{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:09:58Z","timestamp":1784203798367,"version":"3.55.0"},"reference-count":85,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neucom.2026.134098","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T07:34:24Z","timestamp":1779780864000},"page":"134098","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["VLSA-CL: A variational latent space alignment and contrastive learning framework for robust detection of IoT botnet attacks under concept drift"],"prefix":"10.1016","volume":"696","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6972-8935","authenticated-orcid":false,"given":"Hassan","family":"Wasswa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hussein A.","family":"Abbass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Lynar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134098_bib0005","article-title":"Deep learning for network intrusion: a hierarchical approach to reduce false alarms","volume":"18","author":"Moore","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112614","article-title":"Enhancing the security in IoT and IIoT networks: an intrusion detection scheme leveraging deep transfer learning","volume":"305","author":"Ahmad","year":"2024","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134098_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127504","article-title":"ResDNViT: a hybrid architecture for netflow-based attack detection using a residual dense network and vision transformer","volume":"282","author":"Wasswa","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110966","article-title":"TS-IDS: traffic-aware self-supervised learning for IoT network intrusion detection","volume":"279","author":"Nguyen","year":"2023","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134098_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112633","article-title":"An automated intrusion detection system in IoT system using attention based deep bidirectional sparse auto encoder model","volume":"305","author":"Swathi","year":"2024","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134098_bib0030","author":"Wasswa"},{"key":"10.1016\/j.neucom.2026.134098_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129021","article-title":"Improved attack detection in IoT and IIoT networks using attention mechanisms in convolutional neural networks","volume":"296","author":"Ahmad","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131285","article-title":"XMF-GNN: a cross-modality dynamic fusion heterogeneous graph neural network for network intrusion detection","volume":"655","author":"Ma","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134098_bib0045","author":"Wasswa"},{"issue":"7","key":"10.1016\/j.neucom.2026.134098_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.asej.2024.102777","article-title":"A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem","volume":"15","author":"Nazir","year":"2024","journal-title":"Ain Shams Eng. J."},{"key":"10.1016\/j.neucom.2026.134098_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126886","article-title":"Intrusion detection for industrial internet of things based on deep learning","volume":"564","author":"Lu","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134098_bib0060","series-title":"2023 IEEE Region 10 Symposium, TENSYMP 2023","first-page":"1","article-title":"Enhancing IoT-Botnet detection using variational auto-encoder and cost-sensitive learning: a deep learning approach for imbalanced datasets","author":"Wasswa","year":"2023"},{"key":"10.1016\/j.neucom.2026.134098_bib0065","doi-asserted-by":"crossref","first-page":"6430","DOI":"10.1109\/ACCESS.2021.3140015","article-title":"Generative deep learning to detect cyberattacks for the IoT-23 dataset","volume":"10","author":"Abdalgawad","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.134098_bib0070","series-title":"Proceedings of the 8th ACM on Cyber-Physical System Security Workshop","first-page":"41","article-title":"Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks","author":"Zakariyya","year":"2022"},{"key":"10.1016\/j.neucom.2026.134098_bib0075","series-title":"2024 3rd International Conference for Innovation in Technology (INOCON)","first-page":"1","article-title":"Impact of latent space dimension on IoT botnet detection performance: VAE-encoder versus ViT-Encoder","author":"Wasswa","year":"2024"},{"issue":"2","key":"10.1016\/j.neucom.2026.134098_bib0080","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1007\/s10207-023-00787-8","article-title":"TL-BILSTM IoT: transfer learning model for prediction of intrusion detection system in IoT environment","volume":"23","author":"Nandanwar","year":"2024","journal-title":"Int. J. Inf. Secur."},{"key":"10.1016\/j.neucom.2026.134098_bib0085","series-title":"Information Systems Security and Privacy: 6th International Conference, ICISSP 2020, Valletta, Malta, February 25\u201327, 2020, Revised Selected Papers","first-page":"222","article-title":"Using MedBIoT dataset to build effective machine learning-based IoT botnet detection systems","author":"Nomm","year":"2022"},{"key":"10.1016\/j.neucom.2026.134098_bib0090","series-title":"2023 Global Conference on Information Technologies and Communications (GCITC)","first-page":"1","article-title":"IoT botnet detection: application of vision transformer to classification of network flow traffic","author":"Wasswa","year":"2023"},{"issue":"6","key":"10.1016\/j.neucom.2026.134098_bib0095","article-title":"A distributed intrusion detection system using machine learning for IoT based on