A Systematic Review of Intrusion Detection Systems in Internet of Things Using ML and DL

Hussain, Ayaz; Sharif, Hanan; Rehman, Faisal; Kirn, Hina; Sadiq, Ashina; Khan, Muhammad Shahzad; Riaz, Amjad; Ali, Chaudhry Nouman; Chandio, Adil Hussain

doi:10.1109/icomet57998.2023.10099142

Cited by 1 publication

(1 citation statement)

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“…ML and DL techniques can use sophisticated analytical techniques to use the enormous and complex data of IoT systems cohesively to form a normal baseline for the network traffic of IoT devices [ 27 , 33 ]. This will result in improved accuracy, faster response times, cost-effectiveness, real-time detection, and more [ 31 , 34 , 35 ]. As a result, the ML and DL techniques can help detect when a system diverts from the baseline.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

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With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

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