{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T15:38:16Z","timestamp":1767713896103,"version":"3.44.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02631-y","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T08:17:29Z","timestamp":1745482649000},"page":"6487-6511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Feature engineering based intelligent intrusion detection system for IoT network security"],"prefix":"10.1007","volume":"16","author":[{"given":"Sapna","family":"Sadhwani","sequence":"first","affiliation":[]},{"given":"Asmi","family":"Sriwastawa","sequence":"additional","affiliation":[]},{"given":"Raja","family":"Muthalagu","sequence":"additional","affiliation":[]},{"given":"Pranav M.","family":"Pawar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"2631_CR1","doi-asserted-by":"crossref","unstructured":"Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), IEEE Xplore, Nov. 01, https:\/\/ieeexplore.ieee.org\/document\/7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"2631_CR2","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis N, Moustafa N, Sitnikova E, Turnbull B (2019) Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset. Future Generation Comput Syst 100:779\u2013796. https:\/\/doi.org\/10.1016\/j.future.2019.05.041","journal-title":"Future Generation Comput Syst"},{"key":"2631_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/access.2020.3022862","volume":"8","author":"A Alsaedi","year":"2020","unstructured":"Alsaedi A, Moustafa N, Tari Z, Mahmood A, Anwar A (2020) TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access 8:1\u20131. https:\/\/doi.org\/10.1109\/access.2020.3022862","journal-title":"IEEE Access"},{"key":"2631_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/jiot.2020.2996590","volume":"8","author":"O Alkadi","year":"2020","unstructured":"Alkadi O, Moustafa N, Turnbull B, Choo KKR (2020) A deep blockchain framework-enabled collaborative intrusion detection for protecting iot and cloud networks. IEEE Internet Things J 8:1\u20131. https:\/\/doi.org\/10.1109\/jiot.2020.2996590","journal-title":"IEEE Internet Things J"},{"key":"2631_CR5","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J (2015) The significant features of the UNSW-NB15 and the kdd99 data sets for network intrusion detection systems, 2015 4th international workshop on building analysis datasets and gathering experience returns for security (BADGERS), 2015, https:\/\/doi.org\/10.1109\/badgers.2015.014.","DOI":"10.1109\/badgers.2015.014."},{"key":"2631_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00379-6","author":"SM Kasongo","year":"2020","unstructured":"Kasongo SM, Sun Y (2020) Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. J of Big Data. https:\/\/doi.org\/10.1186\/s40537-020-00379-6","journal-title":"J of Big Data"},{"key":"2631_CR7","doi-asserted-by":"publisher","first-page":"41525","DOI":"10.1109\/access.2019.2895334","volume":"7","author":"R Vinayakumar","year":"2019","unstructured":"Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525\u201341550. https:\/\/doi.org\/10.1109\/access.2019.2895334","journal-title":"IEEE Access"},{"key":"2631_CR8","doi-asserted-by":"crossref","unstructured":"Elijah AV, Abdullah A, JhanJhi N, Supramaniam M, Abdullateef B (2019) Ensemble and deep-learning methods for two-class and multi-attack anomaly intrusion detection: an empirical study. Int J Adv Comput Sci Appl. DOIhttps:\/\/doi.org\/10.4569\/ijacsa.2019.0100969","DOI":"10.14569\/IJACSA.2019.0100969"},{"key":"2631_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2022.104622","author":"S Saif","year":"2022","unstructured":"Saif