A Collaborative Trust-Based Defense Architecture For Distributed Mitigation Of Ddos Attacks In Large-Scale Iot Networks Using Edge Intelligence
DOI:
https://doi.org/10.29284/sae0xp11Keywords:
IoT Security, DDoS Mitigation, Edge Intelligence, Trust Management, Collaborative Defense, Machine Learning.Abstract
The prevalence of the Internet of Things (IoT) devices have contributed to an increase in the attack surfaces, and as such, the wide popularization and proliferation of the IoT networks to standard dimensions has become highly susceptible to Distributed Denial-of-Service (DDoS) attacks. The latency, high energy overhead, and non-scalable methodologies of many traditional centralized mitigation systems are unable to process heterogeneous, high-volume traffic effectively, which makes them vulnerable to delays in detection and possible network disruption. A Collaborative Trust-Based Defense Architecture (CTDA) that supports edge intelligence is one of the solutions to these challenges of distributed DDoS attacks in IoT networks. The framework incorporates trust and reputation management as well as collaborative mechanisms of consensus, allowing the edge nodes to mitigate local anomalies and share verified threats to neighboring nodes. The edge currently uses machine learning and hybrid deep learning models to recognize anomalies in traffic patterns as quickly as possible, and trust estimation is also used to effectively isolate malicious or compromised devices, minimizing false positives. Synthetic and benchmark IoT DDoS traffic is simulated and evaluated on a variety of metrics such as detection accuracy and rate, detection latency, energy consumption, and scalability. The results indicate that the proposed framework can detect with an accuracy greater than 95 percent against a wide range of attack types and false positive rates less than 5 percent and minimize detection latency and energy consumption as opposed to centralized and edge-only strategies. Moreover, the scalability of CTDA with larger networks and the size of the attacks does not cause any major overhead of the resources since it promises to be timely and effective. These results indicate that it can be effective to leverage edge intelligence with trust-based collaborative mechanisms to offer robust, energy-efficient, and scalable DDoS support in heterogeneous IoT scenarios. The framework provides the effective remedy to the security of IoT in the critical applications and wind scale implementations.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
