AI-Driven Threat Detection in IoT Environments Using Adaptive Cybersecurity Frameworks
DOI:
https://doi.org/10.64137/XXXXXXXX/IJCSEI-V1I1P101Keywords:
IoT Security, AI-Driven Cybersecurity, Adaptive Frameworks, Threat Detection, Anomaly Detection, Machine Learning, Intrusion Detection System (IDS), Behavior AnalysisAbstract
More and more devices connected to the Internet of Things (IoT) in different fields have led to a much bigger area where attacks can take place. Different from standard computer systems, IoT devices usually have limited resources, no common security standards and are set up with weak or little threat monitoring. Due to this situation, cyber attackers can easily target common weaknesses by launching different types of attacks like DDoS, spoofing, and data theft. Such systems, which work on stored and unchanging rules and defend only the borders, are not up to the task in today’s diverse and fast-paced networks. Since the threat environment keeps evolving, it is important to have a flexible and intelligent way to secure the growing number of IoT devices. The study proposes an AI-based threat detection system as a key part of a cybersecurity framework built for use in the IoT ecosystem. It makes use of both supervised and unsupervised learning methods by machine learning algorithms, which can detect questionable or suspicious activities in the network. Using contextual information, behavior analysis can tell if activities are allowed or not, and anomaly detection systems allow early threat detection regardless of whether the danger is known. Besides, the system is able to deal with threats on its own by cutting off compromised devices and reporting sensor detections to administrators, all with a fast response. The test using standard datasets and imitated real-time conditions (such as BoT-IoT) demonstrates a detection accuracy of more than 96%. It means that by making use of this technology, IoT infrastructures can boost their ability to resist emerging cybersecurity threats
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