Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT
Computer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, network nodes can be smart objects,...
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| Format: | Article |
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Islamic Azad University Bushehr Branch
2024-02-01
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| Series: | مهندسی مخابرات جنوب |
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| Online Access: | https://sanad.iau.ir/journal/jce/Article/869978 |
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| author | Sepehr Sharifi Soulmaz Gheisari |
| author_facet | Sepehr Sharifi Soulmaz Gheisari |
| author_sort | Sepehr Sharifi |
| collection | DOAJ |
| description | Computer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, network nodes can be smart objects, and in this sense, this network has many nodes and there is a lot of traffic in this network. Like any computer network, it faces its own challenges and problems, one of which is the issue of network intrusion and disruption. This dissertation focuses on detecting anomaly-based intrusion into the Internet of Things using data mining. In this study, after collecting and preparing data, the improved support vector machine with grasshopper optimization algorithm is used as a proposed method to detect anomaly-based intrusion in the Internet of Things. The bagging and k-nearest neighbor classifiers and Basic SVM are compared based on error types and standard performance criteria. The simulation results show 97.2% accuracy in the proposed method and better performance compared to other methods. |
| format | Article |
| id | doaj-art-680ff2357704492492bc02bd0b5e14d4 |
| institution | Kabale University |
| issn | 2980-9231 |
| language | fas |
| publishDate | 2024-02-01 |
| publisher | Islamic Azad University Bushehr Branch |
| record_format | Article |
| series | مهندسی مخابرات جنوب |
| spelling | doaj-art-680ff2357704492492bc02bd0b5e14d42025-01-25T17:51:16ZfasIslamic Azad University Bushehr Branchمهندسی مخابرات جنوب2980-92312024-02-0112464358Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoTSepehr Sharifi0Soulmaz Gheisari1Department of Information Technology ,Science and Research Branch, Islamic Azad university, Tehran, IranDepartment of computer engineering, Pardis Branch, Islamic Azad University, Pardis, IranComputer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, network nodes can be smart objects, and in this sense, this network has many nodes and there is a lot of traffic in this network. Like any computer network, it faces its own challenges and problems, one of which is the issue of network intrusion and disruption. This dissertation focuses on detecting anomaly-based intrusion into the Internet of Things using data mining. In this study, after collecting and preparing data, the improved support vector machine with grasshopper optimization algorithm is used as a proposed method to detect anomaly-based intrusion in the Internet of Things. The bagging and k-nearest neighbor classifiers and Basic SVM are compared based on error types and standard performance criteria. The simulation results show 97.2% accuracy in the proposed method and better performance compared to other methods.https://sanad.iau.ir/journal/jce/Article/869978grasshopper optimization algorithmiotsupport vector machineanomaly-based intrusion detection |
| spellingShingle | Sepehr Sharifi Soulmaz Gheisari Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT مهندسی مخابرات جنوب grasshopper optimization algorithm iot support vector machine anomaly-based intrusion detection |
| title | Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT |
| title_full | Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT |
| title_fullStr | Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT |
| title_full_unstemmed | Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT |
| title_short | Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT |
| title_sort | design of anomaly based intrusion detection system using support vector machine and grasshopper optimization algorithm in iot |
| topic | grasshopper optimization algorithm iot support vector machine anomaly-based intrusion detection |
| url | https://sanad.iau.ir/journal/jce/Article/869978 |
| work_keys_str_mv | AT sepehrsharifi designofanomalybasedintrusiondetectionsystemusingsupportvectormachineandgrasshopperoptimizationalgorithminiot AT soulmazgheisari designofanomalybasedintrusiondetectionsystemusingsupportvectormachineandgrasshopperoptimizationalgorithminiot |