S, Das P, Biswas S, Khari M, Shanmuganathan V (2022) HIIDS: Hybrid intelligent intrusion detection system empowered with machine learning and metaheuristic algorithms for application in IoT based healthcare. Microprocessors Microsyst. https:\/\/doi.org\/10.1016\/j.micpro.2022.104622","journal-title":"Microprocessors Microsyst"},{"key":"2631_CR10","doi-asserted-by":"publisher","first-page":"113199","DOI":"10.1109\/access.2021.3104113","volume":"9","author":"SM Kasongo","year":"2021","unstructured":"Kasongo SM (2021) An advanced intrusion detection system for IIoT based on GA and tree based algorithms. IEEE Access 9:113199\u2013113212. https:\/\/doi.org\/10.1109\/access.2021.3104113","journal-title":"IEEE Access"},{"key":"2631_CR11","doi-asserted-by":"crossref","unstructured":"T Janarthanan, S Zargari, 2017 Feature selection in UNSW-NB15 and KDDCUP\u201999 datasets, IEEE Xplore. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8001537. Accessed 20 Jul 2022","DOI":"10.1109\/ISIE.2017.8001537"},{"issue":"29","key":"2631_CR12","doi-asserted-by":"publisher","first-page":"171319","DOI":"10.4108\/eai.13-10-2021.171319","volume":"8","author":"N Sharma","year":"2021","unstructured":"Sharma N, Yadav N, Sharma S (2021) Classification of UNSW-NB15 dataset using exploratory data analysis using ensemble learning. EAI Endorsed Trans Ind Netw Intell Syst 8(29):171319. https:\/\/doi.org\/10.4108\/eai.13-10-2021.171319","journal-title":"EAI Endorsed Trans Ind Netw Intell Syst"},{"key":"2631_CR13","doi-asserted-by":"crossref","unstructured":"Z. Zoghi and G. Serpen, 2021 UNSW-NB15 computer security dataset: analysis through visualization, arXiv:2101.05067 [cs]. Accessed: 09 Jan 2023. [Online]. Available: https:\/\/arxiv.org\/abs\/2101.05067","DOI":"10.22541\/au.170663595.54651719\/v1"},{"issue":"4","key":"2631_CR14","doi-asserted-by":"publisher","first-page":"645","DOI":"10.6633\/IJNS.201807","volume":"20","author":"Y Hamid","year":"2018","unstructured":"Hamid Y, Balasaraswathi V, Journaux L, Sugumaran M (2018) Benchmark datasets for network intrusion detection: a review. Int J Netw Security 20(4):645\u2013654. https:\/\/doi.org\/10.6633\/IJNS.201807","journal-title":"Int J Netw Security"},{"key":"2631_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100615","author":"H Ahmetoglu","year":"2022","unstructured":"Ahmetoglu H, Das R (2022) A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions. Internet Things. https:\/\/doi.org\/10.1016\/j.iot.2022.100615","journal-title":"Internet Things"},{"issue":"5","key":"2631_CR16","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.3390\/app12052672","volume":"12","author":"A Razmjoo","year":"2022","unstructured":"Razmjoo A et al (2022) An investigation of the policies and crucial sectors of smart cities based on IoT application. Appl Sci 12(5):2672. https:\/\/doi.org\/10.3390\/app12052672","journal-title":"Appl Sci"},{"key":"2631_CR17","doi-asserted-by":"publisher","first-page":"100467","DOI":"10.1016\/j.cosrev.2022.100467","volume":"44","author":"E Schiller","year":"2022","unstructured":"Schiller E, Aidoo A, Fuhrer J, Stahl J, Zi\u00f6rjen M, Stiller B (2022) Landscape of IoT security. Comput Sci Rev 44:100467. https:\/\/doi.org\/10.1016\/j.cosrev.2022.100467","journal-title":"Comput Sci Rev"},{"issue":"1","key":"2631_CR18","doi-asserted-by":"publisher","first-page":"138","DOI":"10.30574\/wjarr.2022.15.1.0573","volume":"15","author":"MHU Sharif","year":"2022","unstructured":"Sharif MHU, Mohammed MA, Sharif MHU, Mohammed MA (2022) A literature review of financial losses statistics for cyber security and future trend. World J Adv Res Rev 