AI-Powered Cyber security: Neural Networks for Threat Detection and Prevention

The threats that are associated with cyber are increasing at a very fast rate, and the traditional security measures cannot effectively address this problem. This study aims at establishing purpose of AI in enhancing privacy, threat detection, and mitigation. Applying the Neural Networks, CNNs and Autoencoder, this work examines the 20000 cybersecurity events and proves how AI can detect the patterns of attacks, categorise the threats by their severity, and trigger responses appropriately. The performance of the proposed system was calculated, where CNNs attained 97.6% precision in threat classification with Autoencoders enhancing response accuracy to 97.2% reducing the false positives and augmenting the proactive security measures. In addition, threat intelligence models that are powered by AI cut the risks of data exfiltration by 90.4% and enhanced the security systems. Understanding that this research may enable the deployment of real-time autonomous security risks is helpful for professionals in cybersecurity. Better explanations and interpretations of artificially intelligent systems must be developed, and the incorporation of AI into blockchain for decentralized threat intelligence for sustainable intelligent and adaptive solutions.

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