Arefi, Mohamad, Rahmani, Parisa, Shokrzadeh, Hamid. (1404). An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things. , 10(1), 61-80. doi: 10.22059/jcss.2025.395014.1146
Mohamad Arefi; Parisa Rahmani; Hamid Shokrzadeh. "An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things". , 10, 1, 1404, 61-80. doi: 10.22059/jcss.2025.395014.1146
Arefi, Mohamad, Rahmani, Parisa, Shokrzadeh, Hamid. (1404). 'An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things', , 10(1), pp. 61-80. doi: 10.22059/jcss.2025.395014.1146
Arefi, Mohamad, Rahmani, Parisa, Shokrzadeh, Hamid. An Arctic Puffin Optimization with SCA approach, enhanced by a random neural network model for detecting attacks on the Internet of Things. , 1404; 10(1): 61-80. 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

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
آمار
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