Rootkit Detection using Deep Learning: A Comprehensive Survey
S Suresh Kumar, S Stephen, M Suhainul Rumysia · 2024
The “Rootkit Detection using Deep Learning with Reinforcement Learning” task addresses the important venture of detecting and mitigating rootkits, insidious types of malwares that frequently pass neglected within Personal Computer (PC) structures. Rootkits pose large threats to machine protection and their detection calls for innovative approaches. This task leverages the strength of deep s, especially reinforcement getting to know, to expand a dynamic and adaptable anomaly detection machine. The undertaking encompasses the collection of existing datasets regarded rootkits, ensuring information availability for training the reinforcement mastering agent. The dataset is created by logging actions, identifying subtleties, and classifying them based on malicious characteristics. It is meticulously annotated to describe rootkit types and behavior. The dataset is used for real-time monitoring and scanning by a reinforcement learning agent to distinguish between rootkit presence and normal system behavior. Reinforcement gaining knowledge of, a subset of deep mastering, allows the agent to analyze effective strategies to identify and reply to anomalies using a Generative Adversial Network (GAN), contributing to robust detection capabilities. The venture is structured into modules, inclusive of data collection, preprocessing, reinforcement studying, continuous model, and moral considerations. Evaluation metrics like precision, keep in mind, and F1-rating gauge the agent’s detection accuracy. This research-pushed project targets to decorate the security of laptop structures by supplying a flexible and proactive method for rootkit detection.