Toward Dynamic Topology Obfuscation for IoT Networks With Evolutionary Defense

Traditional defenses against DDoS and LFAs in IoT networks, such as static network topology obfuscation and traffic engineering, suffer from critical limitations. These include high computational overhead that is incompatible with low-power devices, rigid policies that are vulnerable to evolving attacks, and isolated modules that create exploitable gaps. Accordingly, a new problem setting for IoT network defense is introduced, i.e., dynamic open-world network defense that poses two critical tasks: 1) Open defense, aiming not only to mitigate attacks from known patterns but also to detect and counteract unknown adversarial strategies through real-time traffic analysis and adaptive obfuscation. 2) Dynamic adaptation, aiming to continuously learn new attack types (e.g., novel probing techniques or topology inference methods) without retraining the entire model while preserving robustness against previously encountered threats and avoiding catastrophic forgetting of learned defense policies. To address these problems, we propose OptiPathNet, a unified framework integrating multi-objective evolutionary optimization with dynamic topology obfuscation and real-time traffic analysis. At its core, OptiPathNet leverages two innovations. First is a GCMOEA-based method that dynamically balances security, latency and energy efficiency through Pareto-optimal trade-offs, reducing attackers’ topology inference accuracy while ensuring control packet delays and consumption. Moreover, a synergistic detection-obfuscation framework combining lightweight traffic classification with adaptive obfuscation policies, enabling protocol-compliant noise injection to disrupt both reconnaissance for traceroute-based probing and forensic analysis for timing-based attack traces. Evaluations across IoT scenarios, i.e., smart cities and industrial systems, demonstrate OptiPathNet’s superiority over the state-of-the-art methods. Reduces the structural similarity between the attacker-inferred and real topologies by 43%, while the delay overhead was reduced by 17%, and achieves optimal defense in 95% of the scenarios tested. By transforming IoT’s inherent constraints, including resource limitations and dynamic topologies, into defensive assets, OptiPathNet establishes a scalable, adaptive security paradigm for next-generation networks, bridging the gap between theoretical innovation and practical deployment in resource-constrained environments.