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An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things | ||
| Journal of Cyberspace Studies | ||
| دوره 10، شماره 1، فروردین 2026، صفحه 61-80 اصل مقاله (874.33 K) | ||
| نوع مقاله: Original article | ||
| شناسه دیجیتال (DOI): 10.22059/jcss.2025.395014.1146 | ||
| نویسندگان | ||
| Mohamad Arefi* 1؛ Parisa Rahmani2؛ Hamid Shokrzadeh2 | ||
| 1Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran. | ||
| 2Department of Computer Engineering, Par.C., Islamic Azad University, Tehran, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Intrusion Detection System (IDS)؛ IoT؛ machine learning algorithm؛ meta-heuristic algorithms؛ network security؛ Sine-Cosine Algorithm (SCA) | ||
| مراجع | ||
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