15(1):138\u2013156","journal-title":"World J Adv Res Rev"},{"key":"2631_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-6186-0_4","author":"MK Kagita","year":"2021","unstructured":"Kagita MK, Thilakarathne N, Gadekallu TR, Maddikunta PKR, Singh S (2021) A review on cyber crimes on the internet of things. Deep Learning Security Privacy Preserv in IoT. https:\/\/doi.org\/10.1007\/978-981-16-6186-0_4","journal-title":"Deep Learning Security Privacy Preserv in IoT"},{"key":"2631_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2022.105028","volume":"58","author":"Z Wang","year":"2022","unstructured":"Wang Z, Xia L, Yuan H, Srinivasan RS, Song X (2022) Principles, research status, and prospects of feature engineering for data-driven building energy prediction: a comprehensive review. J Building Eng 58:105028. https:\/\/doi.org\/10.1016\/j.jobe.2022.105028","journal-title":"J Building Eng"},{"key":"2631_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2020.109326","volume":"198","author":"Y Diao","year":"2021","unstructured":"Diao Y, Yan L, Gao K (2021) Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features. Mater Design 198:109326. https:\/\/doi.org\/10.1016\/j.matdes.2020.109326","journal-title":"Mater Design"},{"key":"2631_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-021-01893-8","author":"M Ahmad","year":"2021","unstructured":"Ahmad M, Riaz Q, Zeeshan M, Tahir H, Haider SA, Khan MS (2021) Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set. EURASIP J Wireless Commun Netw. https:\/\/doi.org\/10.1186\/s13638-021-01893-8","journal-title":"EURASIP J Wireless Commun Netw"},{"key":"2631_CR23","doi-asserted-by":"publisher","first-page":"31306","DOI":"10.1109\/access.2022.3159235","volume":"10","author":"M Aledhari","year":"2022","unstructured":"Aledhari M, Razzak R, Qolomany B, Al-Fuqaha A, Saeed F (2022) Biomedical IoT: enabling technologies, architectural elements, challenges, and future directions. IEEE Access 10:31306\u201331339. https:\/\/doi.org\/10.1109\/access.2022.3159235","journal-title":"IEEE Access"},{"issue":"17","key":"2631_CR24","doi-asserted-by":"publisher","first-page":"9937","DOI":"10.3390\/app13179937","volume":"13","author":"S Sadhwani","year":"2023","unstructured":"Sadhwani S, Manibalan B, Muthalagu R, Pawar P (2023) A lightweight model for DDoS attack detection using machine learning techniques. Appl Sci 13(17):9937","journal-title":"Appl Sci"},{"key":"2631_CR25","doi-asserted-by":"crossref","unstructured":"S Sadhwani, Urvi Kavan Modi, Raja Muthalagu, and Pranav M Pawar 2024 \"SmartSentry: Cyber Threat Intelligence in Industrial IoT.\" IEEE Access,2024","DOI":"10.1109\/ACCESS.2024.3371996"},{"key":"2631_CR26","doi-asserted-by":"crossref","unstructured":"S Sadhwani, Aakar Mathur, Raja Muthalagu, and Pranav Mothabhau Pawar (2024) \"5G-SIID: an intelligent hybrid DDoS intrusion detector for 5G IoT networks.\" Int J Mach Learning Cybernetics, 1-21","DOI":"10.1007\/s13042-024-02332-y"},{"key":"2631_CR27","doi-asserted-by":"publisher","first-page":"102930","DOI":"10.1016\/j.adhoc.2022.102930","volume":"134","author":"IA Khan","year":"2022","unstructured":"Khan IA, Keshk M, Pi D, Khan N, Hussain Y, Soliman H (2022) Enhancing IIoT networks protection: a robust security model for attack detection in internet industrial control systems. Ad Hoc Netw 134:102930","journal-title":"Ad Hoc Netw"},{"issue":"10","key":"2631_CR28","first-page":"8467","volume":"10","author":"KI Ahmed","year":"2022","unstructured":"Ahmed KI, Pi D, Abbas MZ, Zia U, Hussain Y, Soliman H (2022) Federated-SRUs: a federated-simple-recurrent-units-based IDS for accurate detection