OPTIONS attack detection in WSN using Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network

Abstract

The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus Optimization Algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security.