Real-time Threat Monitoring: Utilizing IoT Data for Intrusion Detection in Smart Environments

The ubiquitous adoption of Internet of Things (IoT) devices has resulted in interconnected environments offering levels of convenience and efficiency. However, the complex nature and interconnectivity of these environments also pose cybersecurity challenges, such, as detecting access and monitoring potential risks. This research delves into the concept of real-time tracking of threats. Detecting intrusions in environments by leveraging the vast data streams generated by IoT devices. The paper explores the characteristics of data including its speed, variety, and scale which present both opportunities and challenges for effective Network infrastructure implementing intrusion detection systems. It also investigates the techniques and approaches used to leverage data for threat surveillance, including anomaly detection machine learning algorithms, and data integration methods. Moreover, it underscores the importance of scalable detection technologies that can adeptly address the evolving dynamics of smart environments. Through an analysis of academic studies and empirical research, we uncover prevalent trends, hurdles, and promising avenues in utilizing IoT data for intrusion detection, in smart environments. The main goal of this article is to offer suggestions and insights, for creating intrusion detection systems that are robust efficient tailored to the requirements of interconnected IoT settings.