of cyber attacks against IoT-augmented industrial control systems. IEEE Internet Things J 10(10):8467\u20138476","journal-title":"IEEE Internet Things J"},{"issue":"13","key":"2631_CR29","first-page":"11604","volume":"9","author":"KI Ahmed","year":"2021","unstructured":"Ahmed KI, Moustafa N, Pi D, Sallam KM, Zomaya AY, Li Bentian (2021) A new explainable deep learning framework for cyber threat discovery in industrial IoT networks. IEEE Internet Things J 9(13):11604\u201311613","journal-title":"IEEE Internet Things J"},{"key":"2631_CR30","doi-asserted-by":"crossref","unstructured":"Khan Izhar Ahmed, Dechang Pi, Nasrullah Khan, Zaheer Ullah Khan, Yasir Hussain, Asif Nawaz, and Farman Ali (2021) \"A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks.\" Appl Intell, 1-16","DOI":"10.1007\/s10489-021-02222-8"},{"issue":"4","key":"2631_CR31","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/TSC.2020.2966970","volume":"13","author":"A Yazdinejad","year":"2020","unstructured":"Yazdinejad A, Parizi RM, Dehghantanha A, Zhang Q, Choo KKR (2020) An energy-efficient SDN controller architecture for IoT networks with blockchain-based security. IEEE Trans Services Comput 13(4):625\u2013638","journal-title":"IEEE Trans Services Comput"},{"key":"2631_CR32","doi-asserted-by":"crossref","unstructured":"Namakshenas Danyal, Abbas Yazdinejad, Ali Dehghantanha, and Gautam Srivastava (2024)\"Federated quantum-based privacy-preserving threat detection model for consumer internet of things.\" IEEE Transactions on Consumer Electronics","DOI":"10.1109\/TCE.2024.3377550"},{"key":"2631_CR33","doi-asserted-by":"crossref","unstructured":"Yazdinejad Abbas, Ali Dehghantanha, Hadis Karimipour, Gautam Srivastava, and Reza M. Parizi (2024) \"A robust privacy-preserving federated learning model against model poisoning attacks.\" IEEE Transactions on Information Forensics and Security","DOI":"10.1109\/TIFS.2024.3420126"},{"issue":"11","key":"2631_CR34","doi-asserted-by":"publisher","first-page":"8356","DOI":"10.1109\/TII.2022.3168011","volume":"18","author":"Abbas Yazdinejad","year":"2022","unstructured":"Yazdinejad Abbas, Dehghantanha Ali, Parizi Reza M, Hammoudeh Mohammad, Karimipour Hadis, Srivastava Gautam (2022) Block hunter: federated learning for cyber threat hunting in blockchain-based IIot networks. IEEE Trans Ind Inform 18(11):8356\u20138366","journal-title":"IEEE Trans Ind Inform"},{"issue":"4","key":"2631_CR35","first-page":"5152","volume":"17","author":"S Jacob","year":"2023","unstructured":"Jacob S, Karimipour H, Dehghantanha A, Yazdinejad A, Gadekallu TR, Victor N, Islam A (2023) A generalizable deep neural network method for detecting attacks in industrial cyber-physical systems. IEEE Syst J 17(4):5152\u20135160","journal-title":"IEEE Syst J"},{"key":"2631_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2022.103801","volume":"144","author":"A Yazdinejad","year":"2023","unstructured":"Yazdinejad A, Dehghantanha A, Parizi RM, Srivastava G, Karimipour H (2023) Secure intelligent fuzzy blockchain framework: effective threat detection in Iot networks. Comput Ind 144:103801","journal-title":"Comput Ind"},{"issue":"3","key":"2631_CR37","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1007\/s10586-023-04089-5","volume":"27","author":"N Vu-Duc","year":"2024","unstructured":"Vu-Duc N, Vuong TC, Luong Van T, Tran H (2024) Machine learning-based intrusion detection: feature selection versus feature extraction. Cluster Comput 27(3):2365\u20132379","journal-title":"Cluster Comput"},{"issue":"2","key":"2631_CR38","doi-asserted-by":"publisher","first-page":"709","DOI":"10.5267\/j.ijdns.2024.1.007","volume":"8","author":"M