A survey on Deep Learning based Intrusion Detection Systems on Internet of Things

Slevi, S. Tamil; Visalakshi, P.

doi:10.1109/i-smac52330.2021.9641050

Cited by 6 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, another study [ 36 ] conducted a survey on IDSs in IoT networks and the limitations of conventional systems. It explored various DL methods including Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTM Networks), and Recurrent Neural Networks (RNNs).…”

Section: Deep Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

“…For the DL-based studies, the model most cited with the highest accuracy is the Long Short-Term Memory (LSTM) Neural Network and its hybrid [ 85 , 89 ], which appeared about seven times, followed by the Conventional Neural Network (CNN) [ 87 ] and its hybrid [ 84 ], which appeared about six times, followed by the Deep Neural Network (DNN) [ 36 ]. Hence, from the literature, LSTM, CNN, and DNN models appear to be the most accurate DL models in the detection of anomalies and attacks.…”

Section: Research Summarymentioning

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%

“…The current work on detecting anomalies in IoT networks using ML techniques has several limitations and areas for improvement. These include the inaccuracy of current algorithms, the lack of consideration of adversarial attacks, the complexity and variability of IoT data, the lack of privacy and security issues, and the insufficient comprehen- For the DL-based studies, the model most cited with the highest accuracy is the Long Short-Term Memory (LSTM) Neural Network and its hybrid [85,89], which appeared about seven times, followed by the Conventional Neural Network (CNN) [87] and its hybrid [84], which appeared about six times, followed by the Deep Neural Network (DNN) [36]. Hence, from the literature, LSTM, CNN, and DNN models appear to be the most accurate DL models in the detection of anomalies and attacks.…”

Section: Research Gapsmentioning

confidence: 99%

“…However, the differing factors between ML and DL techniques lie in their architecture and complexity, with DL being more complex as it deals with Neural Networks [ 32 ]. DL uses Neural Networks to learn a hierarchical representation of the data, which enables it to learn complex patterns [ 36 ]. ML and DL techniques combined with the IoT result in efficient anomaly detection that allows abnormalities to be found and fixed quickly.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Learning—iot Network Anomaly Detectionmentioning

confidence: 99%