Comparative Analysis of State-of-the-Art Attack Detection Models

Kumari, Priyanka; Mangat, Veenu; Singh, Anshul

doi:10.1109/icccnt56998.2023.10306428

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…In [ 71 ], comparative research of different ML models highlights the difficulties in identifying vulnerabilities used by cyberattacks as it addresses the problem of detecting intrusions in IoT networks. It makes use of the IoT network intrusion dataset as well as the IoT-23 dataset.…”

Section: Machine Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

“…The ranged uses of RFs over the years makes it an accurate ML model for detecting anomalies and attacks. Most of the studies share a common drawback, which is the need for more varied datasets to validate the proposed models [ 59 , 64 , 71 ]. This is followed by the drawback that the models can be computationally heavy for IoT systems [ 69 ].…”

Section: Research Summarymentioning

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: Machine Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

Section: Research Summarymentioning

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

View full text Add to dashboard Cite

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A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study

Krzysztoń,

Rojek,

Mikołajewski

2024

Applied Sciences

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The growth of the Internet of Things (IoT) and its integration with Industry 4.0 and 5.0 are generating new security challenges. One of the key elements of IoT systems is effective anomaly detection, which identifies abnormal behavior in devices or entire systems. This paper presents a comprehensive overview of existing methods for anomaly detection in IoT networks using machine learning (ML). A detailed analysis of various ML algorithms, both supervised (e.g., Random Forest, Gradient Boosting, SVM) and unsupervised (e.g., Isolation Forest, Autoencoder), was conducted. The results of tests conducted on popular datasets (IoT-23 and CICIoT-2023) were collected and analyzed in detail. The performance of the selected algorithms was evaluated using commonly used metrics (Accuracy, Precision, Recall, F1-score). The experimental results showed that the Random Forest and Autoencoder methods are highly effective in detecting anomalies. The article highlights the importance of appropriate data preprocessing to improve detection accuracy. Furthermore, the limitations of a centralized machine learning approach in the context of distributed IoT networks are discussed. The article also presents potential directions for future research in the field of anomaly detection in the IoT.

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Comparative Analysis of State-of-the-Art Attack Detection Models

Cited by 2 publications

References 14 publications

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

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

A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study

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