ToN-IoT dataset","volume":"13","author":"Gad","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0100","series-title":"2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","first-page":"1","article-title":"Securing networks: unleashing the power of the FT-transformer for intrusion detection","author":"Saraniya","year":"2024"},{"key":"10.1016\/j.neucom.2026.134098_bib0105","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","article-title":"Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: bot-iot dataset","volume":"100","author":"Koroniotis","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"issue":"3","key":"10.1016\/j.neucom.2026.134098_bib0110","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","article-title":"N-baiot\u2014network-based detection of IoT botnet attacks using deep autoencoders","volume":"17","author":"Meidan","year":"2018","journal-title":"IEEE Pervasive Comput."},{"issue":"1","key":"10.1016\/j.neucom.2026.134098_bib0115","first-page":"1","article-title":"IoT-23: a labeled dataset with malicious and benign IoT network traffic","volume":"4","author":"Garc\u00eda","year":"2020","journal-title":"Cybersecurity"},{"issue":"1","key":"10.1016\/j.neucom.2026.134098_bib0120","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/JIOT.2021.3085194","article-title":"ToN_IoT: the role of heterogeneity and the need for standardization of features and attack types in IoT network intrusion data sets","volume":"9","author":"Booij","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.134098_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2021.102994","article-title":"A new distributed architecture for evaluating AI-based security systems at the edge: network TON_IoT datasets","volume":"72","author":"Moustafa","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"10.1016\/j.neucom.2026.134098_bib0130","series-title":"2022 19th Annual International Conference on Privacy, Security & Trust (PST)","first-page":"1","article-title":"Towards the development of a realistic multidimensional IoT profiling dataset","author":"Dadkhah","year":"2022"},{"key":"10.1016\/j.neucom.2026.134098_bib0135","author":"Kang"},{"key":"10.1016\/j.neucom.2026.134098_bib0140","author":"Bastian"},{"key":"10.1016\/j.neucom.2026.134098_bib0145","series-title":"25th Annual Network and Distributed System Security Symposium, NDSS 2018","first-page":"1","article-title":"Kitsune: an ensemble of autoencoders for online network intrusion detection","author":"Mirsky","year":"2018"},{"issue":"13","key":"10.1016\/j.neucom.2026.134098_bib0150","doi-asserted-by":"crossref","first-page":"5941","DOI":"10.3390\/s23135941","article-title":"CICIoT2023: a real-time dataset and benchmark for large-scale attacks in IoT environment","volume":"23","author":"Neto","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.neucom.2026.134098_bib0155","doi-asserted-by":"crossref","first-page":"40281","DOI":"10.1109\/ACCESS.2022.3165809","article-title":"Edge-IIoTset: a new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning","volume":"10","author":"Ferrag","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.134098_bib0160","series-title":"Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security","first-page":"111","article-title":"Insomnia: towards concept-drift robustness in network intrusion detection","author":"Andresini","year":"2021"},{"key":"10.1016\/j.neucom.2026.134098_bib0165","series-title":"2024 IEEE Region 10 Symposium (TENSYMP)","first-page":"1","article-title":"Preserving seasonal and trend information: a variational autoencoder-latent space arithmetic based approach for non-stationary learning","author":"Wasswa","year":"2024"},{"key":"10.1016\/j.neucom.2026.134098_bib0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2022.102757","article-title":"Concept drift and cross-device behavior: challenges and implications for effective android malware detection","volume":"120","author":"Guerra-Manzanares","year":"2022","journal-title":"Comput. Secur."},{"key":"10.1016\/j.neucom.2026.134098_bib0175","article-title":"Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge","volume":"141","author":"Nitish","year":"2024","journal-title":"Comput. Secur."},{"key":"10.1016\/j.neucom.2026.134098_bib0180","article-title":"Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks","volume":"131","author":"de C\u00e1mara","year":"2023","journal-title":"Comput. Secur."},{"key":"10.1016\/j.neucom.2026.134098_bib0185","article-title":"TD-IVDM: a multi-scale concept drift detection method for time series forecasting tasks","volume":"653","author":"li Wang","year":"2025","journal-title":"Neurocomputing"},{"issue":"20","key":"10.1016\/j.neucom.2026.134098_bib0190","doi-asserted-by":"crossref","first-page":"19706","DOI":"10.1109\/JIOT.2022.3167005","article-title":"Intrusion detection in the IoT under data and concept drifts: online deep learning approach","volume":"9","author":"Wahab","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.134098_bib0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111681","article-title":"CD-BTMSE: a concept drift detection model based on bidirectional temporal convolutional network and multi-stacking ensemble learning","volume":"294","author":"Cai","year":"2024","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134098_bib0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111596","article-title":"Entropy-based concept drift detection in information systems","volume":"290","author":"Sun","year":"2024","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134098_bib0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131190","article-title":"Concept