Survey On Intelligent Anomaly Detection Techniques In IOT Security
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Abstract
The rapid development of various advanced technologies, including the Internet of Things (IoT), coupled with users’ heavy reliance on technology in various aspects of their daily lives, has led to an increase in the number of devices connected to the Internet. As a result of this rapid growth, the amount of data generated will increase significantly, as the Internet of Things covers many areas, from industrial and healthcare sectors to smart cities and smart homes. However, many challenges, attacks, vulnerabilities, and various anomalies related to the security of IoT devices arise, negatively impacting individuals and organizations. Several anomaly detection techniques have emerged, including machine learning and deep learning, which in turn detect anomalies. This enhances the security, integrity, reliability, and effectiveness of IoT systems. This paper provides a comprehensive survey of peer-reviewed articles from 2018 up to the present that focus on machine learning and deep learning in anomaly detection and attacks on various layers of the Internet of Things architecture. The survey results provide potential insights and recommendations for future research endeavors.
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