TY  - EJOU
AU  - Joo, Soyoung 
AU  - Park, So-Hyun 
AU  - Shim, Hye-Yeon 
AU  - Oh, Ye-Sol 
AU  - Lee, Il-Gu 

TI  - Machine Learning-Based Detection and Selective Mitigation of Denial-of-Service Attacks in Wireless Sensor Networks
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Distributed coordinated function mechanism; jamming attack; machine learning-based attack detection; selective attack mitigation model; selective attack mitigation model; selfish attack

DO  - 10.32604/cmc.2025.058963