{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:11:13Z","timestamp":1784257873721,"version":"3.55.0"},"reference-count":72,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.future.2025.108296","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T07:57:53Z","timestamp":1767167873000},"page":"108296","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Knowledge distillation-based Multi-Optimization intrusion detection system"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2920-0941","authenticated-orcid":false,"given":"Haofan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5729-8536","authenticated-orcid":false,"given":"Farah","family":"Kandah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.future.2025.108296_bib0001","author":"Analytics"},{"key":"10.1016\/j.future.2025.108296_bib0002","series-title":"Internet of Things (IoT) Statistics: Market & Growth Data","author":"Kumar","year":"2025"},{"key":"10.1016\/j.future.2025.108296_bib0003","series-title":"IoT Security Risks: Stats and Trends to Know in 2025","author":"Blanton","year":"2025"},{"key":"10.1016\/j.future.2025.108296_bib0004","author":"Snegireva"},{"key":"10.1016\/j.future.2025.108296_bib0005","series-title":"2023 Threatlabz report indicates 400 malware attacks","author":"Gandhi","year":"2023"},{"issue":"12","key":"10.1016\/j.future.2025.108296_bib0006","first-page":"2370","article-title":"Insights into modern intrusion detection strategies for internet of things ecosystems","volume":"13","author":"Isong","year":"2024","journal-title":"Electronics (Basel)"},{"key":"10.1016\/j.future.2025.108296_bib0007","unstructured":"R. Kimanzi, Kimanga, Peter, D. Cherori, P.K. Gikunda, Deep Learning Algorithms Used in Intrusion Detection Systems-A Review, Technical Report, arXiv preprint, 2024."},{"key":"10.1016\/j.future.2025.108296_bib0008","doi-asserted-by":"crossref","unstructured":"M. Uddin, A. Rahman, Abdul, Dynamic multi layer signature based intrusion detection system using mobile agents, Technical Report, arXiv preprint, 2010.","DOI":"10.5121\/ijnsa.2010.2411"},{"issue":"3","key":"10.1016\/j.future.2025.108296_bib0009","first-page":"1","article-title":"Securing IoT edge: a survey on lightweight cryptography, anonymous routing and communication protocol enhancements","volume":"24","author":"Gui\u0103","year":"2025","journal-title":"Int. J. Inf. Secur."},{"key":"10.1016\/j.future.2025.108296_bib0010","unstructured":"N. Sheikh, Uddin, Rahman, Hasina, Vikram, Shashwat, H. Alqahtani, A lightweight signature-based IDS for IoT environment, Technical Report, arXiv preprint, 2018."},{"key":"10.1016\/j.future.2025.108296_bib0011","series-title":"Snort: Lightweight intrusion detection for networks","author":"Roesch","year":"1999"},{"key":"10.1016\/j.future.2025.108296_bib0012","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.1109\/ACCESS.2021.3137318","article-title":"Investigating the effect of traffic sampling on machine learning-based network intrusion detection approaches","volume":"10","author":"Alikhanov","year":"2021","journal-title":"IEEE Access"},{"issue":"18","key":"10.1016\/j.future.2025.108296_bib0013","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1016\/j.comnet.2013.09.003","article-title":"Bloom filter applications in network security: a state-of-the-art survey","volume":"57","author":"Geravand","year":"2013","journal-title":"Comput. Netw."},{"key":"10.1016\/j.future.2025.108296_bib0014","unstructured":"M.A. Faizal, M. Zaki, Mohd, S. Shahrin, Y. Robiah, S. Rahayu, Siti, B. Nazrulazhar, Threshold verification technique for network intrusion detection system, Technical Report, arXiv preprint, 2009."},{"key":"10.1016\/j.future.2025.108296_bib0015","series-title":"3\u00c9me Conf\u00c9rence Sur La S\u00c9curit\u00c9 Et Architectures R\u00c9seaux (SAR)","first-page":"381","article-title":"Efficient intrusion detection using principal component analysis","author":"Bouzida","year":"2004"},{"issue":"10","key":"10.1016\/j.future.2025.108296_bib0016","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3390\/computers11100142","article-title":"Efficient, lightweight cyber intrusion detection system for IoT ecosystems using mi2g algorithm","volume":"11","author":"Kaushik","year":"2022","journal-title":"Computers"},{"key":"10.1016\/j.future.2025.108296_bib0017","series-title":"An anomaly detection model based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G Internet of Everything","volume":"11","author":"Yin","year":"2024"},{"key":"10.1016\/j.future.2025.108296_bib0018","series-title":"ESA Conference on Adaptive Hardware and Systems (AHS)","first-page":"248","article-title":"Dynamically adaptive and reliable approximate computing using light-weight error analysis","author":"Grigorian","year":"2014"},{"issue":"3","key":"10.1016\/j.future.2025.108296_bib0019","first-page":"913","article-title":"ADEPOS: A novel approximate computing framework for anomaly detection systems and its implementation in 65-nm CMOS","volume":"67","author":"Bose","year":"2019","journal-title":"IEEE Trans. Circuits Syst. I: Regul. Pap."