Enhancing network security with information-guided-enhanced Runge Kutta feature selection for intrusion detection
Intrusion detection system (IDS) classify network traffic as either threatening or normal based on data features, aiming to identify malicious activities attempting to compromise computer systems. However, the volume of intrusion-related data is increasing daily, and the redundant features within th...
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| Veröffentlicht in: | Cluster computing 2024-12, Vol.27 (9), p.12569-12602 |
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| creator | Yuan, Li Tian, Xiongjun Yuan, Jiacheng zhang, Jingyu Dai, Xiaojing Heidari, Ali Asghar Chen, Huiling Yu, Sudan |
| description | Intrusion detection system (IDS) classify network traffic as either threatening or normal based on data features, aiming to identify malicious activities attempting to compromise computer systems. However, the volume of intrusion-related data is increasing daily, and the redundant features within this data hinder the improvement of IDS classification performance and efficiency. This study introduces a wrapper feature selection model, denoted as bICSRUN-KNN, with Runge–kutta optimization for information-guided communication (ICSRUN) to detect system intrusions. Comparative experiments on the IEEE CEC 2014 benchmark functions demonstrate ICSRUN’s superiority over other algorithms. Subsequently, comparative experiments are conducted using 12 UCI datasets, NSL-KDD, ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, against competing algorithms. Experimental results demonstrate that the bICSRUN-KNN model achieved remarkable accuracy rates of 98.705% and 98.341% in the binary and multiclass contexts of NSL-KDD. For ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, accuracy rates of 96.107%, 99.772%, and 88.748% are respectively attained. |
| doi_str_mv | 10.1007/s10586-024-04544-x |
| format | Article |
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However, the volume of intrusion-related data is increasing daily, and the redundant features within this data hinder the improvement of IDS classification performance and efficiency. This study introduces a wrapper feature selection model, denoted as bICSRUN-KNN, with Runge–kutta optimization for information-guided communication (ICSRUN) to detect system intrusions. Comparative experiments on the IEEE CEC 2014 benchmark functions demonstrate ICSRUN’s superiority over other algorithms. Subsequently, comparative experiments are conducted using 12 UCI datasets, NSL-KDD, ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, against competing algorithms. Experimental results demonstrate that the bICSRUN-KNN model achieved remarkable accuracy rates of 98.705% and 98.341% in the binary and multiclass contexts of NSL-KDD. 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However, the volume of intrusion-related data is increasing daily, and the redundant features within this data hinder the improvement of IDS classification performance and efficiency. This study introduces a wrapper feature selection model, denoted as bICSRUN-KNN, with Runge–kutta optimization for information-guided communication (ICSRUN) to detect system intrusions. Comparative experiments on the IEEE CEC 2014 benchmark functions demonstrate ICSRUN’s superiority over other algorithms. Subsequently, comparative experiments are conducted using 12 UCI datasets, NSL-KDD, ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, against competing algorithms. Experimental results demonstrate that the bICSRUN-KNN model achieved remarkable accuracy rates of 98.705% and 98.341% in the binary and multiclass contexts of NSL-KDD. For ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, accuracy rates of 96.107%, 99.772%, and 88.748% are respectively attained.