Blockchain-Aided Intrusion Detection in Marine Tactical Network Using Reinforcement Learning
Paper Title: Blockchain-Aided Intrusion Detection in Marine Tactical Network Using Reinforcement Learning
Journal Name: IEEE Transactions on Network and Service Management
Abstract: Advanced intrusion detection systems are especially important in developing maritime tactical networks. This is because cyber attackers continually improve their strategies. This research proposes an innovative intrusion detection algorithm for maritime tactical networks. It benefits from adaptive neural network classification through a modified reinforcement learning of traditional access detection systems, on the other hand, that rely on fixed and pre-labeled datasets. The key innovation of this system is integrating adversarial reinforcement learning within a simulated environment. Where adversarial elements increase the classification ability by focusing on challenging cases, this approach significantly improves the identification of underrepresented attack types. Weighting accuracy is more than 80%, and the F1 score is more than 79% when validated on two datasets. The model incorporates blockchain technology for secure and immutable data storage to increase safety for future audit trails. To ensure the integrity of sensitive network traffic data, this research offers state-of-the-art solutions for effective intrusion detection in marine environments.