{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:44:26Z","timestamp":1765370666335,"version":"3.44.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"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":["Iran J Comput Sci"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s42044-025-00249-5","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T09:32:50Z","timestamp":1742290370000},"page":"893-912","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing network security with memory-augmented visual attention networks and predator\u2013prey optimization models"],"prefix":"10.1007","volume":"8","author":[{"given":"Pradeep","family":"Mani","sequence":"first","affiliation":[]},{"given":"Gopalakrishnan","family":"Subburayalu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"249_CR1","first-page":"1","volume":"20","author":"ASA Issa","year":"2023","unstructured":"Issa, A.S.A., Albayrak, Z.: DDoS attack intrusion detection system based on hybridization of CNN and LSTM. Acta Polytechnica Hungarica 20, 1\u201319 (2023)","journal-title":"Acta Polytechnica Hungarica"},{"key":"249_CR2","doi-asserted-by":"publisher","first-page":"6048087","DOI":"10.1155\/2023\/6048087","volume":"2023","author":"A Momand","year":"2023","unstructured":"Momand, A., Jan, S.U., Ramzan, N.: A systematic and comprehensive survey of recent advances in intrusion detection systems using machine learning: deep learning, datasets, and attack taxonomy. J. Sens. 2023, 6048087 (2023)","journal-title":"J. Sens."},{"key":"249_CR3","doi-asserted-by":"crossref","unstructured":"Latha, R., Bommi, R.: Hybrid catboost regression model based intrusion detection system in iot-enabled networks. In: 2023 9th International Conference on Electrical Energy Systems (ICEES), 2023, pp. 264\u2013269","DOI":"10.1109\/ICEES57979.2023.10110148"},{"key":"249_CR4","doi-asserted-by":"publisher","first-page":"64228","DOI":"10.1109\/ACCESS.2023.3289405","volume":"11","author":"M Bakro","year":"2023","unstructured":"Bakro, M., Kumar, R.R., Alabrah, A., Ashraf, Z., Ahmed, M.N., Shameem, M., et al.: An improved design for a cloud intrusion detection system using hybrid features selection approach with ML classifier. IEEE Access 11, 64228\u201364247 (2023)","journal-title":"IEEE Access"},{"key":"249_CR5","doi-asserted-by":"publisher","first-page":"948","DOI":"10.26599\/TST.2023.9010032","volume":"29","author":"A Eljialy","year":"2024","unstructured":"Eljialy, A., Uddin, M.Y., Ahmad, S.: Novel framework for an intrusion detection system using multiple feature selection methods based on deep learning. Tsinghua Sci. Technol. 29, 948\u2013958 (2024)","journal-title":"Tsinghua Sci. Technol."},{"key":"249_CR6","first-page":"1","volume":"15","author":"D Srilatha","year":"2023","unstructured":"Srilatha, D., Thillaiarasu, N.: Implementation of intrusion detection and prevention with deep learning in cloud computing. J. Inf. Technol. Manag. 15, 1\u201318 (2023)","journal-title":"J. Inf. Technol. Manag."},{"key":"249_CR7","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.inffus.2022.09.026","volume":"90","author":"A Thakkar","year":"2023","unstructured":"Thakkar, A., Lohiya, R.: Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system. Inf. Fusion 90, 353\u2013363 (2023)","journal-title":"Inf. Fusion"},{"key":"249_CR8","doi-asserted-by":"publisher","first-page":"23615","DOI":"10.1007\/s11042-023-14795-2","volume":"82","author":"M Mohy-Eddine","year":"2023","unstructured":"Mohy-Eddine, M., Guezzaz, A., Benkirane, S., Azrour, M.: An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection. Multimed. Tools Appl. 82, 23615\u201323633 (2023)","journal-title":"Multimed. Tools Appl."},{"key":"249_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3327024","author":"A Aljuhani","year":"2023","unstructured":"Aljuhani, A., Alamri, A., Kumar, P., Jolfaei, A.: An intelligent and explainable SAAS-based intrusion detection system for resource-constrained IoMT. IEEE Internet Things J. (2023). https:\/\/doi.org\/10.1109\/JIOT.2023.3327024","journal-title":"IEEE Internet Things J."