Volume 22, Issue 2 (9-2025)
JSDP 2025, 22(2): 43-64 |
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sarhadi Z, khazaiepoor M. A detection system for smart cities Using a neural network and Sailfish Optimizer algorithm. JSDP 2025; 22 (2) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1378-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1378-en.html
PhD student, Computer Engineering Department, Technical and Engineering Faculty, Birjand Branch, Islamic Azad University, Birjand, Iran
Abstract: (280 Views)
“The Internet of Things” is an extensive network of intelligent objects that has a large number of objects connected to the Internet. One of the applications of the IOT network is in smart cities. In smart cities, all parts of the city, such as the transportation system, electricity network, health network, etc., are interconnected with IOT support. One of the critical challenges of the IOT network is the occurrence of attacks against this network, which causes the network services to be disrupted. Intrusion detection systems are used to detect attacks on the IOT. The role of an IoT network intrusion detection system is to analyze the network traffic, detect abnormal traffic, and send necessary warning to the firewall. One of the methods of detecting attacks on the IOT and smart cities is to use machine learning methods such as support vector machines(SVM). One method to reduce the error of the support vector machine in detecting attacks on the IOT network and the smart city is to use feature selection methods and optimize its parameters. By selecting the feature and optimizing the parameters of the support vector machine, the attack detection error will be reduced. In this article, an intrusion detection method with an artificial neural network and a swordfish optimization algorithm is presented to detect attacks on the smart city. The proposed method includes three different phases: data set balancing with game theory and GAN network, feature selection with Sailfish Optimizer algorithm, and optimization of SVM parameters with Archimedes optimization algorithm (AOA) algorithm. The role of a multilayer neural network in the proposed method of evaluating feature vectors and the role of the support vector machine is to classify network traffic into two categories: attack and normal. The evaluation and tests performed in MATLAB software and on the NSL-KDD data set show that the accuracy, sensitivity, and precision of the proposed method are 99.12%, 98.92%, and 98.96%, respectively, and the support vector machine with Gaussian kernel seems to be more accurate. The results of the experiments showed that the proposed method is more accurate than meta-heuristic algorithms, such as gray wolf optimization and genetic algorithms in detecting attacks on the smart city
Article number: 3
Keywords: Internet of Things, Smart Cities, Intrusion Detection System, Machine Learning, Sailfish Optimizer Algorithm, Feature Selection
Type of Study: Research |
Subject:
Paper
Received: 2023/04/27 | Accepted: 2025/07/21 | Published: 2025/09/13 | ePublished: 2025/09/13
Received: 2023/04/27 | Accepted: 2025/07/21 | Published: 2025/09/13 | ePublished: 2025/09/13
References
1. Z. Abou El Houda, B. Brik, and L. Khoukhi, "Why Should I Trust Your IDS?": An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks," IEEE Open Journal of the Communications Society, vol. 3, pp. 1164-1176, 2022. [DOI:10.1109/OJCOMS.2022.3188750]
2. M. M. Rashid et al., "Adversarial Training for Deep Learning-based Cyberattack Detection in IoT-based Smart City Applications," Computers & Security, p. 102783, 2022. [DOI:10.1016/j.cose.2022.102783]
3. N. Al-Taleb and N. A. Saqib, "Towards a Hybrid Machine Learning Model for Intelligent Cyber Threat Identification in Smart City Environments," Applied Sciences, vol. 12, no. 4, p. 1863, 2022. [DOI:10.3390/app12041863]
4. R. Zhao, Y. Mu, L. Zou, and X. Wen, "A Hybrid Intrusion Detection System Based on Feature Selection and Weighted Stacking Classifier," IEEE Access, vol. 10, pp. 71414-71426, 2022. [DOI:10.1109/ACCESS.2022.3186975]
5. E. Mahdavi, A. Fanian, A. Mirzaei, and Z. Taghiyarrenani, "ITL-IDS: Incremental Transfer Learning for Intrusion Detection Systems," Knowledge-Based Systems, vol. 253, p. 109542, 2022. [DOI:10.1016/j.knosys.2022.109542]
6. A. K. Zamani and A. Chapnevis, "BotNet Intrusion Detection System in Internet of Things with Developed Deep Learning," arXiv preprint, arXiv:2207.04503, 2022.
