Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review
The significance of intrusion detection systems in networks has grown because of the digital revolution and increased operations. The intrusion detection method classifies the network traffic as threat or normal based on the data features. The Intrusion detection system faces a trade-off between var...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Internet of Things and Cyber-Physical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345224000038 |
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| author | Shubhkirti Sharma Vijay Kumar Kamlesh Dutta |
| author_facet | Shubhkirti Sharma Vijay Kumar Kamlesh Dutta |
| author_sort | Shubhkirti Sharma |
| collection | DOAJ |
| description | The significance of intrusion detection systems in networks has grown because of the digital revolution and increased operations. The intrusion detection method classifies the network traffic as threat or normal based on the data features. The Intrusion detection system faces a trade-off between various parameters such as detection accuracy, relevance, redundancy, false alarm rate, and other objectives. The paper presents a systematic review of intrusion detection in Internet of Things (IoT) networks using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities and reducing the chances of security attacks. MOAs provide a set of optimized solutions for the intrusion detection process in highly complex IoT networks. This paper presents the identification of multiple objectives of intrusion detection, comparative analysis of multi-objective algorithms for intrusion detection in IoT based on their approaches, and the datasets used for their evaluation. The multi-objective optimization algorithms show the encouraging potential in IoT networks to enhance multiple conflicting objectives for intrusion detection. Additionally, the current challenges and future research ideas are identified. In addition to demonstrating new advancements in intrusion detection techniques, this study attempts to identify research gaps that can be addressed while designing intrusion detection systems for IoT networks. |
| format | Article |
| id | doaj-art-79a69e515b3d4826a292c697a496fc1b |
| institution | Kabale University |
| issn | 2667-3452 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj-art-79a69e515b3d4826a292c697a496fc1b2025-01-27T04:22:36ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-014258267Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic reviewShubhkirti Sharma0Vijay Kumar1Kamlesh Dutta2DoCSE, NIT, Hamirpur, 177005, HP, India; Corresponding author.DoIT, Dr B R Ambedkar NIT, Jalandhar, 144008, Punjab, IndiaDoCSE, NIT, Hamirpur, 177005, HP, IndiaThe significance of intrusion detection systems in networks has grown because of the digital revolution and increased operations. The intrusion detection method classifies the network traffic as threat or normal based on the data features. The Intrusion detection system faces a trade-off between various parameters such as detection accuracy, relevance, redundancy, false alarm rate, and other objectives. The paper presents a systematic review of intrusion detection in Internet of Things (IoT) networks using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities and reducing the chances of security attacks. MOAs provide a set of optimized solutions for the intrusion detection process in highly complex IoT networks. This paper presents the identification of multiple objectives of intrusion detection, comparative analysis of multi-objective algorithms for intrusion detection in IoT based on their approaches, and the datasets used for their evaluation. The multi-objective optimization algorithms show the encouraging potential in IoT networks to enhance multiple conflicting objectives for intrusion detection. Additionally, the current challenges and future research ideas are identified. In addition to demonstrating new advancements in intrusion detection techniques, this study attempts to identify research gaps that can be addressed while designing intrusion detection systems for IoT networks.http://www.sciencedirect.com/science/article/pii/S2667345224000038Multi-objectiveIntrusion detectionIoTOptimization |
| spellingShingle | Shubhkirti Sharma Vijay Kumar Kamlesh Dutta Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review Internet of Things and Cyber-Physical Systems Multi-objective Intrusion detection IoT Optimization |
| title | Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review |
| title_full | Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review |
| title_fullStr | Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review |
| title_full_unstemmed | Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review |
| title_short | Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review |
| title_sort | multi objective optimization algorithms for intrusion detection in iot networks a systematic review |
| topic | Multi-objective Intrusion detection IoT Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2667345224000038 |
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