An Optimized Intrusion Detection System Framework for Internet of Things Networks: Integrating Arctic Puffin Optimization and RNN

Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems

Mohammad Arefi ¹ , Parisa Rahmani ^{2 *} , Zoleikha Jahanbakhsh Naghadeh ³ , Mahdieh Rahmani ⁴

1 - Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran
2 - Department of Computer Engineering, Par.C., Islamic Azad University, Tehran, Iran
3 - Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran
4 - Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Received: 2025-01-16 Accepted : 2025-02-27 Published : 2025-03-21

Keywords: IoT, Machine Learning Algorithm, Network Security, Metaheuristic Algorithms, Intrusion Detection System ,

Abstract :

Over the past few years, significant research has been conducted on the Internet of Things (IoT), with a major challenge being network security and penetration. Security solutions require careful planning and vigilance to safeguard system security and privacy. In this paper, we propose a new hybrid Intrusion Detection System (IDS) based on machine learning and metaheuristic algorithms called IDS-RNNAPO, which has 3 stages: (1) Pre- Processing, (2) Feature Selection, and (3) Attack Detection. In the pre-processing stage, including Cleaning, Visualization, Feature Engineering, and Vectorization. Intrusion Detection Systems (IDS) form a transitional security component of networks that monitor malicious activities within networks. In the feature selection stage, Arctic puffin Optimization (APO) is used, In the attack detection stage, a modified Random Neural Network (RNN) is used, the proposed method is evaluated using the DS2OS dataset. The results have shown that The proposed approach in these experiments through a multiple learning model resulted in an improvement in accuracy to 99.62%.

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