7. A. A. R. Melvin et al., "Dynamic malware attack dataset leveraging virtual machine monitor audit data for the detection of intrusions in cloud," Trans. Emerging Telecommunications Technologies, vol. 33, no. 4, e4287, 2022. [DOI:10.1002/ett.4287]
8. K. Malik et al., "Lightweight Internet of Things Botnet Detection Using One-Class Classification," Sensors, vol. 22, no. 10, p. 3646, 2022. [DOI:10.3390/s22103646] [PMID] []
9. D. B. Mandru et al., "Assessing deep neural network and shallow for network intrusion detection systems in cyber security," in Computer Networks and Inventive Communication Technologies, Springer, Singapore, 2022, pp. 703-713. [DOI:10.1007/978-981-16-3728-5_52]
10. Z. Rustama and N. P. A. A. Ariantari, "Comparison between Support Vector Machine and Fuzzy Kernel C-Means as Classifiers for Intrusion Detection System using Chi-Square Feature Selection," in AIP Conf. Proc., vol. 20214, no. 2018, 2023. [DOI:10.1063/1.5064211]
11. T. Wu et al., "Intrusion detection system combined enhanced random forest with SMOTE algorithm," EURASIP J. Adv. Signal Process., vol. 2022, no. 1, pp. 1-20, 2022. [DOI:10.1186/s13634-022-00871-6]
12. M. Jeyaselvi et al., "A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks," Cluster Computing, pp. 1-16, 2022. [DOI:10.1007/s10586-022-03607-1]
13. D. Aksu and M. A. Aydin, "MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach," Computers & Security, vol. 118, p. 102717, 2022. [DOI:10.1016/j.cose.2022.102717]
14. M. Ajdani, A. Noori, and H. Ghaffary, "Providing a Consistent Method to Model the Behavior and Modelling Intrusion Detection Using A Hybrid Particle Swarm Optimization-Logistic Regression Algorithm," Security and Communication Networks, 2022. [DOI:10.1155/2022/5933086]
15. S. Shadravan, H. R. Naji, and V. K. Bardsiri, "The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems," Eng. Appl. Artif. Intell., vol. 80, pp. 20-34, 2019. [DOI:10.1016/j.engappai.2019.01.001]
16. O. Ali et al., "A Comprehensive Review of Internet of Things: Technology Stack, Middlewares, and Fog/Edge Computing Interface," Sensors, vol. 22, no. 3, p. 995, 2022. [DOI:10.3390/s22030995] [PMID] []
17. F. Hussain et al., "A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks," IEEE Access, vol. 9, pp. 163412-163430, 2021. [DOI:10.1109/ACCESS.2021.3131014]
18. S. M. Sajjad et al., "Detection and Blockchain-Based Collaborative Mitigation of Internet of Things Botnets," Wireless Communications and Mobile Computing, 2022. [DOI:10.1155/2022/1194899]
19. J. E. M. Díaz, "Internet of things and distributed denial of service as risk factors in information security," in Bioethics in Medicine and Society, IntechOpen, 2020.
20. R. Vishwakarma and A. K. Jain, "A survey of DDoS attacking techniques and defence mechanisms in the IoT network," Telecommunication Systems, vol. 73, no. 1, pp. 3-25, 2020. [DOI:10.1007/s11235-019-00599-z]
21. J. Asharf et al., "A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions," Electronics, vol. 9, no. 7, p. 1177, 2020. [DOI:10.3390/electronics9071177]
22. Z. K. Maseer et al., "Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset," IEEE Access, vol. 9, pp. 22351-22370, 2021. [DOI:10.1109/ACCESS.2021.3056614]
23. M. Almiani et al., "Deep recurrent neural network for IoT intrusion detection system," Simulation Modelling Practice and Theory, vol. 101, p. 102031, 2020. [DOI:10.1016/j.simpat.2019.102031]
24. A. O. Alzahrani and M. J. Alenazi, "Designing a Network Intrusion Detection System Based on Machine Learning for Software Defined Networks," Future Internet, vol. 13, no. 5, p. 111, 2021. [DOI:10.3390/fi13050111]
25. A. Davahli, M. Shamsi, and G. Abaei, "Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks," J. Ambient Intell. Humaniz. Comput., vol. 11, no. 11, pp. 5581-5609, 2020. [DOI:10.1007/s12652-020-01919-x]
26. R. Yao et al., "Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion CNN-LSTM-based approach," Sensors, vol. 21, no. 2, p. 626, 2021. [DOI:10.3390/s21020626] [PMID] []
27. E. Altulaihan, M. A. Almaiah, and A. Aljughaiman, "Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms," Sensors, vol. 24, no. 2, p. 713, 2024. [DOI:10.3390/s24020713] [PMID] []
28. محمدی، شهریار، خلعتبری، احمد، باباگلی، مهدی، «ارائه یک مدل فراابتکاری تشخیص نفوذ به کمک انتخاب ویژگی مبتنی بر بهینه سازی گرگ خاکستری بهبودیافته و جنگل تصادفی»، فصلنامة پردازش علائم و دادهها، دورة 20، شمارة 1، صص 133-144، 1402.
