A Deep Learning-Based Approach for Real-Time Network Intrusion Detection and Prevention

our paper presents deep learning-based real-time network intrusion detection and prevention methods for IDS and IPS, rule-based systems that cannot deal with incognito or zero-day attacks as the pattern is unrecognizable. Deep learning uses neural networks to learn patterns and find the similarities between normal data or traffic and malicious ones, making it an ideal choice for intrusion detection and prevention. It combines two components to the main proposal: A deep learning model for intrusion detection and a real-time prevention mechanism. A large number of labeled examples of normal and abnormal network traffic are needed to train the deep learning model so that it learns to correctly predict whether network behavior is normal or abnormal. The model operates in real-time, constantly monitoring your incoming traffic and providing insights into any malicious activity for further verification. This module works with the deep learning model and responds in real-time as soon as any suspicious activity has been detected. It then utilizes real-time firewall rules and network reconfiguration to proactively block malicious traffic and prevent the compromise from spreading. The proposed technique has been tested on a real-world dataset and proved its efficacy with a high detection rate. The fact that it is also realtime makes it the perfect candidate to be leveraged in dynamic and fast-paced network environments.