Design of IoT Network using Deep Learning-based Model for Anomaly Detection

Varalakshmi, Sudha; Premnath, S. P.; Yogalakshmi, V; Vijayalakshmi, P.; Kavitha, V.; Vimalarani, G.

doi:10.1109/i-smac52330.2021.9640700

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…With regards to IDSs, the study in [ 86 ] proposed an IDS for detecting anomalies in IoT networks using DL models. The proposed system uses pre-processed network traffic data to train a deep Convolutional Neural Network (CNN) to distinguish between regular and abnormal traffic.…”

Section: Deep Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

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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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Section: Deep Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

“…Hence, from the literature, LSTM, CNN, and DNN models appear to be the most accurate DL models in the detection of anomalies and attacks. The most common drawbacks for DL-based models appears to be the need for more varied datasets [ 36 ] and larger datasets [ 86 ], and the computational complexity of the models [ 88 , 104 ].…”

Section: Research Summarymentioning

confidence: 99%