{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:07:57Z","timestamp":1778821677387,"version":"3.51.4"},"reference-count":232,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Self-Supervised Learning (S-SL) is a recent line of research that could represent the next step to understanding human intuition. By blending the strengths of unsupervised and supervised learning paradigms, S-SL endows Deep Learning models with stronger generalization capabilities. Although better known for its applications in Computer Vision and Natural Language Processing, S-SL has also proved its value in other fields, such as cybersecurity. In this work, we review the current progress and future trends in S-SL for the two most relevant problems discussed in the cybersecurity literature: network intrusion and malware detection. The scope of this survey spans from 2019 to 2025. From an initial analysis of over 200 documents, we distill the 50 most relevant papers. We also highlight opportunity areas, such as attack detection over encrypted network traffic, RAM-based analysis of obfuscated malware, creating S-SL models for tabular data and resource-constrained devices, as well as the research on backdooring, encoder extraction, the transferability of vulnerabilities, and data memorization in S-SL. To the best of our knowledge, this is the first comprehensive survey regarding the application of Self-Supervised Learning in cybersecurity, benchmarking contrastive learning vs auxiliary pretext tasks and presenting the data requirements for implementing S-SL solutions in this field. We hope this paper provides a firm ground for further exploration.<\/jats:p>","DOI":"10.3390\/make8050121","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:54:07Z","timestamp":1777654447000},"page":"121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Survey on Self-Supervised Learning in Cybersecurity: Network Intrusion and Malware Detection"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8343-4530","authenticated-orcid":false,"given":"Josue Genaro","family":"Almaraz-Rivera","sequence":"first","affiliation":[{"name":"Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5597-939X","authenticated-orcid":false,"given":"Jose Antonio","family":"Cantoral-Ceballos","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7072-8924","authenticated-orcid":false,"given":"Juan Felipe","family":"Botero","sequence":"additional","affiliation":[{"name":"Universidad de Antioquia, Electronics and Telecommunications Engineering Department, Calle 67 N.\u00ba 53-108, Medellin 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1145\/3577925","article-title":"Self-Supervised Learning for Videos: A Survey","volume":"55","author":"Schiappa","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_3","first-page":"9486949","article-title":"Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning","volume":"2021","author":"Wang","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_4","unstructured":"LeCun, Y., and Misra, I. (2022, August 12). Self-Supervised Learning: The Dark Matter of Intelligence. Available online: https:\/\/ai.facebook.com\/blog\/self-supervised-learning-the-dark-matter-of-intelligence\/."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Albelwi, S. (2022). Survey on Self-Supervised Learning: Auxiliary Pretext Tasks and Contrastive Learning Methods in Imaging. Entropy, 24.","DOI":"10.3390\/e24040551"},{"key":"ref_6","first-page":"63","article-title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","unstructured":"Lee, I.T., Marwah, M., and Arlitt, M. (2020). Attention-Based Self-Supervised Feature Learning for Security Data. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MSP.2021.3134634","article-title":"Self-Supervised Representation Learning: Introduction, advances, and challenges","volume":"39","author":"Ericsson","year":"2022","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_11","first-page":"22243","article-title":"Big Self-Supervised Models are Strong Semi-Supervised Learners","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","article-title":"Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey","volume":"43","author":"Jing","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Towhid, M.S., and Shahriar, N. (July, January 27). Encrypted Network Traffic Classification using Self-supervised Learning. Proceedings of the 2022 IEEE 8th International Conference on Network Softwarization (NetSoft), Milan, Italy.","DOI":"10.1109\/NetSoft54395.2022.9844044"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Towhid, M.S., and Shahriar, N. (July, January 27). Encrypted Network Traffic Classification in SDN using Self-supervised Learning. Proceedings of the 2022 IEEE 8th International Conference on Network Softwarization (NetSoft), Milan, Italy.","DOI":"10.1109\/NetSoft54395.2022.9844082"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103093","DOI":"10.1016\/j.jnca.2021.103093","article-title":"Emerging DDoS attack detection and mitigation strategies in software-defined networks: Taxonomy, challenges and future directions","volume":"187","author":"Valdovinos","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mahmoud, H., Wu, W., and Gaber, M.M. (2022). A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems. Energies, 15.","DOI":"10.3390\/en15030914"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Almaraz-Rivera, J.G., Cantoral-Ceballos, J.A., and Botero, J.F. (2023). Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks. Sensors, 23.","DOI":"10.3390\/s23218701"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/COMST.2018.2885561","article-title":"Data-Driven Cybersecurity Incident Prediction: A Survey","volume":"21","author":"Sun","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12305","DOI":"10.1002\/int.23088","article-title":"Explainable machine learning in cybersecurity: A survey","volume":"37","author":"Yan","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102954","DOI":"10.1016\/j.cose.2022.102954","article-title":"Cybersecurity capabilities and cyber-attacks as drivers of investment in cybersecurity systems: A UK survey for 2018 and 2019","volume":"124","author":"Arranz","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_21","first-page":"857","article-title":"Self-Supervised Learning: Generative or Contrastive","volume":"35","author":"Liu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_22","first-page":"5879","article-title":"Graph Self-Supervised Learning: A Survey","volume":"35","author":"Liu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.neucom.2019.02.056","article-title":"Application of deep learning to cybersecurity: A survey","volume":"347","author":"Mahdavifar","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105124","DOI":"10.1016\/j.knosys.2019.105124","article-title":"Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues","volume":"189","author":"Aldweesh","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1109\/JSYST.2020.2992966","article-title":"A Comprehensive Survey of Databases and Deep Learning Methods for Cybersecurity and Intrusion Detection Systems","volume":"15","author":"Genovese","year":"2021","journal-title":"IEEE Syst. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100317","DOI":"10.1016\/j.cosrev.2020.100317","article-title":"Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review","volume":"39","author":"Dixit","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_27","first-page":"589","article-title":"Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection","volume":"25","author":"Sewak","year":"2022","journal-title":"Inf. Syst. Front."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19572","DOI":"10.1109\/ACCESS.2022.3151248","article-title":"Machine Learning and Deep Learning Approaches for CyberSecurity: A Review","volume":"10","author":"Halbouni","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109032","DOI":"10.1016\/j.comnet.2022.109032","article-title":"A survey on deep learning for cybersecurity: Progress, challenges, and opportunities","volume":"212","author":"Macas","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.neucom.2022.06.002","article-title":"A survey on neural networks for (cyber-) security and (cyber-) security of neural networks","volume":"500","author":"Pawlicki","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101804","DOI":"10.1016\/j.inffus.2023.101804","article-title":"Artificial intelligence for cybersecurity: Literature review and future research directions","volume":"97","author":"Kaur","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12229","DOI":"10.1109\/ACCESS.2024.3355547","article-title":"A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions","volume":"12","author":"Akin","year":"2024","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/SURV.2013.031413.00127","article-title":"A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks","volume":"15","author":"Zargar","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Almaraz-Rivera, J.G., Perez-Diaz, J.A., and Cantoral-Ceballos, J.A. (2022). Transport and Application Layer DDoS Attacks Detection to IoT Devices by Using Machine Learning and Deep Learning Models. Sensors, 22.","DOI":"10.3390\/s22093367"},{"key":"ref_35","first-page":"92","article-title":"A Survey on Deep Learning: Algorithms, Techniques, and Applications","volume":"51","author":"Pouyanfar","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/OJCOMS.2021.3057679","article-title":"The Road Towards 6G: A Comprehensive Survey","volume":"2","author":"Jiang","year":"2021","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jnca.2012.09.004","article-title":"Intrusion detection system: A comprehensive review","volume":"36","author":"Liao","year":"2013","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cose.2014.02.005","article-title":"On fingerprinting probing activities","volume":"43","author":"Debbabi","year":"2014","journal-title":"Comput. Secur."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"50078","DOI":"10.1109\/ACCESS.2021.3068961","article-title":"Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network","volume":"9","author":"Tufan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","unstructured":"(2025, May 26). Patator. Available online: https:\/\/github.com\/lanjelot\/patator."