AI-Based Malware Detection and Classification for Wireless Networks

Abstract: This study highlights the shortcomings of conventional detection strategies like signature-based and anomaly-based approaches by examining the application of artificial intelligence (AI) techniques for malware detection and classification in wireless networks. Using machine learning and deep learning algorithms, the study shows notable gains in false positive rates, detection accuracy, and adaptation to changing threats. The acquisition of data from many sources, the extraction of features from malware samples, and the thorough training and assessment of models are important techniques. The results demonstrate how successful AI-based systems are in detecting and reducing malware threats, even in the face of issues like dataset variety and interpretability. The potential of AI technology to improve cybersecurity measures in wireless environments is highlighted by this research.