},{"key":"10.1016\/j.future.2025.108296_bib0020","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/TVLSI.2020.3016939","article-title":"ADIC: Anomaly detection integrated circuit in 65-nm CMOS utilizing approximate computing","volume":"28","author":"Kar","year":"2020","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"10.1016\/j.future.2025.108296_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2024.103451","article-title":"Optimizing machine learning models with data-level approximate computing: the role of diverse sampling, precision scaling, quantization and feature selection strategies","volume":"24","author":"Dalloo","year":"2024","journal-title":"Result. Eng."},{"key":"10.1016\/j.future.2025.108296_bib0022","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-3-642-00975-4_24","article-title":"Beyond shannon: characterizing internet traffic with generalized entropy metrics","volume":"10","author":"Tellenbach","year":"2009","journal-title":"Passive Active Netw. Measur.: 10th Int. Conf."},{"key":"10.1016\/j.future.2025.108296_bib0023","unstructured":"S. Han, Mao, Huizi, W.J. Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, Technical Report, arXiv preprint, 2015."},{"key":"10.1016\/j.future.2025.108296_bib0024","series-title":"2024 IEEE International Symposium on Circuits and Systems (ISCAS)","first-page":"1","article-title":"Efficient neural compression with inference-time decoding","author":"Metz","year":"2024"},{"issue":"9","key":"10.1016\/j.future.2025.108296_bib0025","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.3390\/e24091177","article-title":"Evaluation of fast sample entropy algorithms on FPGAs: from performance to energy efficiency","volume":"24","author":"Chen","year":"2022","journal-title":"Entropy"},{"issue":"2","key":"10.1016\/j.future.2025.108296_bib0026","first-page":"962","article-title":"Fully dynamic inference with deep neural networks","volume":"10","author":"Xia","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"10.1016\/j.future.2025.108296_bib0027","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"8817","article-title":"Blockdrop: dynamic inference paths in residual networks","author":"Wu","year":"2018"},{"issue":"4","key":"10.1016\/j.future.2025.108296_bib0028","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/JSTSP.2020.2979669","article-title":"Dual dynamic inference: enabling more efficient, adaptive, and controllable deep inference","volume":"14","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"10.1016\/j.future.2025.108296_bib0029","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","article-title":"Dynamic representations toward efficient inference on deep neural networks by decision gates","author":"Shafiee","year":"2019"},{"key":"10.1016\/j.future.2025.108296_bib0030","doi-asserted-by":"crossref","first-page":"129825","DOI":"10.52202\/079017-4124","article-title":"Fast yet safe: early-exiting with risk control","volume":"37","author":"Jazbec","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"10.1016\/j.future.2025.108296_bib0031","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: a survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.future.2025.108296_bib0032","article-title":"A lightweight IoT intrusion detection model based on improved BERT-of-Theseus","volume":"238","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.future.2025.108296_bib0033","article-title":"Transformer-based knowledge distillation for explainable intrusion detection system","volume":"154","author":"Nadiah","year":"2025","journal-title":"Comput. Secur."},{"issue":"13","key":"10.1016\/j.future.2025.108296_bib0034","doi-asserted-by":"crossref","first-page":"23156","DOI":"10.1109\/JIOT.2024.3387328","article-title":"Adaptive knowledge distillation-based lightweight intelligent fault diagnosis framework in IoT edge computing","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.future.2025.108296_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.future.2024.107637","article-title":"KDRSFL: A knowledge distillation resistance transfer framework for defending model inversion attacks in split federated learning","volume":"166","author":"Chen","year":"2025","journal-title":"Future Generat. Comput. Syst."