Intrusion detection algorithm of wireless network based on network traffic anomaly analysis
Due to the openness and sharing nature of wireless networks, they are vulnerable to various network attacks. To promptly identify and mitigate abnormal behaviors while ensuring normal operation and security, this paper proposes an algorithm for detecting compromised nodes in wireless networks based...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000829 |
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| _version_ | 1849697542498942976 |
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| author | Xiangqian Nie Jiao Xing Qimeng Li Fan Xiao |
| author_facet | Xiangqian Nie Jiao Xing Qimeng Li Fan Xiao |
| author_sort | Xiangqian Nie |
| collection | DOAJ |
| description | Due to the openness and sharing nature of wireless networks, they are vulnerable to various network attacks. To promptly identify and mitigate abnormal behaviors while ensuring normal operation and security, this paper proposes an algorithm for detecting compromised nodes in wireless networks based on network traffic anomaly analysis. In the proposed detection architecture, a network traffic data acquisition module mines and reconstructs real-time traffic data from wireless nodes, removing redundant information. The processed data is then fed into an anomaly analysis module, where abnormal traffic features are extracted and dimensionality-reduced via a stacked autoencoder to form standardized anomaly profiles. These features are analyzed by an intrusion detection module combining particle swarm optimization and support vector machine algorithms. Experimental results demonstrate that the algorithm efficiently extracts traffic anomalies, accurately detects attack duration and traffic volume changes in compromised nodes, and maintains a false detection rate below 6 %. |
| format | Article |
| id | doaj-art-fac1d9e37f12401882fc673fb4a1378d |
| institution | DOAJ |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-fac1d9e37f12401882fc673fb4a1378d2025-08-20T03:19:11ZengElsevierEgyptian Informatics Journal1110-86652025-06-013010068910.1016/j.eij.2025.100689Intrusion detection algorithm of wireless network based on network traffic anomaly analysisXiangqian Nie0Jiao Xing1Qimeng Li2Fan Xiao3Corresponding author.; State Grid Hebei Information & Telecommunication Company, Shijiazhuang 050000 Hebei, ChinaState Grid Hebei Information & Telecommunication Company, Shijiazhuang 050000 Hebei, ChinaState Grid Hebei Information & Telecommunication Company, Shijiazhuang 050000 Hebei, ChinaState Grid Hebei Information & Telecommunication Company, Shijiazhuang 050000 Hebei, ChinaDue to the openness and sharing nature of wireless networks, they are vulnerable to various network attacks. To promptly identify and mitigate abnormal behaviors while ensuring normal operation and security, this paper proposes an algorithm for detecting compromised nodes in wireless networks based on network traffic anomaly analysis. In the proposed detection architecture, a network traffic data acquisition module mines and reconstructs real-time traffic data from wireless nodes, removing redundant information. The processed data is then fed into an anomaly analysis module, where abnormal traffic features are extracted and dimensionality-reduced via a stacked autoencoder to form standardized anomaly profiles. These features are analyzed by an intrusion detection module combining particle swarm optimization and support vector machine algorithms. Experimental results demonstrate that the algorithm efficiently extracts traffic anomalies, accurately detects attack duration and traffic volume changes in compromised nodes, and maintains a false detection rate below 6 %.http://www.sciencedirect.com/science/article/pii/S1110866525000829Network trafficWireless networkInvaded nodeSelf-coding network |
| spellingShingle | Xiangqian Nie Jiao Xing Qimeng Li Fan Xiao Intrusion detection algorithm of wireless network based on network traffic anomaly analysis Egyptian Informatics Journal Network traffic Wireless network Invaded node Self-coding network |
| title | Intrusion detection algorithm of wireless network based on network traffic anomaly analysis |
| title_full | Intrusion detection algorithm of wireless network based on network traffic anomaly analysis |
| title_fullStr | Intrusion detection algorithm of wireless network based on network traffic anomaly analysis |
| title_full_unstemmed | Intrusion detection algorithm of wireless network based on network traffic anomaly analysis |
| title_short | Intrusion detection algorithm of wireless network based on network traffic anomaly analysis |
| title_sort | intrusion detection algorithm of wireless network based on network traffic anomaly analysis |
| topic | Network traffic Wireless network Invaded node Self-coding network |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000829 |
| work_keys_str_mv | AT xiangqiannie intrusiondetectionalgorithmofwirelessnetworkbasedonnetworktrafficanomalyanalysis AT jiaoxing intrusiondetectionalgorithmofwirelessnetworkbasedonnetworktrafficanomalyanalysis AT qimengli intrusiondetectionalgorithmofwirelessnetworkbasedonnetworktrafficanomalyanalysis AT fanxiao intrusiondetectionalgorithmofwirelessnetworkbasedonnetworktrafficanomalyanalysis |