Denial-of-service (DoS) attacks pose a significant threat to wireless sensor networks (WSNs) and are an impediment to their mass deployment. Current research has identified trust models as a plausible defense against DoS attacks. However, most of the proposed solutions focus only on one or two specific DoS attack scenarios and provide very limited guidance to a WSN with regard to important practical aspects, such as setting up threshold configurations and weight allocation schemes. This study conducts a comparative analysis of relevant work to build a foundation for the development of robust trust models that can counter a range of different types of DoS attacks in WSNs. In particular, this study examines the required trust evidence, the methods for extracting trust evidence, and the trust evaluation techniques for developing effective trust models. This study identifies the challenges in the implementation of trust models, such as the need to determine feasible threshold limits and trust metric weightings and the need to manage the loss of trust information. In addition, this study explores link quality and node authentication as factors affecting trust evaluation and the integration of trust models with network routing protocols.

Wireless sensor networks (WSNs), including Software-Defined Wireless Sensors, are particularly vulnerable to Denial-of-Service (DoS) attacks. Trust models are widely acknowledged as an effective strategy to mitigate the threat of successful DoS attacks in WSNs. However, existing trust models commonly rely on threshold configurations that are based on the network administrator’s experience and leave the challenging task of weight allocation for various trust metrics to network users. This limits the widespread application of trust models as a WSN defence mechanism. To address that issue, this study proposes and theoretically analyses an Adaptive, Threshold-Free, and Automatically Weighted Trust Model (ATAW-TM) for SDWSNs. The model architecture is aligned with the layered centralized management architecture of SDWSNs, which makes it flexible and enhances its responsiveness. The proposed model does not require manual threshold configuration and weight allocation and allows for rapid trust system recovery. It has significant advantages compared to existing trust models and is potentially more feasible to implement on a large scale.

Wireless sensor networks (WSNs) including Software Defined Wireless Sensor are partic-ularly vulnerable to Denial-of-Service (DoS) attacks. Trust models are widely acknowl-edged as an effective strategy to mitigate the threat of a successful DoS attacks in WSNs. However, existing trust models commonly rely on threshold configurations that are based on the network administrator’s experience and leaving the challenging task of weight allocation for various trust metrics to network users. This limits the widespread application of trust models as a WSN defense mechanism. To address this issue, this study proposes and analyses theoretically an Adaptive, Threshold-Free, and Automati-cally Weighted Trust Model (ATAW-TM) for SDWSNs. The model architecture is aligned with the layered centralized management architecture of SDWSNs, which makes it flex-ible and enhances its responsiveness. The proposed model does not require manual threshold configuration and weight allocation, and allows for a rapid trust system re-covery. It has significant advantages compared to to existing trust models, and is po-tentially more feasible to implemented on a large scale.

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.

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.

Denial-of-service (DoS) attacks pose a significant threat to wireless sensor networks (WSNs) and are an impediment to their mass deployment. Current research has identified trust models as a plausible defense against DoS attacks. However, most of the proposed solutions focus only on one or two specific DoS attack scenarios and provide a very limited guidance to the WSN with regard to important practical aspects such as setting up threshold configurations and weight allocation schemes. This study conducts a comparative analysis of relevant work to build a foundation for the development of robust trust models that can counter a range of different types of DoS attacks in WSNs. In particular, this study examines the required trust evidence, the methods for extracting trust evidence, and the trust evaluation techniques for developing effective trust models. The study identifies the challenges to the implementation of trust models such as the need to determine feasible threshold limits and trust metric weightings, and the need to manage the loss of trust information. In addition, this study explores link quality and node authentication as factors affecting trust evaluation and the integration of trust models with network routing protocols.