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Platt, J., and Hoffman, T. (2006). Analysis of Representations for Domain Adaptation. Proceedings of the Advances in Neural Information Processing Systems, MIT Press.","DOI":"10.7551\/mitpress\/7503.001.0001"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1007\/s10462-021-10088-y","article-title":"Explainable artificial intelligence: A comprehensive review","volume":"55","author":"Minh","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bilge, L., and Dumitra\u015f, T. (2012). Before We Knew It: An Empirical Study of Zero-Day Attacks in the Real World. Proceedings of the 2012 ACM Conference on Computer and Communications Security, ACM.","DOI":"10.1145\/2382196.2382284"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100270","DOI":"10.1016\/j.cosrev.2020.100270","article-title":"A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability","volume":"37","author":"Huang","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_48","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Adversarial Examples Are Not Bugs, They Are Features. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"48867","DOI":"10.1109\/ACCESS.2019.2908033","article-title":"Evading Anti-Malware Engines With Deep Reinforcement Learning","volume":"7","author":"Fang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103444","DOI":"10.1016\/j.jnca.2022.103444","article-title":"A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning","volume":"205","author":"Carrera","year":"2022","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1145\/3465055","article-title":"An Attentive Survey of Attention Models","volume":"12","author":"Chaudhari","year":"2021","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The Graph Neural Network Model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A Comprehensive Survey on Graph Neural Networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","article-title":"One-shot learning of object categories","volume":"28","author":"Fergus","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","article-title":"Attribute-Based Classification for Zero-Shot Visual Object Categorization","volume":"36","author":"Lampert","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10844-022-00747-z","article-title":"Leveraging siamese networks for one-shot intrusion detection model","volume":"60","author":"Hindy","year":"2022","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sirinam, P., Mathews, N., Rahman, M.S., and Wright, M. (2019). Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-Shot Learning. Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, ACM.","DOI":"10.1145\/3319535.3354217"},{"key":"ref_58","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111820","DOI":"10.1109\/ACCESS.2022.3211306","article-title":"Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"80218","DOI":"10.1109\/ACCESS.2023.3300381","article-title":"From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy","volume":"11","author":"Gupta","year":"2023","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.adhoc.2012.02.016","article-title":"Internet of things: Vision, applications and research challenges","volume":"10","author":"Miorandi","year":"2012","journal-title":"Ad. Hoc Netw."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1109\/COMST.2017.2749442","article-title":"Botnet Communication Patterns","volume":"19","author":"Vormayr","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"106106","DOI":"10.1016\/j.neunet.2024.106106","article-title":"Self-supervised anomaly detection in computer vision and beyond: A survey and outlook","volume":"172","author":"Hojjati","year":"2024","journal-title":"Neural Netw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","article-title":"A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends","volume":"46","author":"Gui","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","unstructured":"Gidaris, S., Singh, P., and Komodakis, N. (2018). Unsupervised Representation Learning by Predicting Image Rotations. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kolesnikov, A., Zhai, X., and Beyer, L. (2019, January 15\u201320). Revisiting Self-Supervised Visual Representation Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00202"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., and Makedon, F. (2021). A Survey on Contrastive Self-Supervised Learning. Technologies, 9.","DOI":"10.3390\/technologies9010002"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_69","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_70","first-page":"21271","article-title":"Bootstrap Your Own Latent\u2014A New Approach to Self-Supervised Learning","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_71","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning, Virtual Online."},{"key":"ref_72","first-page":"6679","article-title":"TabNet: Attentive Interpretable Tabular Learning","volume":"35","author":"Arik","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_73","first-page":"12310","article-title":"Barlow Twins: Self-Supervised Learning via Redundancy Reduction","volume":"Volume 139","author":"Meila","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"key":"ref_74","first-page":"1298","article-title":"data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language","volume":"Volume 162","author":"Chaudhuri","year":"2022","journal-title":"Proceedings of the 39th International Conference on Machine Learning; PMLR"},{"key":"ref_75","unstructured":"van den Oord, A., Li, Y., and Vinyals, O. (2019). Representation Learning with Contrastive Predictive Coding. