVs Playing Black Hole Attacks. In Simpósio Brasileiro de Cibersegurança, SBSeg 2026, Porto Alegre, RS, Brazil. SBC.
Batista, A. S. and Santos, A. L. (2024). A Survey on Resilience in Information Sharing on Networks: Taxonomy and Applied Techniques. ACM Computing Surveys, 56(12):1–36.
Batista, A. S. and Santos, A. L. (2025). Resilient UAVs location sharing service based on information freshness and opportunistic deliveries. Pervasive and Mobile Computing, 111:102066.
Firdaus, M. and Rhee, K.-H. (2021). On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks. Applied Sciences, 11(1):414.
Li, Z. and Shen, H. (2009). A mobility and congestion resilient data management system for distributed mobile networks. In Proceedings of the 6th International Conference on Mobile Adhoc and Sensor Systems, MOBHOC, pages 60–69, New Jersey, USA. IEEE.
Liu, Y., Dong, Y., Wang, H., Jiang, H., and Xu, Q. (2022). Distributed Fog Computing and Federated-Learning-Enabled Secure Aggregation for IoT Devices. IEEE Internet of Things Journal, 9(21):21025–21037.
Mohsan, S. A. H., Khan, M. A., Alsharif, M. H., Uthansakul, P., and Solyman, A. A. A. (2022). Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions. Sensors, 22(14).
Pang, Z., Yao, Y., Li, Q., Zhang, X., and Zhang, J. (2022). Electronic Health Records Sharing Model Based on Blockchain With Checkable State PBFT Consensus Algorithm. IEEE Access, 10:87803–87815.
Pedroso, C., Batista, A., Brisio, S., Rodrigues, G., and Santos, A. (2024). A Direct Collaborative Network Intrusion Detection System for IoT Networks Integration. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, SBRC 2024, pages 309–322, Porto Alegre, RS, Brazil. SBC.
Shen, H., Li, Z., and Chen, K. (2013). A Scalable and Mobility-Resilient Data Search System for Large-Scale Mobile Wireless Networks. IEEE Transactions on Parallel and Distributed Systems, 25(5):1124–1134.
Song, Z., Zhou, T., Zhong, W., Chen, D., Liu, L., and Yang, X. (2023). Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid. IEEE Access, 11:2169–3536.
Spyropoulos, T., Rais, R. N. B., Turletti, T., Obraczka, K., and Vasilakos, A. (2010). Routing for disruption tolerant networks: Taxonomy and design. Wireless networks, 16(8):2349–2370.