},{"key":"10.1016\/j.future.2025.108296_bib0036","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.future.2023.12.025","article-title":"A light-weight edge-enabled knowledge distillation technique for next location prediction of multitude transportation means","volume":"154","author":"Tsanakas","year":"2024","journal-title":"Future Generat. Comput. Syst."},{"key":"10.1016\/j.future.2025.108296_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112118","article-title":"Lightweight intrusion detection model based on CNN and knowledge distillation","volume":"165","author":"Wang","year":"2024","journal-title":"Appl. Soft Comput."},{"issue":"7","key":"10.1016\/j.future.2025.108296_bib0038","doi-asserted-by":"crossref","first-page":"291","DOI":"10.3390\/computers14070291","article-title":"A lightweight intrusion detection system for IoT and UAV using deep neural networks with knowledge distillation","volume":"14","author":"Wisanwanichthan","year":"2025","journal-title":"Computers"},{"key":"10.1016\/j.future.2025.108296_bib0039","series-title":"Proceedings of the Sixth Workshop on CPS&IoT Security and Privacy","first-page":"93","article-title":"Transforming in-vehicle network intrusion detection: VAE-based knowledge distillation meets explainable AI","author":"Yagiz","year":"2024"},{"key":"10.1016\/j.future.2025.108296_bib0040","doi-asserted-by":"crossref","unstructured":"R. Frenken, S.G. Bhatti, H. Zhang, Q. Ahmed, KD-GAT: Combining Knowledge Distillation and Graph Attention Transformer for a Controller Area Network Intrusion Detection System, arxiv: 2507.19686edition, 2025.","DOI":"10.1109\/ITSC60802.2025.11423252"},{"key":"10.1016\/j.future.2025.108296_bib0041","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11868","article-title":"Class attention transfer based knowledge distillation","author":"Guo","year":"2023"},{"key":"10.1016\/j.future.2025.108296_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109924","article-title":"The choice of scaling technique matters for classification performance","volume":"133","author":"Amorim","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.future.2025.108296_bib0043","first-page":"1205","article-title":"Efficient feature selection via analysis of relevance and redundancy","volume":"5","author":"Yu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"issue":"19","key":"10.1016\/j.future.2025.108296_bib0044","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.future.2025.108296_bib0045","article-title":"Analyzing ANOVA F-test and sequential feature selection for intrusion detection systems","volume":"14","author":"Siraj","year":"2022","journal-title":"Int. J. Adv. Soft Comput. Appl."},{"issue":"3","key":"10.1016\/j.future.2025.108296_bib0046","doi-asserted-by":"crossref","first-page":"625","DOI":"10.19026\/rjaset.7.299","article-title":"A novel feature selection based on one-way anova f-test for e-mail spam classification","volume":"7","author":"Elssied","year":"2014","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"10.1016\/j.future.2025.108296_bib0047","unstructured":"Y. Qin, Sheng, Z. Quan, N.J. Falkner, Dustdar, Schahram, Wang, Hua, A.V. Vasilakos, When things matter: A data-centric view of the internet of things, Technical Report, arXiv preprint, 2014."},{"key":"10.1016\/j.future.2025.108296_bib0048","series-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","first-page":"2099","article-title":"Fast k-means for large scale clustering","author":"Hu","year":"2017"},{"issue":"16","key":"10.1016\/j.future.2025.108296_bib0049","doi-asserted-by":"crossref","first-page":"7985","DOI":"10.3390\/app12167985","article-title":"Cluster analysis with k-mean versus k-medoid in financial performance evaluation","volume":"12","author":"Herman","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.future.2025.108296_bib0050","unstructured":"G. Hinton, Vinyals, Oriol, J. Dean, Distilling the knowledge in a neural network, Technical Report, arXiv preprint, 2015."},{"key":"10.1016\/j.future.2025.108296_bib0051","article-title":"A survey on knowledge distillation: recent advancements","volume":"18","author":"Moslemi","year":"2024","journal-title":"Mach. Learn. Appl."},{"issue":"6","key":"10.1016\/j.future.2025.108296_bib0052","doi-asserted-by":"crossref","first-page":"938","DOI":"10.3390\/e25060938","article-title":"Attention-Based spatial-Temporal convolution gated recurrent unit for traffic flow forecasting","volume":"25","author":"Zhang","year":"2023","journal-title":"Entropy"},{"key":"10.1016\/j.future.2025.108296_bib0053","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.future.2025.108296_bib0054","unstructured":"H. Gholamalinezhad, H. Khosravi, Pooling methods in deep neural networks, a review, Technical Report, arXiv preprint, 2020."