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"106909","DOI":"10.1109\/ACCESS.2022.3211513","article-title":"Toward the Protection of IoT Networks: Introducing the LATAM-DDoS-IoT Dataset","volume":"10","author":"Botero","year":"2022","journal-title":"IEEE Access"},{"key":"ref_77","unstructured":"Almaraz-Rivera, J.G., Perez-Diaz, J.A., Cantoral-Ceballos, J.A., Botero, J.F., and Trejo, L.A. (2022). LATAM-DDoS-IoT Dataset, IEEE."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"177","DOI":"10.5220\/0010908200003120","article-title":"Detecting Obfuscated Malware using Memory Feature Engineering","volume":"Volume 1","author":"Carrier","year":"2022","journal-title":"Proceedings of the 8th International Conference on Information Systems Security and Privacy"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1145\/3382158","article-title":"A Data-Driven Characterization of Modern Android Spyware","volume":"11","author":"Pierazzi","year":"2020","journal-title":"ACM Trans. Manag. Inf. Syst."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Herrera Silva, J.A., Barona L\u00f3pez, L.I., Valdivieso Caraguay, \u00c1.L., and Hern\u00e1ndez-\u00c1lvarez, M. (2019). A Survey on Situational Awareness of Ransomware Attacks\u2014Detection and Prevention Parameters. Remote Sens., 11.","DOI":"10.3390\/rs11101168"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"118299","DOI":"10.1016\/j.eswa.2022.118299","article-title":"Crypto-ransomware detection using machine learning models in file-sharing network scenarios with encrypted traffic","volume":"209","author":"Berrueta","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_82","first-page":"102828","article-title":"Malware classification and composition analysis: A survey of recent developments","volume":"59","author":"Abusitta","year":"2021","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"102458","DOI":"10.1016\/j.cose.2021.102458","article-title":"Malbert: A novel pre-training method for malware detection","volume":"111","author":"Xu","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_84","first-page":"4171","article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","volume":"Volume 1","author":"Burstein","year":"2019","journal-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"659101","DOI":"10.1155\/2015\/659101","article-title":"A Novel Approach to Detect Malware Based on API Call Sequence Analysis","volume":"11","author":"Ki","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e285","DOI":"10.7717\/peerj-cs.285","article-title":"Deep learning based Sequential model for malware analysis using Windows exe API Calls","volume":"6","author":"Catak","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"e346","DOI":"10.7717\/peerj-cs.346","article-title":"Data augmentation based malware detection using convolutional neural networks","volume":"7","author":"Catak","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"103121","DOI":"10.1109\/ACCESS.2022.3206445","article-title":"Self-Supervised Vision Transformers for Malware Detection","volume":"10","author":"Seneviratne","year":"2022","journal-title":"IEEE Access"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Freitas, S., Duggal, R., and Chau, D.H. (2022). MalNet: A Large-Scale Image Database of Malicious Software. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, ACM.","DOI":"10.1145\/3511808.3557533"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Allix, K., Bissyand\u00e9, T.F., Klein, J., and Le Traon, Y. (2016). AndroZoo: Collecting Millions of Android Apps for the Research Community. Proceedings of the 13th International Conference on Mining Software Repositories, ACM.","DOI":"10.1145\/2901739.2903508"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_93","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"102915","DOI":"10.1016\/j.cose.2022.102915","article-title":"An Android Malware Detection and Classification Approach Based on Contrastive Lerning","volume":"123","author":"Yang","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Mahdavifar, S., Abdul Kadir, A.F., Fatemi, R., Alhadidi, D., and Ghorbani, A.A. (2020). Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning. Proceedings of the 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), IEEE.","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094"},{"key":"ref_96","unstructured":"Shen, D., Zheng, M., Shen, Y., Qu, Y., and Chen, W. (2020). A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation. arXiv."},{"key":"ref_97","first-page":"6256","article-title":"Unsupervised Data Augmentation for Consistency Training","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Jiang, X. (2012). Dissecting Android Malware: Characterization and Evolution. Proceedings of the 2012 IEEE Symposium on Security and Privacy, IEEE.","DOI":"10.1109\/SP.2012.16"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Arp, D., Spreitzenbarth, M., H\u00fcbner, M., Gascon, H., and Rieck, K. (2014, January 23\u201326). Drebin: Effective and explainable detection of android malware in your pocket. Proceedings of the 2014 Network and Distributed System Security Symposium, San Diego, CA, USA.","DOI":"10.14722\/ndss.2014.23247"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.cose.2015.02.007","article-title":"Stealth attacks: An extended insight into the obfuscation effects on Android malware","volume":"51","author":"Maiorca","year":"2015","journal-title":"Comput. Secur."