drift detection based on radial distance","volume":"653","author":"Shang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134098_bib0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128439","article-title":"Concept drift meets industrial data streams: an efficient drift adaptation framework with knowledge embedding and transfer","volume":"290","author":"Zhang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122114","article-title":"QuadCDD: a quadruple-based approach for understanding concept drift in data streams","volume":"238","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127968","article-title":"Feature-based analyses of concept drift","volume":"600","author":"Hinder","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134098_bib0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116510","article-title":"A K-means clustering and SVM based hybrid concept drift detection technique for network anomaly detection","volume":"193","author":"Jain","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.neucom.2026.134098_bib0230","doi-asserted-by":"crossref","first-page":"500","DOI":"10.26599\/BDMA.2023.9020027","article-title":"An adaptive scalable data pipeline for multiclass attack classification in large-scale IoT networks","volume":"7","author":"Saravanan","year":"2024","journal-title":"Big Data Min. Anal."},{"issue":"3","key":"10.1016\/j.neucom.2026.134098_bib0235","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1007\/s10586-021-03249-9","article-title":"Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network data","volume":"24","author":"Jain","year":"2021","journal-title":"Clust. Comput."},{"issue":"6","key":"10.1016\/j.neucom.2026.134098_bib0240","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.3390\/electronics13061004","article-title":"Drift adaptive online DDoS attack detection framework for IoT system","volume":"13","author":"Beshah","year":"2024","journal-title":"Electronics"},{"key":"10.1016\/j.neucom.2026.134098_bib0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114749","article-title":"Latent space alignment for robust detection of IoT botnet attacks in non-stationary environments","volume":"330","author":"Wasswa","year":"2025","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.134098_bib0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109155","article-title":"Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification","volume":"116","author":"Tian","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110108","article-title":"Dynamic malware detection based on supervised contrastive learning","volume":"123","author":"Yang","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109213","article-title":"Dual-view multi-modal contrastive learning for graph-based recommender systems","volume":"116","author":"Guo","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108574","article-title":"DuCL: dual-stage contrastive learning framework for Chinese semantic textual matching","volume":"106","author":"Zuo","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108401","article-title":"Multi-label disaster text classification via supervised contrastive learning for social media data","volume":"104","author":"Xie","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0275","author":"Harshit"},{"issue":"4","key":"10.1016\/j.neucom.2026.134098_bib0280","doi-asserted-by":"crossref","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"10.1016\/j.neucom.2026.134098_bib0285","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s44311-025-00012-w","article-title":"Machine learning-based detection of concept drift in business processes","volume":"2","author":"Kraus","year":"2025","journal-title":"Process Sci."},{"key":"10.1016\/j.neucom.2026.134098_bib0290","doi-asserted-by":"crossref","first-page":"151614","DOI":"10.1109\/ACCESS.2025.3602848","article-title":"Sudden concept drift detection and adaptation in virtual metrology for semiconductor manufacturing","volume":"13","author":"Li","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.134098_bib0295","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/ACCESS.2018.2886026","article-title":"An overview on concept drift learning","volume":"7","author":"Iwashita","year":"2019","journal-title":"IEEE Access"},{"issue":"12","key":"10.1016\/j.neucom.2026.134098_bib0300","first-page":"2346","article-title":"Learning under concept drift: a review","volume":"31","author":"Lu","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.112931","article-title":"Online ensemble learning-based anomaly detection for IoT systems","volume":"173","author":"Wu","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2026.134098_bib0310","series-title":"Proceedings of the 5th International Conference on Big Data Technologies","first-page":"266","article-title":"Cross-domain character recognition through latent space alignment","author":"Wu","year":"2022"},{"key":"10.1016\/j.neucom.2026.134098_bib0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110300","article-title":"Cross-domain recommender systems via multimodal domain adaptation","volume":"123","author":"Shyam","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0320","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"520","article-title":"Cross-modal latent space alignment for image to avatar translation","author":"De Guevara","year":"2023"},{"key":"10.1016\/j.neucom.2026.134098_bib0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107943","article-title":"Discriminative feature alignment: improving transferability of unsupervised domain adaptation by Gaussian-guided latent alignment","volume":"116","author":"Wang","year":"2021","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.neucom.2026.134098_bib0330","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1109\/TII.2021.3108464","article-title":"Concept drift analysis by dynamic residual projection for effectively detecting botnet cyber-attacks in IoT scenarios","volume":"18","author":"Qiao","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.neucom.2026.134098_bib0335","article-title":"Comparative evaluation of a novel IDS dataset for SDN-IoT using deep learning models against InSDN, BoT-IoT, and ToN-IoT","author":"Dhirar","year":"2025","journal-title":"Meas.: Digit."