As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.

The Internet of Things (IoT) is extensively applied across various industrial domains, such as smart homes, factories, and intelligent transportation, becoming integral to daily life. Establishing robust policies for managing and governing IoT devices is imperative. Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols. Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations, which are impractical for lightweight devices. To address this, our study introduces the privacy-preserving software-defined range proof (SDRP) model, which achieves secure authentication with low complexity. SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range. Since authentication is performed using a digital ID sequence generated from indirect personal data, it can avoid the disclosure of actual individual attributes. Experimental results demonstrate that SDRP significantly improves security efficiency, increasing it by an average of 93.02% compared to conventional methods. It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.