},{"key":"10.1016\/j.future.2025.108296_bib0055","unstructured":"S. Bai, J.Z. Kolter, V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, Technical Report, arXiv preprint, 2018."},{"issue":"6088","key":"10.1016\/j.future.2025.108296_bib0056","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"10.1016\/j.future.2025.108296_bib0057","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"10.1016\/j.future.2025.108296_bib0058","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1016\/j.ifacol.2020.12.1342","article-title":"On the vanishing and exploding gradient problem in gated recurrent units","volume":"53","author":"Rehmer","year":"2020","journal-title":"IFAC-PapersOnLine"},{"issue":"1","key":"10.1016\/j.future.2025.108296_bib0059","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"issue":"9","key":"10.1016\/j.future.2025.108296_bib0060","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.3390\/rs12091366","article-title":"Deep discriminative representation learning with attention map for scene classification","volume":"12","author":"Li","year":"2020","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.future.2025.108296_bib0061","series-title":"Neurocomputing: Algorithms, Architectures and Applications","first-page":"227","article-title":"Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition","author":"Bridle","year":"1990"},{"key":"10.1016\/j.future.2025.108296_bib0062","unstructured":"S. Zagoruyko, N. Komodakis, Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer, Technical Report, arXiv preprint, 2016."},{"issue":"1","key":"10.1016\/j.future.2025.108296_bib0063","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3520142","article-title":"Optimus: an operator fusion framework for deep neural networks","volume":"22","author":"Cai","year":"2022","journal-title":"ACM Trans. Embedded Comput. Syst."},{"key":"10.1016\/j.future.2025.108296_bib0064","series-title":"Proceedings of the 36Th ACM International Conference on Supercomputing","first-page":"1","article-title":"A data-centric optimization framework for machine learning","author":"Rausch","year":"2022"},{"key":"10.1016\/j.future.2025.108296_bib0065","series-title":"Neural Networks: Tricks of the Trade","first-page":"55","article-title":"Early stopping-but when?","author":"Prechelt","year":"2002"},{"issue":"5","key":"10.1016\/j.future.2025.108296_bib0066","doi-asserted-by":"crossref","first-page":"2900","DOI":"10.1109\/TPAMI.2023.3334614","article-title":"Structured pruning for deep convolutional neural networks: a survey","volume":"46","author":"He","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.future.2025.108296_bib0067","series-title":"WUSTL-IIOT-2021 Dataset","year":"2021"},{"key":"10.1016\/j.future.2025.108296_bib0068","series-title":"X-IIoTID: intrusion dataset for IoT and IIoT","year":"2022"},{"key":"10.1016\/j.future.2025.108296_bib0069","series-title":"IoT-FKGDL-SL: Anomaly detection framework integrating knowledge distillation and a swarm learning for 5G IoT","author":"Tang","year":"2024"},{"issue":"4","key":"10.1016\/j.future.2025.108296_bib0070","doi-asserted-by":"crossref","first-page":"6438","DOI":"10.1109\/JIOT.2023.3310794","article-title":"Lkd-stnn: a lightweight malicious traffic detection method for internet of things based on knowledge distillation","volume":"11","author":"Zhu","year":"2023","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"10.1016\/j.future.2025.108296_bib0071","doi-asserted-by":"crossref","first-page":"853","DOI":"10.3390\/biomedinformatics4010048","article-title":"Comparing ANOVA and powershap feature selection methods via shapley additive explanations of models of mental workload built with the theta and alpha EEG band ratios","volume":"4","author":"Raufi","year":"2024","journal-title":"BioMedInformatics"},{"issue":"2","key":"10.1016\/j.future.2025.108296_bib0072","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1049\/cvi2.12020","article-title":"Tanhexp: a smooth activation function with high convergence speed for lightweight neural networks","volume":"15","author":"Liu","year":"2021","journal-title":"IET Comput. Vis."}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X25005904?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X25005904?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:21:00Z","timestamp":1773951660000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X25005904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":72,"alternative-id":["S0167739X25005904"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2025.108296","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Knowledge distillation-based Multi-Optimization intrusion detection system","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2025.108296","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108296"}}