
تعداد نشریات | 163 |
تعداد شمارهها | 6,877 |
تعداد مقالات | 74,134 |
تعداد مشاهده مقاله | 137,824,295 |
تعداد دریافت فایل اصل مقاله | 107,228,782 |
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 | ||
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 29 شهریور 1404 اصل مقاله (944.62 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 | ||
Anwar SS, Asaduzzman, Sarker IH. (2024). “A differential privacy aided DEEPFED intrusion detection system for IOT applications”. Security and Privacy. 7(6). https://doi.org/10.1002/spy2.445.
Chen C, Wang LC, Yu CM. (2022). “D2CRP: A novel distributed 2-hop cluster routing protocol for wireless sensor networks”. IEEE Internet of Things Journal. 9(20). https://doi.org/10.1109/JIOT.2022.3148106.
Chen K, Fengyu Z, Lei Y, Shuqian W, Yugang W, Fang W. (2018). “A hybrid particle swarm optimizer with sine cosine acceleration coefficients”. Information Sciences. 422: 218-241. http://dx.doi.org/10.1016/j.ins.2017.09.015.
Hasan M, Islam M, Zarif II, Hashem MMA. (2019). “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches”. Internet Things. 7. https://doi.org/10.1016/j.iot.2019.100059.
Khan S, Alvi AN, Awais Javed M, Al-Otaibi YD, Kashif Bashir A. (2021). “An efficient medium access control protocol for RF energy harvesting based IoT devices”. Computer Communications. 171: 28-38. https://doi.org/10.1016/j.comcom.2021.02.011.
Kumar GS, Premalatha K, Maheshwari GU, Kanna PR, Vijaya G, Nivaashini M. (2024). “Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation”. Engineering Applications of Artificial Intelligence. 128. https://doi.org/10.1016/j.engappai.2023.107399.
Kumar GS, Premalatha K, Maheshwari GU, Kanna PR. (2023). “No more privacy concern: A privacy-chain based homomorphic encryption scheme and statistical method for privacy preservation of user’s private and sensitive data”. Expert Systems with Applications. 234. https://doi.org/10.1016/j.eswa.2023.121071.
Latif S, Zou Z, Idrees Z, Ahmad J. (2020). “Novel attack detection scheme for the industrial internet of things using a lightweight random neural network”. IEEE Access. 8. https://doi.org/10.1109/ACCESS.2020.2994079.
Maazaahi M, Hosseini S. (2025). “Machine learning and metaheuristic optimization algorithms for feature selection and botnet attack detection”. Knowledge and Information Systems. 67: 3549-3597. https://doi.org/10.1007/s10115-024-02322-0.
Mirjalili S. (2016). “SCA: A Sine Cosine Algorithm for solving optimization problems”. Knowledge-Based Systems. 96: 120-133. http://dx.doi.org/10.1016/j.knosys.2015.12.022.
Pahl MO, Aubet FX. (2018a). “Ds2Os Traffic Traces IOT Traffic Traces”. https://www.kaggle.com/francoisxa/ds2ostraffictraces.
Pahl MO, Aubet FX (2018b). “All eyes on you: Distributed multidimensional IoT microservice anomaly detection”. in Proc. 14th Int. Conf. Netw. Service Manage (CNSM). Nov. pp. 72-80.
Rajabi S, Asgari S, Jamali S, Fotohi R. (2024). “An intrusion detection system using the artificial neural network‑based approach and firefly algorithm”. Wireless Personal Communications. 137: 2409-2440. https://doi.org/10.1007/s11277-024-11505-5.
Subramaniam A, Chelladurai S, Ande SA, Srinivasan S. (2024). “Securing IOT network with hybrid evolutionary lion intrusion detection system: a composite motion optimization algorithm for feature selection and ensemble classification”. Journal of Experimental & Theoretical Artificial Intelligence. https://doi.org/10.1080/0952813X.2024.2342858.
Sveleba S, Katerynchuk I, Kuno I, Sveleba N, Semotyjuk O. (2021). “Investigation of the transition mechanism to chaos in multilayer neural networks”. IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT). https://doi.org/10.1109/aict52120.2021.9628919.
Wang LD, Yang G. (2024). “A network intrusion detection system based on deep learning in the IOT”. The Journal of Supercomputing. 80: 24520-24558. https://doi.org/10.1007/s11227-024-06345-w.
Wang Z, Yang X, Zeng Z, He D, Chan S. (2024a). “A hierarchical hybrid intrusion detection model for industrial internet of things”. Peer-to-Peer Networking and Applications. 17: 3385-3407. https://doi.org/10.1007/s12083-024-01749-0.
Wang W, Tian W, Xu D, Zang H. (2024b), “Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization”. Advances in Engineering Software. 195. https://doi.org/10.1016/j.advengsoft.2024.103694.
Wang Z, Yang X, Zeng Z, He D, Chan S. (2024c). “A hierarchical hybrid intrusion detection model for industrial internet of things”. Peer-to-Peer Networking and Applications. 17: 3385-3407. https://doi.org/10.1007/s12083-024-01749-0.
Wang X, Dai L, Yang G. (2024d). “A network intrusion detection system based on deep learning in the IOT”. The Journal of Supercomputing. 80(16): 24520-24558. https://doi.org/10.1007/s11227-024-06345-w.
Zhong C, Sarkar A, Manna S, Khan MZ, Noorwali A, Das A, Chakraborty K. (2024). “Federated learning‑guided intrusion detection and neural key exchange for safeguarding patient data on the internet of medical things”. International Journal of Machine Learning and Cybernetics. 15: 5635-5665. https://doi.org/10.1007/s13042-024-02269-2. | ||
آمار تعداد مشاهده مقاله: 19 تعداد دریافت فایل اصل مقاله: 46 |