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Sern, L.J., Kai Keng, T., and Fu, C.Z. (2022). BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec. Proceedings of the 2022 1st International Conference on AI in Cybersecurity (ICAIC), IEEE.","DOI":"10.1109\/ICAIC53980.2022.9897062"},{"key":"ref_102","unstructured":"Stroschein, J. (2025, June 11). Malware Samples. Available online: https:\/\/github.com\/jstrosch\/malware-samples."},{"key":"ref_103","unstructured":"(2025, June 11). Vx-Underground. Available online: https:\/\/vx-underground.org\/Samples."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, M., Luo, K., Tong, H., Jin, C., and Xie, B. (2023). BiBE: A Self-supervised Contrastive Learning Architecture for Malware Detection. Proceedings of the 2023 IEEE 11th International Conference on Computer Science and Network Technology (ICCSNT), IEEE.","DOI":"10.1109\/ICCSNT58790.2023.10334572"},{"key":"ref_105","unstructured":"Anderson, H.S., and Roth, P. (2018). EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models. arXiv."},{"key":"ref_106","unstructured":"Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"58823","DOI":"10.1109\/ACCESS.2024.3392251","article-title":"MalSSL\u2014Self-Supervised Learning for Accurate and Label-Efficient Malware Classification","volume":"12","author":"Ismail","year":"2024","journal-title":"IEEE Access"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Chen, X., and He, K. (2021). Exploring Simple Siamese Representation Learning. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref_109","first-page":"9912","article-title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_111","unstructured":"(2025, May 26). Imagenette. Available online: https:\/\/github.com\/fastai\/imagenette."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Nataraj, L., Karthikeyan, S., Jacob, G., and Manjunath, B.S. (2011, January 20). Malware images: Visualization and automatic classification. Proceedings of the 8th International Symposium on Visualization for Cyber Security, Pittsburgh, PA, USA.","DOI":"10.1145\/2016904.2016908"},{"key":"ref_113","unstructured":"Ismail, S.J.I. (2024). Maldeb Dataset, IEEE."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13635-025-00188-5","article-title":"MIDALF\u2014Multimodal image and audio late fusion for malware detection","volume":"2025","author":"Ismail","year":"2025","journal-title":"EURASIP J. Inf. Secur."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Yang, L., Ciptadi, A., Laziuk, I., Ahmadzadeh, A., and Wang, G. (2021). BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware. Proceedings of the 2021 IEEE Security and Privacy Workshops (SPW), IEEE.","DOI":"10.1109\/SPW53761.2021.00020"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1145\/3536425","article-title":"SETTI: A Self-supervised AdvErsarial Malware DeTection ArchiTecture in an IoT Environment","volume":"18","author":"Golmaryami","year":"2022","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"110299","DOI":"10.1016\/j.engappai.2025.110299","article-title":"Self-supervised contrastive representation learning for classifying Internet of Things malware","volume":"150","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_118","unstructured":"Garcia, S., Parmisano, A., and Erquiaga, M.J. (2020). IoT-23: A Labeled Dataset with Malicious and Benign IoT Network Traffic, Zenodo."},{"key":"ref_119","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."},{"key":"ref_120","unstructured":"Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., and Ahmadi, M. (2018). Microsoft Malware Classification Challenge. arXiv."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"103706","DOI":"10.1016\/j.cose.2024.103706","article-title":"BenchMFC: A benchmark dataset for trustworthy malware family classification under concept drift","volume":"139","author":"Jiang","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_122","unstructured":"Pa, Y.M.P., Suzuki, S., Yoshioka, K., Matsumoto, T., Kasama, T., and Rossow, C. (2015). IoTPOT: Analysing the rise of IoT compromises. Proceedings of the 9th USENIX Conference on Offensive Technologies, ACM."},{"key":"ref_123","unstructured":"Kato, S., Tanabe, R., Yoshioka, K., and Matsumoto, T. (2021). Adaptive Observation of Emerging Cyber Attacks targeting Various IoT Devices. Proceedings of the 2021 IFIP\/IEEE International Symposium on Integrated Network Management (IM), IEEE."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"109320","DOI":"10.1016\/j.comnet.2022.109320","article-title":"F2DC: Android malware classification based on raw traffic and neural networks","volume":"217","author":"Lu","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"100529","DOI":"10.1016\/j.cosrev.2022.100529","article-title":"A comprehensive survey on deep learning based malware detection techniques","volume":"47","author":"Sethuraman","year":"2023","journal-title":"Comput. Sci. Rev."},{"key":"ref_126","unstructured":"(2025, June 30). Malware Statistics from the AV-TEST Institute. Available online: https:\/\/www.av-test.org\/en\/statistics\/malware."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","article-title":"Toward developing a systematic approach to generate benchmark datasets for intrusion detection","volume":"31","author":"Shiravi","year":"2012","journal-title":"Comput. Secur."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Glasser, J., and Lindauer, B. (2013). Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data. Proceedings of the 2013 IEEE Security and Privacy Workshops, IEEE.","DOI":"10.1109\/SPW.2013.37"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), IEEE.