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pro vyhledávání: '"Kostas, Kahraman"'
Machine learning is increasingly used for intrusion detection in IoT networks. This paper explores the effectiveness of using individual packet features (IPF), which are attributes extracted from a single network packet, such as timing, size, and sou
Externí odkaz:
http://arxiv.org/abs/2406.07578
Previous research on behaviour-based attack detection on networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited, and often not demonstrated. In this paper we present an approach for modelling
Externí odkaz:
http://arxiv.org/abs/2401.01343
In the era of rapid IoT device proliferation, recognizing, diagnosing, and securing these devices are crucial tasks. The IoTDevID method (IEEE Internet of Things 2022) proposes a machine learning approach for device identification using network packe
Externí odkaz:
http://arxiv.org/abs/2307.08679
Autor:
Kostas, Kahraman
While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market. In this environment, IoT device identification methods provide a pr
Externí odkaz:
http://arxiv.org/abs/2304.13905
Autor:
Kostas, Kahraman
While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market. In this environment, IoT device identification methods provide a pr
Externí odkaz:
http://arxiv.org/abs/2304.13894
In recent years WiFi became the primary source of information to locate a person or device indoor. Collecting RSSI values as reference measurements with known positions, known as WiFi fingerprinting, is commonly used in various positioning methods an
Externí odkaz:
http://arxiv.org/abs/2208.07190
Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine learning-based method, IoTDevID, that recognizes devices th
Externí odkaz:
http://arxiv.org/abs/2102.08866
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Publikováno v:
Journal of Clinical & Experimental Neuropsychology; Apr2024, Vol. 46 Issue 3, p272-301, 30p
Akademický článek
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