Alsharaiah","year":"2024","unstructured":"Alsharaiah M, Abualhaj M, Baniata L, Al-saaidah A, Kharma Q, Al-Zyoud M (2024) An innovative network intrusion detection system (NIDS): hierarchical deep learning model based on Unsw-Nb15 dataset. Int J Data Net Sci 8(2):709\u2013722","journal-title":"Int J Data Net Sci"},{"key":"2631_CR39","unstructured":"https:\/\/www.kaggle.com\/datasets\/sadhwanisapna\/unsw-nb15-feiids-test-and-train"},{"key":"2631_CR40","doi-asserted-by":"crossref","unstructured":"Pansari Nikunj, Shourya Srivastava, RH Raghavendra, and Mayank Agarwal 2024 \"Attack classification using machine learning on UNSW-NB 15 dataset using XGBoost feature selection and ablation analysis.\" In 2024 IEEE 9th international conference for convergence in technology (I2CT). IEEE, pp. 1-9","DOI":"10.1109\/I2CT61223.2024.10543523"},{"key":"2631_CR41","doi-asserted-by":"crossref","unstructured":"Mohamed Fawzia Omer Albasheer, and Mayank Agarwal 2024 \"Using recursive feature elimination feature selection based machine learning classifier for attack classification on UNSW-NB 15 dataset.\" In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT). IEEE, pp. 1-7","DOI":"10.1109\/I2CT61223.2024.10544076"},{"key":"2631_CR42","doi-asserted-by":"crossref","unstructured":"Graves Alex, J\u00fcrgen Schmidhuber (2005) Framewise phoneme classification with bidirectional LSTM networks. In Proceedings. 2005 IEEE international joint conference on neural networks, 2005., vol. 4, pp. 2047-2052. IEEE","DOI":"10.1109\/IJCNN.2005.1556215"},{"issue":"6","key":"2631_CR43","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1007\/s10586-022-03776-z","volume":"26","author":"A Heidari","year":"2023","unstructured":"Heidari A, Jamali MAJ (2023) Internet of Things intrusion detection systems: a comprehensive review and future directions. Cluster Comput 26(6):3753\u20133780","journal-title":"Cluster Comput"},{"key":"2631_CR44","doi-asserted-by":"crossref","unstructured":"Amiri Zahra, Arash Heidari, and Nima Jafari Navimipour (2024) \"Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation.\" Energy, 132827","DOI":"10.1016\/j.energy.2024.132827"},{"issue":"10","key":"2631_CR45","doi-asserted-by":"publisher","first-page":"8445","DOI":"10.1109\/JIOT.2023.3237661","volume":"10","author":"H Arash","year":"2023","unstructured":"Arash H, Navimipour NJ, Unal M (2023) A secure intrusion detection platform using blockchain and radial basis function neural networks for internet of drones. IEEE Internet Things J 10(10):8445\u20138454","journal-title":"IEEE Internet Things J"},{"key":"2631_CR46","doi-asserted-by":"crossref","unstructured":"Heidari Arash, Houshang Shishehlou, Mehdi Darbandi, Nima Jafari Navimipour, and Senay Yalcin (2024) \"A reliable method for data aggregation on the industrial internet of things using a hybrid optimization algorithm and density correlation degree.\" Cluster Comput, 1-19","DOI":"10.1007\/s10586-024-04351-4"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02631-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02631-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02631-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:00:42Z","timestamp":1757156442000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02631-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,24]]},"references-count":46,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2631"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02631-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,4,24]]},"assertion":[{"value":"4 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}