{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T22:14:02Z","timestamp":1773699242524,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20837-8","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T04:44:21Z","timestamp":1744778661000},"page":"42627-42648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Central Tendency Feature Selection (CTFS): a novel approach for efficient and effective feature selection in intrusion detection systems"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3264-601X","authenticated-orcid":false,"given":"Mohamed Aly","family":"Bouke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azizol","family":"Abdullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nur Izura","family":"Udzir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Normalia","family":"Samian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"20837_CR1","doi-asserted-by":"publisher","first-page":"110227","DOI":"10.1016\/j.asoc.2023.110227","volume":"140","author":"A Si-Ahmed","year":"2022","unstructured":"Si-Ahmed A, Al-Garadi MA, Boustia N (2022) Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things. Appl Soft Comput 140:110227. https:\/\/doi.org\/10.1016\/j.asoc.2023.110227","journal-title":"Appl Soft Comput"},{"key":"20837_CR2","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.aej.2023.03.072","volume":"71","author":"H Kadry","year":"2023","unstructured":"Kadry H, Farouk A, Zanaty EA, Reyad O (2023) Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security. Alexandria Eng J 71:491\u2013500. https:\/\/doi.org\/10.1016\/j.aej.2023.03.072","journal-title":"Alexandria Eng J"},{"key":"20837_CR3","doi-asserted-by":"publisher","unstructured":"Idrissi I, Boukabous M, Grari M, Azizi M, Moussaoui O (2023) An Intrusion Detection System Using Machine Learning for Internet of Medical Things. In IEEE Access, vol. 8, pp 641\u2013649. https:\/\/doi.org\/10.1007\/978-981-19-6223-3_66.","DOI":"10.1007\/978-981-19-6223-3_66"},{"key":"20837_CR4","doi-asserted-by":"publisher","first-page":"102164","DOI":"10.1016\/j.cose.2020.102164","volume":"102","author":"A Nazir","year":"2021","unstructured":"Nazir A, Khan RA (2021) A novel combinatorial optimization based feature selection method for network intrusion detection. Comput Secur 102:102164. https:\/\/doi.org\/10.1016\/j.cose.2020.102164","journal-title":"Comput Secur"},{"key":"20837_CR5","unstructured":"Kurakin A, Goodfellow I, Bengio S (2016) Adversarial machine learning at scale. arXiv Prepr. arXiv1611.01236"},{"issue":"May","key":"20837_CR6","doi-asserted-by":"publisher","first-page":"103419","DOI":"10.1016\/j.jnca.2022.103419","volume":"205","author":"D Soni","year":"2022","unstructured":"Soni D, Kumar N (2022) Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy. J Netw Comput Appl 205(May):103419. https:\/\/doi.org\/10.1016\/j.jnca.2022.103419","journal-title":"J Netw Comput Appl"},{"issue":"2019","key":"20837_CR7","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1016\/j.procs.2020.04.133","volume":"171","author":"T Saranya","year":"2020","unstructured":"Saranya T, Sridevi S, Deisy C, Chung TD, Khan MKAA (2020) Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review. Procedia Comput Sci 171(2019):1251\u20131260. https:\/\/doi.org\/10.1016\/j.procs.2020.04.133","journal-title":"Procedia Comput Sci"},{"issue":"June","key":"20837_CR8","doi-asserted-by":"publisher","first-page":"100532","DOI":"10.1016\/j.clet.2022.100532","volume":"9","author":"A Balla","year":"2022","unstructured":"Balla A, Habaebi MH, Islam MR, Mubarak S (2022) Applications of deep learning algorithms for Supervisory Control and Data Acquisition intrusion detection system. Clean Eng Technol 9(June):100532. https:\/\/doi.org\/10.1016\/j.clet.2022.100532","journal-title":"Clean Eng Technol"},{"issue":"1","key":"20837_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48185\/jaai.v3i1.450","volume":"3","author":"MA Bouke","year":"2022","unstructured":"Bouke MA, Abdullah A, ALshatebi SH, Abdullah MT (2022) E2IDS: An Enhanced Intelligent Intrusion Detection System Based On Decision Tree Algorithm. J Appl Artif Intell 3(1):1\u201316. https:\/\/doi.org\/10.48185\/jaai.v3i1.450","journal-title":"J Appl Artif Intell"},{"key":"20837_CR10","doi-asserted-by":"publisher","first-page":"59386","DOI":"10.1109\/ACCESS.2023.3284975","volume":"11","author":"MA Bouke","year":"2023","unstructured":"Bouke MA, Abdullah A, Frnda J, Cengiz K, Salah B (2023) BukaGini: A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance. IEEE Access 11:59386\u201359396. https:\/\/doi.org\/10.1109\/ACCESS.2023.3284975","journal-title":"IEEE