Babylonian Journal of Internet of Things

A Real-Time Intrusion Detection System for DoS/DDoS Attack Classification in IoT Networks Using KNN-Neural Network Hybrid Technique

Aisha Ibrahim Gide

Department of Computer Science, Umaru Musa Yar’adua University Katsina ,Nigeria.

https://orcid.org/0009-0003-7096-3865

Abubakar Aminu Mu’azu

Department of Computer Science, Umaru Musa Yar’adua University Katsina ,Nigeria.

https://orcid.org/0000-0002-1326-0990

DOI: https://doi.org/10.58496/BJIoT/2024/008

Keywords: IoT security, real time DoS/DDoS attacks detection, Kth Nearest Neighbor (KNN), Artificial Intelligence (AI), dense neural networks

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Abstract

As more devices are connected to the Internet through the Internet of Things (IoT), there are huge security challenges. One of the major problems is Distributed Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks floods networks with useless traffic, disrupting IoT services. There is a need for better security measures to handle this. Intrusion Detection Systems (IDS) is used to find suspicious activities, but many of them can't keep up with new types of attacks in real time. This study focuses on creating an efficient real time hybrid framework that uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks. This proposed framework aims to identify and categorize DoS/DDoS attacks in real time through the utilization of a simulation model and MQTT-IoT-IDS2020 dataset and compared with existing frameworks, our proposed framework excels in accuracy, precision, and recall.