Accurate Anomaly Detection using various Machine Learning methods for IoT devices in Indoor Environment

Vidya, M.; Sakthidharan, G. R.

doi:10.1109/i-smac52330.2021.9640962

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Considering the Wi-Fi network domain, the study in [ 51 ] focused on indoor positioning challenges, signal attenuation, and the need for precise anomaly detection. It assessed the effectiveness of different ML models for anomaly detection, suggested a method based on ensemble learning for better accuracy, and provided an experimental analysis of the proposed approach.…”

Section: Machine Learning—iot Network Anomaly Detectionmentioning

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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Section: Machine Learning—iot Network Anomaly Detectionmentioning

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

show abstract

“…In this situation, the IDS comes into play to picture for protecting user information from any type of intrusion. Different techniques have been used for intrusion development, like Neural networks, SVM (Support Vector Machines), and GA (Genetic algorithms) [11][12] and other methods [13][14][15][16][17][18]. For specific types of attacks, these techniques have a good rate of detection, unable to identify other types of attacks.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Framework of Artificial immune system based on Network Intrusion Detection System for Network Security Sustainability

Kodati,

Sreekanth,

Sarma

et al. 2023

E3S Web Conf.

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The popularity and rapid growth of the internet have reemphasized the importance of intrusion detection systems (IDS) significance in the network security. IDS decreases hacking, data theft risk, privacy intrusion, and others. To save the system from external and internal intruders, the primary approaches of IDS are used. Many techniques[13], like genetic algorithms, artificial neural networks, and artificial immune systems, have been applied to IDS. This paper describes an Ensemble Framework of Artificial Immune System (AIS) based on Network Intrusion Detection System. Without placing a significant additional load on networks and monitoring systems, the large volume of data is analysed by a network-based Intrusion Detection System (NIDS). For determining the connection type, data from KDD Cup 99 competitions is utilized. To differentiate between attacks and valid connections, IDS can be utilized. Optimized feature selection is used to speed up the time-consuming rough set. The results obtained from the IDS system indicate that it can effectively identify the attacking connections with a high success rate.

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Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

Gebremariam,

Panda,

Indu

2023

Alexandria Engineering Journal

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Accurate Anomaly Detection using various Machine Learning methods for IoT devices in Indoor Environment

Cited by 4 publications

References 9 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

Ensemble Framework of Artificial immune system based on Network Intrusion Detection System for Network Security Sustainability

Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

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