Access"},{"key":"20837_CR11","doi-asserted-by":"publisher","first-page":"102221","DOI":"10.1016\/j.cose.2021.102221","volume":"104","author":"S Yuan","year":"2021","unstructured":"Yuan S, Wu X (2021) Deep learning for insider threat detection: Review, challenges and opportunities. Comput Secur 104:102221. https:\/\/doi.org\/10.1016\/j.cose.2021.102221","journal-title":"Comput Secur"},{"issue":"12","key":"20837_CR12","doi-asserted-by":"publisher","first-page":"3254","DOI":"10.1109\/TC.2022.3148235","volume":"71","author":"X Hu","year":"2022","unstructured":"Hu X et al (2022) A Systematic View of Model Leakage Risks in Deep Neural Network Systems. IEEE Trans Comput 71(12):3254\u20133267. https:\/\/doi.org\/10.1109\/TC.2022.3148235","journal-title":"IEEE Trans Comput"},{"key":"20837_CR13","doi-asserted-by":"publisher","unstructured":"Abidin DZ, Nurmaini S, Firsandava Malik R, Erwin, Rasywir E, Pratama Y (2020) RSSI Data Preparation for Machine Learning. Proc. - 2nd Int. Conf. Informatics, Multimedia, Cyber, Inf. Syst. ICIMCIS 2020, pp 284\u2013289. https:\/\/doi.org\/10.1109\/ICIMCIS51567.2020.9354273","DOI":"10.1109\/ICIMCIS51567.2020.9354273"},{"key":"20837_CR14","doi-asserted-by":"publisher","first-page":"100463","DOI":"10.1016\/j.cosrev.2022.100463","volume":"44","author":"I Souiden","year":"2022","unstructured":"Souiden I, Omri MN, Brahmi Z (2022) A survey of outlier detection in high dimensional data streams. Comput Sci Rev 44:100463. https:\/\/doi.org\/10.1016\/j.cosrev.2022.100463","journal-title":"Comput Sci Rev"},{"issue":"August 2022","key":"20837_CR15","doi-asserted-by":"publisher","first-page":"104823","DOI":"10.1016\/j.micpro.2023.104823","volume":"98","author":"MA Bouke","year":"2023","unstructured":"Bouke MA, Abdullah A, ALshatebi SH, Abdullah MT, El Atigh H (2023) An intelligent DDoS attack detection tree-based model using Gini index feature selection method. Microprocess Microsyst 98(August 2022):104823. https:\/\/doi.org\/10.1016\/j.micpro.2023.104823","journal-title":"Microprocess Microsyst"},{"issue":"June","key":"20837_CR16","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.comcom.2019.06.010","volume":"145","author":"RK Deka","year":"2019","unstructured":"Deka RK, Bhattacharyya DK, Kalita JK (2019) Active learning to detect DDoS attack using ranked features. Comput Commun 145(June):203\u2013222. https:\/\/doi.org\/10.1016\/j.comcom.2019.06.010","journal-title":"Comput Commun"},{"key":"20837_CR17","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.cose.2017.10.011","volume":"73","author":"T Hamed","year":"2018","unstructured":"Hamed T, Dara R, Kremer SC (2018) Network intrusion detection system based on recursive feature addition and bigram technique. Comput Secur 73:137\u2013155. https:\/\/doi.org\/10.1016\/j.cose.2017.10.011","journal-title":"Comput Secur"},{"key":"20837_CR18","doi-asserted-by":"publisher","unstructured":"Pilnenskiy N, Smetannikov I (2019) Modern Implementations of Feature Selection Algorithms and Their Perspectives. Conf Open Innov Assoc Fruct, pp 250\u2013256. https:\/\/doi.org\/10.23919\/FRUCT48121.2019.8981498","DOI":"10.23919\/FRUCT48121.2019.8981498"},{"key":"20837_CR19","doi-asserted-by":"publisher","unstructured":"Subba B, Biswas S, Karmakar S (2016) Enhancing performance of anomaly based intrusion detection systems through dimensionality reduction using principal component analysis. In: 2016 IEEE International conference on advanced networks and telecommunications systems (ANTS), pp 1\u20136. https:\/\/doi.org\/10.1109\/ANTS.2016.7947776","DOI":"10.1109\/ANTS.2016.7947776"},{"key":"20837_CR20","doi-asserted-by":"publisher","unstructured":"Can DC, Le HQ, Ha QT (2021) Detection of Distributed Denial of Service Attacks Using Automatic Feature Selection with Enhancement for Imbalance Dataset. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 386\u2013398. https:\/\/doi.org\/10.1007\/978-3-030-73280-6_31","DOI":"10.1007\/978-3-030-73280-6_31"},{"key":"20837_CR21","doi-asserted-by":"publisher","first-page":"113249","DOI":"10.1016\/j.eswa.2020.113249","volume":"148","author":"H Alazzam","year":"2020","unstructured":"Alazzam H, Sharieh A, Sabri KE (2020) A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl 148:113249","journal-title":"Expert Syst Appl"},{"issue":"2","key":"20837_CR22","doi-asserted-by":"publisher","first-page":"612","DOI":"10.14569\/ijacsa.2020.0110277","volume":"11","author":"S Tangirala","year":"2020","unstructured":"Tangirala S (2020) Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. Int J Adv