Intrusion Response Automation Through Machine Learning Algorithms

The proposed method focuses on automating intrusion response using machine learning algorithms to enhance cybersecurity. It encompasses three vital algorithms: Anomaly Detection using Deep Autoencoders, Intrusion Classification using Random Forest, and Adaptive Response using Reinforcement Learning. The Anomaly Detection step employs deep autoencoders, an artificial neural network, to identify deviations from normal patterns in data, crucial for detecting anomalies. The Intrusion Classification step uses Random Forest to efficiently classify these anomalies into specific intrusion types, aiding in targeted responses. The final step, Adaptive Response, employs reinforcement learning to dynamically adapt response actions based on identified intrusion types, maximizing the system's defense strategies.