An Adaptive Trust-Based Approach to the Protection of Software-Defined Wireless Sensor Networks Against Multiple Types of DoS Attacks

Abstract

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 mitigation 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 tuned attack-specific detection thresholds and metric weights, which severely undermine their effectiveness. This study integrates the adaptive trust model ATAW-TM into the SDN-WISE protocol stack; the ATAW-TM model was adjusted to the resource-constrained sensor node environment by embedding three lightweight adaptations that void the invocation of the exponential and logarithmic software library functions: an inverse-quadratic kernel of Gaussian-like cooperation probability, a quadratic form of second-order Rényi (replacing Shannon entropy) for entropy weight, and a softsign for the aging factor. To evaluate the integrated model, we developed a comprehensive evaluation framework encompassing 14 distinct DoS attack types and the data tampering attack against SDWSNs. The integrated model was evaluated through extensive Cooja-based simulations. The experimental results indicate that the proposed security solution effectively addresses the majority of the DoS attack range and the data-tampering attack through attack detection, abnormality identification, and mitigation. However, for grey hole and sinkhole attacks, the attacker’s trust value stabilizes at a level lower than that of legitimate nodes but above the detection threshold; second, the detection precision of flooding-type attacks is relatively low and may result in victim nodes being wrongly classified as malicious. These security enhancements are achieved with only negligible performance overhead, introducing less than 5.3% additional hop delay and 8.1% additional forwarding delay compared with the standard SDN-WISE. 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.