An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things

Arefi, Mohamad; Rahmani, Parisa; Shokrzadeh, Hamid

doi:10.22059/jcss.2025.395014.1146

An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things

Articles in Press

Document Type : Original article

Authors

¹ Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran.

² Department of Computer Engineering, Par.C., Islamic Azad University, Tehran, Iran.

10.22059/jcss.2025.395014.1146

Abstract

Background: Network security and penetration pose a significant challenge in the extensive IoT research of recent years. System security and user privacy demand security solutions that are carefully planned and diligently maintained.
Aims: This paper introduces a novel three-stage hybrid IDS, IoT-APOSCA, leveraging machine learning and meta-heuristics for attack detection; stages include pre-processing, feature selection, and attack detection. The pre-processing steps are: cleaning, visualization, feature engineering, and vectorization.
Methodology: Networks use Intrusion Detection Systems (IDSs) to monitor and detect malicious activities as a key security feature. The Arctic Puffin Optimization (APO) and Sine-Cosine Algorithm (SCA) are used in the feature selection stage, while a changed Random Neural Network (RNN) is employed in the attack detection stage.
Results: The proposed technique is assessed using the DS2OS dataset, and the outcomes show that the approach, integrating multiple learning models, led to an accuracy enhancement to 99.66%. Also, the values Recall and False Alarm Rate obtained are equal to 0.9926 and 0.003, respectively.
Conclusion: Intrusion detection system efficacy is directly tied to the quality of its classification method. Enhanced neural network performance is achievable through adjustments to parameters, such as network weights.

Keywords

Main Subjects

computer science & technology

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