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Communication</subject><subject>Communications traffic</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Datasets</subject><subject>Feature selection</subject><subject>Heuristic</subject><subject>Internet of Things</subject><subject>Intrusion detection systems</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Operating Systems</subject><subject>Optimization algorithms</subject><subject>Processor Architectures</subject><subject>Runge-Kutta method</subject><issn>1386-7857</issn><issn>1573-7543</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OAyEURonRxFp9AVeTuEZhgGFmaZr6E5uYGF0TBi7t1HamApPat_FZfDJpx8SdK-De73wkB6FLSq4pIfImUCLKApOcY8IF5_jzCI2okAxLwdlxurO0lqWQp-gshCUhpJJ5NUJ-2i50a5p2nrUQt51_zwKY3jdxl22buMia1nV-rWPTtXjeNxYshgMCNnvp2zl8fz31MerMgY69h4SvwOzjWQITHn0f9i8LcZifoxOnVwEufs8xerubvk4e8Oz5_nFyO8MmJyRiITTYmsnClgDaWcd0XmhBy9rwmgpmau6cM0VpCig1MGcLY2jNGZgSSmrZGF0NvRvfffQQolp2vW_Tl4pRUgleFVKmVD6kjO9C8ODUxjdr7XeKErV3qwa3KrlVB7fqM0FsgEIKJwf-r_of6geSfILO</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Yuan, Li</creator><creator>Tian, Xiongjun</creator><creator>Yuan, Jiacheng</creator><creator>zhang, Jingyu</creator><creator>Dai, Xiaojing</creator><creator>Heidari, Ali Asghar</creator><creator>Chen, Huiling</creator><creator>Yu, Sudan</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>20241201</creationdate><title>Enhancing network security with information-guided-enhanced Runge Kutta feature selection for intrusion detection</title><author>Yuan, Li ; Tian, Xiongjun ; Yuan, Jiacheng ; zhang, Jingyu ; Dai, Xiaojing ; Heidari, Ali Asghar ; Chen, Huiling ; Yu, Sudan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c200t-55aedb376d8eeafdf3a26a518bc4b153cb4fffc68c6e8ae3fd6cc1b43ec8e81d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Communication</topic><topic>Communications traffic</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Datasets</topic><topic>Feature selection</topic><topic>Heuristic</topic><topic>Internet of Things</topic><topic>Intrusion detection systems</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Operating Systems</topic><topic>Optimization algorithms</topic><topic>Processor Architectures</topic><topic>Runge-Kutta method</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Li</creatorcontrib><creatorcontrib>Tian, Xiongjun</creatorcontrib><creatorcontrib>Yuan, Jiacheng</creatorcontrib><creatorcontrib>zhang, Jingyu</creatorcontrib><creatorcontrib>Dai, Xiaojing</creatorcontrib><creatorcontrib>Heidari, Ali Asghar</creatorcontrib><creatorcontrib>Chen, Huiling</creatorcontrib><creatorcontrib>Yu, Sudan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Cluster computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Li</au><au>Tian, Xiongjun</au><au>Yuan, Jiacheng</au><au>zhang, Jingyu</au><au>Dai, Xiaojing</au><au>Heidari, Ali Asghar</au><au>Chen, Huiling</au><au>Yu, Sudan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhancing network security with information-guided-enhanced Runge Kutta feature selection for intrusion detection</atitle><jtitle>Cluster computing</jtitle><stitle>Cluster Comput</stitle><date>2024-12-01</date><risdate>2024</risdate><volume>27</volume><issue>9</issue><spage>12569</spage><epage>12602</epage><pages>12569-12602</pages><issn>1386-7857</issn><eissn>1573-7543</eissn><abstract>Intrusion detection system (IDS) classify network traffic as either threatening or normal based on data features, aiming to identify malicious activities attempting to compromise computer systems. However, the volume of intrusion-related data is increasing daily, and the redundant features within this data hinder the improvement of IDS classification performance and efficiency. This study introduces a wrapper feature selection model, denoted as bICSRUN-KNN, with Runge–kutta optimization for information-guided communication (ICSRUN) to detect system intrusions. Comparative experiments on the IEEE CEC 2014 benchmark functions demonstrate ICSRUN’s superiority over other algorithms. Subsequently, comparative experiments are conducted using 12 UCI datasets, NSL-KDD, ISCX-URL-2016, ISCX-Tor-NonTor-2017, and LUFlow Network, against competing algorithms. Experimental results demonstrate that the bICSRUN-KNN model achieved remarkable accuracy rates of 98.705% and 98.341% in the binary and multiclass contexts of NSL-KDD. 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| subjects | Accuracy Algorithms Artificial intelligence Communication Communications traffic Computer Communication Networks Computer Science Datasets Feature selection Heuristic Internet of Things Intrusion detection systems Machine learning Methods Neural networks Operating Systems Optimization algorithms Processor Architectures Runge-Kutta method |
| title | Enhancing network security with information-guided-enhanced Runge Kutta feature selection for intrusion detection |
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