AI-powered strategies for advanced malware detection and prevention
Syed Immamul Ansarullah, Abdul Wahid Wali, Irshad Rasheed, Peer Zada Rayees · 2024
As the digital world continue to progress, the threat of malware grows large, hence demanding innovative approaches to strengthen information systems. Preventing and detecting malware attacks using artificial intelligence (AI) involves leveraging advanced technologies to enhance security measures, detect anomalies, and respond to potential threats in real time. This research discusses the key aspects of AI in preventing and detecting malware attacks, showcasing its ability to significantly enhance and improve traditional security measures. This research applies machine learning algorithms, both supervised and unsupervised, for recognizing known malware signatures and identifying anomalous patterns indicative of novel threats. In this research, we explore deep learning methodologies, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for their efficacy in feature extraction and behavioral analysis. Furthermore, the integration of real-time monitoring, network traffic analysis, and endpoint security solutions driven by AI is examined as a proactive approach to threat mitigation. We emphasize the adaptability of AI models through integration with threat intelligence feeds, ensuring timely responses to emerging malware threats. The amalgamation of these AI-driven strategies forms a dynamic defense framework, complementing traditional cybersecurity measures and fortifying information systems against the ever-evolving landscape of malware attacks.