Қўқон университети хабарномаси Volume 11 (2024) · pp. 112-115
IOT NETWORK INTRUSION DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES
Meliboev, Azizjon
Abstract
The proliferation of Internet of Things (IoT) devices has transformed various industries by providing smart and automated solutions. However, the extensive connectivity and diverse nature of IoT devices have also introduced significant security challenges, particularly in terms of network intrusion. This paper explores the development and implementation of an Intrusion Detection System (IDS) for IoT networks using Machine learning techniques. The proposed IDS aims to detect and mitigate various cyber threats by analyzing network traffic and identifying anomalous patterns indicative of intrusions. This research contributes to the field of IoT security by providing a robust and scalable intrusion detection solution that leverages the power of machine learning.
IoT, IDS, Machine learning, Data science, data analysis, review, platform
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