},{"key":"249_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40537-023-00697-5","volume":"10","author":"A Shiravani","year":"2023","unstructured":"Shiravani, A., Sadreddini, M.H., Nahook, H.N.: Network intrusion detection using data dimensions reduction techniques. J. Big Data 10, 27 (2023)","journal-title":"J. Big Data"},{"key":"249_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3349248","author":"HM Saleh","year":"2024","unstructured":"Saleh, H.M., Marouane, H., Fakhfakh, A.: Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2023.3349248","journal-title":"IEEE Access"},{"key":"249_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100233","volume":"7","author":"M Vishwakarma","year":"2023","unstructured":"Vishwakarma, M., Kesswani, N.: A new two-phase intrusion detection system with Na\u00efve Bayes machine learning for data classification and elliptic envelop method for anomaly detection. Decis. Anal. J. 7, 100233 (2023)","journal-title":"Decis. Anal. J."},{"key":"249_CR13","doi-asserted-by":"publisher","first-page":"465","DOI":"10.17798\/bitlisfen.1240469","volume":"12","author":"F T\u00fcrk","year":"2023","unstructured":"T\u00fcrk, F.: Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren \u00dcniversitesi Fen Bilimleri Dergisi 12, 465\u2013477 (2023)","journal-title":"Bitlis Eren \u00dcniversitesi Fen Bilimleri Dergisi"},{"key":"249_CR14","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1016\/j.procs.2022.12.339","volume":"217","author":"AR Muhammad","year":"2023","unstructured":"Muhammad, A.R., Sukarno, P., Wardana, A.A.: Integrated security information and event management (siem) with intrusion detection system (ids) for live analysis based on machine learning. Procedia Comput. Sci. 217, 1406\u20131415 (2023)","journal-title":"Procedia Comput. Sci."},{"key":"249_CR15","doi-asserted-by":"publisher","first-page":"88","DOI":"10.58496\/BJN\/2024\/010","volume":"2024","author":"G Amirthayogam","year":"2024","unstructured":"Amirthayogam, G., Kumaran, N., Gopalakrishnan, S., Brito, K.A., RaviChand, S., Choubey, S.B.: Integrating behavioral analytics and intrusion detection systems to protect critical infrastructure and smart cities. Babylon. J. Netw. 2024, 88\u201397 (2024)","journal-title":"Babylon. J. Netw."},{"key":"249_CR16","doi-asserted-by":"publisher","first-page":"78","DOI":"10.58496\/BJN\/2024\/009","volume":"2024","author":"MS Sheela","year":"2024","unstructured":"Sheela, M.S., Suganthi, R., Gopalakrishnan, S., Karthikeyan, T., Jyothi, K.J., Ramamoorthy, K.: Secure routing and reliable packets transmission In MANET using fast recursive transfer algorithm. Babylon. J. Netw. 2024, 78\u201387 (2024)","journal-title":"Babylon. J. Netw."},{"key":"249_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121912","volume":"238","author":"GSC Kumar","year":"2024","unstructured":"Kumar, G.S.C., Kumar, R.K., Kumar, K.P.V., Sai, N.R., Brahmaiah, M.: Deep residual convolutional neural network: an efficient technique for intrusion detection system. Expert Syst. Appl. 238, 121912 (2024)","journal-title":"Expert Syst. Appl."},{"key":"249_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3349469","author":"K Cengiz","year":"2024","unstructured":"Cengiz, K., Lipsa, S., Dash, R.K., Ivkovi\u0107, N., Konecki, M.: A novel intrusion detection system based on artificial neural network and genetic algorithm with a new dimensionality reduction technique for UAV communication. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3349469","journal-title":"IEEE Access"},{"key":"249_CR19","doi-asserted-by":"publisher","first-page":"4152","DOI":"10.3390\/s24134152","volume":"24","author":"F Ullah","year":"2024","unstructured":"Ullah, F., Turab, A., Ullah, S., Cacciagrano, D., Zhao, Y.: Enhanced network intrusion detection system for internet of things security using multimodal big data representation with transfer learning and game theory. Sensors 24, 4152 (2024)","journal-title":"Sensors"},{"key":"249_CR20","doi-asserted-by":"publisher","DOI":"10.1080\/19393555.2024.2307638","author":"WA Ali","year":"2024","unstructured":"Ali, W.A., Roccotelli, M., Boggia, G., Fanti, M.P.: Intrusion detection system for vehicular ad hoc network attacks based on machine learning techniques. Inf. Secur. J: A Global Perspect. (2024). https:\/\/doi.org\/10.1080\/19393555.2024.2307638","journal-title":"Inf. Secur. J: A Global Perspect."