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
Islamic Azad University, birjand, Iran
Abstract: (28 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 IOTnetwork is in smart cities. In smart cities, all parts of the city, such as the transportation system, electricity network, health network, etc., are connected with the help of the IOT. 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 the 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 is 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 has 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 is 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
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