Comput Sci Appl 11(2):612\u2013619. https:\/\/doi.org\/10.14569\/ijacsa.2020.0110277","journal-title":"Int J Adv Comput Sci Appl"},{"key":"20837_CR23","doi-asserted-by":"publisher","first-page":"3794","DOI":"10.1016\/j.matpr.2020.06.218","volume":"33","author":"C Kalimuthan","year":"2020","unstructured":"Kalimuthan C, Arokia Renjit J (2020) Review on intrusion detection using feature selection with machine learning techniques. Mater Today Proc 33:3794\u20133802. https:\/\/doi.org\/10.1016\/j.matpr.2020.06.218","journal-title":"Mater Today Proc"},{"issue":"9","key":"20837_CR24","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.3390\/sym12091424","volume":"12","author":"S Alabdulwahab","year":"2020","unstructured":"Alabdulwahab S, Moon B (2020) Feature selection methods simultaneously improve the detection accuracy and model building time of machine learning classifiers. Symmetry (Basel) 12(9):1424. https:\/\/doi.org\/10.3390\/sym12091424","journal-title":"Symmetry (Basel)"},{"issue":"2024","key":"20837_CR25","first-page":"22","volume":"39","author":"M Bouke","year":"2024","unstructured":"Bouke M, Abdullah A, Udzir N, Samian N (2024) Overcoming the challenges of data lack, leakage, and dimensionality in intrusion detection systems: a comprehensive review. J Commun Inf Syst 39(2024):22\u201334","journal-title":"J Commun Inf Syst"},{"key":"20837_CR26","doi-asserted-by":"publisher","unstructured":"Waguie ET, Al-Turjman F (2022) Artificial Intelligence for Edge Computing Security: A Survey. In: 2022 International Conference on Artificial Intelligence in Everything (AIE), IEEE, pp 446\u2013450. https:\/\/doi.org\/10.1109\/AIE57029.2022.00091","DOI":"10.1109\/AIE57029.2022.00091"},{"key":"20837_CR27","unstructured":"Nour Moustafa Abdelhameed Moustafa. \u201cThe UNSW-NB15 Dataset | UNSW Research, UNSW. Accessed 24 Sep 2021. Available: https:\/\/research.unsw.edu.au\/projects\/unsw-nb15-dataset"},{"issue":"12","key":"20837_CR28","doi-asserted-by":"publisher","first-page":"9395","DOI":"10.1016\/j.aej.2022.02.063","volume":"61","author":"Y Kayode Saheed","year":"2022","unstructured":"Kayode Saheed Y, Idris Abiodun A, Misra S, Kristiansen Holone M, Colomo-Palacios R (2022) A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Eng J 61(12):9395\u20139409. https:\/\/doi.org\/10.1016\/j.aej.2022.02.063","journal-title":"Alexandria Eng J"},{"issue":"1","key":"20837_CR29","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s10586-015-0527-8","volume":"19","author":"SH Kang","year":"2016","unstructured":"Kang SH, Kim KJ (2016) A feature selection approach to find optimal feature subsets for the network intrusion detection system. Cluster Comput 19(1):325\u2013333. https:\/\/doi.org\/10.1007\/s10586-015-0527-8","journal-title":"Cluster Comput"},{"key":"20837_CR30","doi-asserted-by":"publisher","unstructured":"Khraisat A, Gondal I, Vamplew P, Kamruzzaman J (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1). https:\/\/doi.org\/10.1186\/s42400-019-0038-7.","DOI":"10.1186\/s42400-019-0038-7"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20837-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20837-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20837-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T23:06:20Z","timestamp":1759446380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20837-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,16]]},"references-count":30,"journal-issue":{"issue":"34","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["20837"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20837-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,16]]},"assertion":[{"value":"24 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors of this study hereby declare that there are no conflicts of interest related to this research work. All methodologies and analyses were conducted impartially, and personal or financial relationships with organizations or individuals influenced no aspect of the research. This research was conducted for the sole purpose of contributing to the scientific community's understanding and knowledge in the field of machine learning for network intrusion detection. Any affiliations or associations that could potentially influence or bias the authors'objectivity, judgment, or interpretation of the data are expressly declared non-existent in the context of this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest statement"}},{"value":"No additional information is available for this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Additional information"}}]}}