28. Sh. Mohammadi, A. Khalatbari, and M. Babagoli, "Proposing a Meta-heuristic Model of Intrusion Detection Using feature Selection Based on Improved Gray Wolf Optimization and Random Forest," Signal Data Processing, pp. 133-144, 2023. [DOI:10.61186/jsdp.20.1.133]
29. تیموری، احمد، دی پیر، محمود، «سامانه دو سطحی تشخیص نفوذ برای شبکه اینترنت اشیا مبتنی بر یادگیری عمیق»، فصلنامة پردازش علائم و دادهها، دورة 21، شمارة 3، صص3-22، 1401.
29. A. Teymoori and M., Deypir "Two-level intrusion detection system for Internet of Things network based on deep learning," Signal Data Process., vol. 3, no. 1, pp. 3-22, 2024. [DOI:10.61186/jsdp.21.3.3]
30. S. S. Kareem et al., "An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection," Sensors, vol. 22, no. 4, p. 1396, 2022. [DOI:10.3390/s22041396] [PMID] []
31. K. Ren, Y. Zeng, Z. Cao, and Y. Zhang, "ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model," Scientific Reports, vol. 12, no. 1, p. 15370, 2022. [DOI:10.1038/s41598-022-19366-3] [PMID] []
32. J. Figueiredo, C. Serrão, and A. M. de Almeida, "Deep learning model transposition for network intrusion detection systems," Electronics, vol. 12, no. 2, p. 293, 2023. [DOI:10.3390/electronics12020293]
33. A. Abdelkhalek and M. Mashaly, "Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning," The Journal of Supercomputing, vol. 79, no. 10, pp. 10611-10644, 2023. [DOI:10.1007/s11227-023-05073-x]
34. P. Sanju, "Enhancing intrusion detection in IoT systems: A hybrid metaheuristics-deep learning approach with ensemble of recurrent neural networks," Journal of Engineering Research, vol. 11, no. 4, pp. 356-361, 2023. [DOI:10.1016/j.jer.2023.100122]
35. V. Hnamte and J. Hussain, "Dependable intrusion detection system using deep convolutional neural network: A novel framework and performance evaluation approach," Telematics and Informatics Reports, vol. 11, p. 100077, 2023. [DOI:10.1016/j.teler.2023.100077]
36. R. A. Elsayed, R. A. Hamada, M. I. Abdalla, and S. A. Elsaid, "Securing IoT and SDN systems using deep-learning based automatic intrusion detection," Ain Shams Engineering Journal, vol. 14, no. 10, p. 102211, 2023. [DOI:10.1016/j.asej.2023.102211]
37. M. Nanjappan et al., "DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments," Cluster Computing, pp. 1-13, 2024. [DOI:10.1007/s10586-023-04223-3]
38. E. Osa, P. E. Orukpe, and U. Iruansi, "Design and implementation of a deep neural network approach for intrusion detection systems," e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 7, p. 100434, 2024. [DOI:10.1016/j.prime.2024.100434]
39. S. S. Shankar et al., "A novel optimization based deep learning with artificial intelligence approach to detect intrusion attack in network system," Education and Information Technologies, vol. 29, no. 4, pp. 3859-3883, 2024. [DOI:10.1007/s10639-023-11885-4]
40. R. Devendiran and A. V. Turukmane, "Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy," Expert Systems with Applications, vol. 245, p. 123027, 2024. [DOI:10.1016/j.eswa.2023.123027]
41. F. A. Hashim et al., "Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems," Applied Intelligence, vol. 51, pp. 1531-1551, 2021. [DOI:10.1007/s10489-020-01893-z]
42. G. Eom and H. Byeon, "Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique," Mathematics, vol. 11, no. 16, p. 3605, 2023. [DOI:10.3390/math11163605]
43. J. Tanha and Z. Zarei, "The Bombus-terrestris bee optimization algorithm for feature selection," Applied Intelligence, vol. 53, no. 1, pp. 470-490, 2023. [DOI:10.1007/s10489-022-03478-4]
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