},{"issue":"4","key":"10.1016\/j.neucom.2026.134098_bib0340","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.3390\/app15042043","article-title":"Making a real-time IoT network intrusion-detection system (INIDS) using a realistic BoT\u2013IoT dataset with multiple machine-learning classifiers","volume":"15","author":"Ashraf","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.neucom.2026.134098_bib0345","series-title":"International Conference on Data Science, Technology and Applications 2025","article-title":"Anomaly detection in IoT networks: a performance comparison of transformer, 1D-CNN, and GrowNet models on the Bot-IoT dataset","author":"Kusumastuti","year":"2025"},{"key":"10.1016\/j.neucom.2026.134098_bib0350","doi-asserted-by":"crossref","first-page":"64701","DOI":"10.1109\/ACCESS.2024.3397512","article-title":"UASDAC: an unsupervised adaptive scalable DDoS attack classification in large-scale IoT network under concept drift","volume":"12","author":"Saravanan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.134098_bib0355","series-title":"International Conference on Machine Learning","first-page":"7301","article-title":"Tesseract: tensorised actors for multi-agent reinforcement learning","author":"Mahajan","year":"2021"},{"key":"10.1016\/j.neucom.2026.134098_bib0360","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.inffus.2020.09.004","article-title":"Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams","volume":"66","author":"Zyblewski","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2026.134098_bib0365","article-title":"Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting","volume":"26","author":"Abdulganiyu","year":"2025","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0370","article-title":"Federated learning-enabled lightweight intrusion detection system for wireless sensor networks: a cybersecurity approach against DDoS attacks in smart city environments","volume":"27","author":"Devi","year":"2025","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0375","article-title":"Computationally efficient deep federated learning with optimized feature selection for IoT botnet attack detection","volume":"25","author":"Danquah","year":"2025","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134098_bib0380","series-title":"Optimal Transport: Old and New","first-page":"93","article-title":"The Wasserstein distances","author":"Villani","year":"2009"},{"key":"10.1016\/j.neucom.2026.134098_bib0385","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.neucom.2026.134098_bib0390","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"2","key":"10.1016\/j.neucom.2026.134098_bib0395","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0016-0032(96)00063-4","article-title":"The jensen-shannon divergence","volume":"334","author":"Men\u00e9ndez","year":"1997","journal-title":"J. Frankl. Inst."},{"key":"10.1016\/j.neucom.2026.134098_bib0400","series-title":"13th International Conference, ICONIP","article-title":"Maximum mean discrepancy","volume":"vol. 6","author":"Smola","year":"2006"},{"key":"10.1016\/j.neucom.2026.134098_bib0405","series-title":"The Thirty-Ninth Annual Conference on Neural Information Processing Systems","article-title":"CaliGCL: calibrated graph contrastive learning via partitioned similarity and consistency discrimination","author":"Lin","year":"2025"},{"key":"10.1016\/j.neucom.2026.134098_bib0410","series-title":"Forty-Second International Conference on Machine Learning","article-title":"Mitigating local cohesion and global sparseness in graph contrastive learning with fuzzy boundaries","author":"Lin","year":"2025"},{"issue":"10","key":"10.1016\/j.neucom.2026.134098_bib0415","doi-asserted-by":"crossref","first-page":"6159","DOI":"10.1109\/TKDE.2025.3590482","article-title":"Simplified graph contrastive learning model without augmentation","volume":"37","author":"Lin","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.134098_bib0420","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.neucom.2026.134098_bib0425","author":"Rehman"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226014967?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226014967?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:50:42Z","timestamp":1784202642000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226014967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":85,"alternative-id":["S0925231226014967"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134098","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"VLSA-CL: A variational latent space alignment and contrastive learning framework for robust detection of IoT botnet attacks under concept drift","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134098","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"134098"}}