The Employment of Context-Awareness Intrusion Detection Systems using AI: Strengthen Cybersecurity in Smart Cities

With the development of Internet of Things communities, the complexity and scale of cyber security threats multiply swiftly in these intelligent cities and their connected digital infrastructure. As hybrid computing and cyber environment continue to develop, NGID systems become increasingly fragile because of their lack of adaptability and the ability to understand local context to accurately detect the cyber-attacks in dynamic cyber environment like cloud computing. The current work investigates the design and deployment of context-awareness intrusion detection systems (IDS) based on advanced artificial intelligence (AI) approaches to improve a security framework in smart city. Utilizing the real time environmental, network and user behavior data, the IDS models introduced here can optimize detection parameters as dynamic, to improve the anomaly recognition, false positives’ reduction and response time. The research is aiming at combining context information from multiple and diverse internet of things devices, and sensors, and communication networks, so as to enable AI algorithms to build a complete situational awareness model. Such context-aware processing enables the IDS to draw the borderline between normal operation and attacks more accurately. Machine learning techniques, like deep learning and reinforcement learning, can be used to continuously enhance the power of detection over adaptive leaning and build robustness against zero-day attacks and adaptive threats. In addition, the software architecture highlights fog computing for real-time threat detection and transmission from edge to network and it emphasizes a decentralized processing system at the edge of the network while maintaining data privacy. Experimental results, analyzed on several simulated smart city networks, clearly show that the context-aware AI-based IDS performs considerably better than traditional IDSs on detecting a variety of cyber threats like DDoS attacks, data breaches, and unauthorized access attempts. The results demonstrate the system's strength, scalability, and efficiency, which points a promising trend for securing the digital backbone of the future smart cities. This work paves the way for smarter, secure urban ecosystems by leveraging AI and context-awareness in the domain of cybersecurity.