Real-Time Threat Detection and Response Using AI for Securing Critical Infrastructure
Shantanu Sudhir Gujar · 2024
The main aim of this paper is to discuss the conceptualisation and application of an Artificial Intelligence-based Real-Time Threat Detection and Response system (RTT-DS) for CIS. The system currently applied the best of machine learning as well as deep learning for perfect identification of an anomalous point as well as threat identification. The operation of the system is enhanced with the aspects of both cloud and edge computing, thereby handling big data and offering low latency. In simulation, the proposed system’s accuracy of the proposed system was found to be 95% in threat classification and also in response time; high level threats was taken within a maximum of 2 seconds. The real-time monitors of the system allow one to promptly address the risks before they aggravate. The effectiveness of the proposed system in dynamic and large-scale environments was confirmed through studying of the performance indicators such as precision, recall and resources used. Based on the experimental findings, the authors argue that the proposed system based on artificial intelligence can be effective for addressing intricate security threats in the critical infrastructure areas, and is versatile.