},{"key":"249_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3391918","author":"S Remya","year":"2024","unstructured":"Remya, S., Pillai, M.J., Arjun, C., Subbareddy, S.R., Yun Cho, Y.: Enhancing security in LLNs using a hybrid trust-based intrusion detection system for RPL. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3391918","journal-title":"IEEE Access"},{"key":"249_CR22","doi-asserted-by":"crossref","unstructured":"Sanagana, D.P.R., Tummalachervu, C.K.: Securing cloud computing environment via optimal deep learning-based intrusion detection systems. In: 2024 Second International Conference on Data Science and Information System (ICDSIS), 2024, pp. 1-6","DOI":"10.1109\/ICDSIS61070.2024.10594404"},{"key":"249_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-023-00807-7","author":"YN Kunang","year":"2024","unstructured":"Kunang, Y.N., Nurmaini, S., Stiawan, D., Suprapto, B.Y.: An end-to-end intrusion detection system with IoT dataset using deep learning with unsupervised feature extraction. Int. J. Inf. Secur. (2024). https:\/\/doi.org\/10.1007\/s10207-023-00807-7","journal-title":"Int. J. Inf. Secur."},{"key":"249_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107132","volume":"127","author":"J Wu","year":"2024","unstructured":"Wu, J., Haider, S.A., Yu, H., Irshad, M., Soni, M., Bhadla, M.K., et al.: An intelligent IoT intrusion detection system using HeInit-WGAN and SSO-BNMCNN based multivariate feature analysis. Eng. Appl. Artif. Intell. 127, 107132 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"249_CR25","first-page":"101003","volume":"31","author":"R Jayaraj","year":"2024","unstructured":"Jayaraj, R., Pushpalatha, A., Sangeetha, K., Kamaleshwar, T., Shree, S.U., Damodaran, D.: Intrusion detection based on phishing detection with machine learning. Meas.: Sens. 31, 101003 (2024)","journal-title":"Meas.: Sens."},{"key":"249_CR26","doi-asserted-by":"publisher","first-page":"4319","DOI":"10.32604\/cmc.2024.050586","volume":"79","author":"FS Alrayes","year":"2024","unstructured":"Alrayes, F.S., Zakariah, M., Amin, S.U., Khan, Z.I., Alqurni, J.S.: CNN channel attention intrusion detection system using NSL-KDD dataset. Comput. Mater. Continua 79, 4319 (2024)","journal-title":"Comput. Mater. Continua"},{"key":"249_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.prime.2024.100673","volume":"9","author":"HQ Gheni","year":"2024","unstructured":"Gheni, H.Q., Al-Yaseen, W.L.: Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset. e-Prime-Adv. Electr. Eng. Electro. Energy 9, 100673 (2024)","journal-title":"e-Prime-Adv. Electr. Eng. Electro. Energy"},{"key":"249_CR28","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.: An innovative network intrusion detection system (NIDS): hierarchical deep learning model based on Unsw-Nb15 dataset. Int. J. Data Netw. Sci. 8, 709\u2013722 (2024)","journal-title":"Int. J. Data Netw. Sci."}],"container-title":["Iran Journal of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00249-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42044-025-00249-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00249-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T07:58:48Z","timestamp":1757145528000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42044-025-00249-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["249"],"URL":"https:\/\/doi.org\/10.1007\/s42044-025-00249-5","relation":{},"ISSN":["2520-8438","2520-8446"],"issn-type":[{"type":"print","value":"2520-8438"},{"type":"electronic","value":"2520-8446"}],"subject":[],"published":{"date-parts":[[2025,3,18]]},"assertion":[{"value":"27 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}