An Adaptive Trust-Based Approach to Multi-DoS Attack Detection in Software-Defined Wireless Sensor Networks

Software-defined wireless sensor networks (SDWSNs) are deployed in mission-critical applications such as environmental monitoring, smart cities, and healthcare. However, existing protocol architectures such as SDN-WISE lack built-in mechanisms for the detection or mitigate of Denial-of-Service (DoS). Existing trust-based security solutions are rarely fully integrated into the protocol stack; nearly all of them rely on manually configured thresholds which severely undermine their effectiveness. This study integrates the Adaptive, Threshold-Free, and Automatically Weighted Trust Model (ATAW-TM) into the SDN-WISE protocol stack; the ATAW-TM model was adjusted to the resource-constraint sensor node environment by embedding three lightweight adaptations: an inverse-quadratic approximation of Gaussian-like cooperation probability that eliminates exponential function calls and mathematical libraries dependences, a quadratic approximation of second-order Rényi (replacing Shannon entropy) for entropy weight calculation that avoids logarithmic operations, and a lightweight sigmoid approximation for the aging factor that preserves monotonicity and smoothness without the use of exponential functions. To evaluate the integrated model, we developed a comprehensive evaluation framework encompassing 14 distinct DoS attack types and one data tampering attack against SDWSNs. The integrated model was evaluated through extensive Cooja-based simulations. The experimental results indicated that compared to the standard SDN-WISE, the proposed security solution successfully detects a comprehensive range of DoS attacks and one type of data tampering attack, with a negligible performance overhead (less than 5.3% additional hop delay, and 8.1% additional forwarding delay). Furthermore, simulation results show that the proposed method can adapt to variations in network traffic conditions without requiring manual threshold tuning. Overall, the proposed lightweight trust-based attack recognition model offers a practically achievable security solution for resource-constrained SDWSNs, without sacrificing protocol efficiency.