DESIGN OF AN IMPROVED METHOD FOR INTRUSION DETECTION USING CNN, LSTM, AND BLOCK CHAIN

The sophistication of cyber-attacks has brought the need for proactive Network Intrusion Detection Systems that will handle real-time threats and maintain data integrity and privacy. The traditional signature-based intrusion detection solutions have a number of drawbacks. These limitations consist of high false positives, non-transparency of decision-making, and vulnerability to tampering. Besides this fact, a centralized approach brings serious risks of privacy disclosure and raises scalability challenges, especially within distributed environments. This work proposes overcoming these limitations by integrating block chain technology along with state-of-the-art latest AI techniques to provide a powerful, decentralized, and explainable intrusion detection framework. This paper proposes a system integrating three key methodologies: (1) multimodal intrusion detection using CNN and LSTM networks for both spatial and temporal features of network traffic with 98.2% detection accuracy and less than 2.3% false positives. The decentralized model was trained using the adaptive federated learning with differential privacy in the following sections, which achieved 95.4% accuracy with 50% faster model convergence compared to the centralized approaches. Block chain is used to log updates of the federated model securely, thus guaranteeing tamper-proof auditability. SHAP is utilized for XAI, explaining AI-driven decisions in an understandable manner. 99.9% of decisions are explainable and will be immutably stored on the block chain for post-event forensic analysis. Such a fusing of the different methods described above will enable a highly accurate, scalable, and transparent NIDS. The new approach goes beyond mere security because early threat detection allows trust, accountability, and privacy-something comprehensive for modern decentralized networks.