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021). Bottleneck Transformers for Visual Recognition. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_131","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_132","first-page":"446","article-title":"A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms","volume":"4","author":"Dhanabal","year":"2015","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, IEEE.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Habibi Lashkari, A., and Ghorbani, A.A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy-ICISSP, SciTePress.","DOI":"10.5220\/0006639801080116"},{"key":"ref_135","unstructured":"Ring, M., Wunderlich, S., Gr\u00fcdl, D., Landes, D., and Hotho, A. (2017). Flow-based benchmark data sets for intrusion detection. Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS), ACM."},{"key":"ref_136","unstructured":"Wang, L., Segal, M., Chen, J., and Qiu, T. (2022). A Novel Self-supervised Few-shot Network Intrusion Detection Method. Proceedings of the Wireless Algorithms, Systems, and Applications, Springer."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"12499","DOI":"10.1007\/s00521-020-04708-x","article-title":"An efficient XGBoost\u2013DNN-based classification model for network intrusion detection system","volume":"32","author":"Devan","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_138","unstructured":"Lotfi, S., Modirrousta, M., Shashaani, S., and Shoorehdeli, M.A. (2023). Network Intrusion Detection with Limited Labeled Data Using Self-supervision. arXiv."},{"key":"ref_139","unstructured":"Lu, W., Zhang, Y., Wen, W., Yan, H., and Li, C. (2022). A Self-supervised Adversarial Learning Approach for Network Intrusion Detection System. Proceedings of the Cyber Security, Springer."},{"key":"ref_140","unstructured":"(2025, May 26). CSE-CIC-IDS2018 Dataset. Available online: https:\/\/registry.opendata.aws\/cse-cic-ids2018\/."},{"key":"ref_141","first-page":"17081","article-title":"Contrastive Learning with Adversarial Examples","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11036-021-01843-0","article-title":"Towards a Standard Feature Set for Network Intrusion Detection System Datasets","volume":"27","author":"Sarhan","year":"2022","journal-title":"Mob. Netw. Appl."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/2133360.2133363","article-title":"Isolation-Based Anomaly Detection","volume":"6","author":"Liu","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"04018048","DOI":"10.1061\/(ASCE)WR.1943-5452.0000969","article-title":"Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks","volume":"144","author":"Taormina","year":"2018","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"110030","DOI":"10.1016\/j.knosys.2022.110030","article-title":"Anomal-E: A self-supervised network intrusion detection system based on graph neural networks","volume":"258","author":"Caville","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Lo, W.W., Layeghy, S., Sarhan, M., Gallagher, M., and Portmann, M. (2022). E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT. Proceedings of the NOMS 2022-2022 IEEE\/IFIP Network Operations and Management Symposium, IEEE.","DOI":"10.1109\/NOMS54207.2022.9789878"},{"key":"ref_147","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., and Hjelm, R.D. (2018). Deep Graph Infomax. arXiv."},{"key":"ref_148","unstructured":"Chen, X., Fan, H., Girshick, R., and He, K. (2020). Improved Baselines with Momentum Contrastive Learning. arXiv."},{"key":"ref_149","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."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Ayesha S., D., A.B., S., and D., M. (2023). FS3: Few-Shot and Self-Supervised Framework for Efficient Intrusion Detection in Internet of Things Networks. Proceedings of the 39th Annual Computer Security Applications Conference, ACM.","DOI":"10.1145\/3627106.3627193"},{"key":"ref_151","unstructured":"(2025, May 26). Pytorch-Widedeep. Available online: https:\/\/github.com\/jrzaurin\/pytorch-widedeep."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"106576","DOI":"10.1109\/ACCESS.2020.3000421","article-title":"Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study","volume":"8","author":"Hady","year":"2020","journal-title":"IEEE Access"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"6822","DOI":"10.1109\/JIOT.2019.2912022","article-title":"Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things","volume":"6","author":"Zolanvari","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"7454","DOI":"10.1109\/TITS.2024.3351438","article-title":"Anomaly Detection for In-Vehicle Network Using Self-Supervised Learning With Vehicle-Cloud Collaboration Update","volume":"25","author":"Cao","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_155","first-page":"100198","article-title":"In-vehicle network intrusion detection using deep convolutional neural network","volume":"21","author":"Song","year":"2020","journal-title":"Veh. Commun."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"5081","DOI":"10.1109\/TITS.2020.3046974","article-title":"CAN-Bus Attack Detection With Deep Learning","volume":"22","author":"Amato","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"7677","DOI":"10.1109\/JIOT.2024.3518636","article-title":"Hybrid Transfer and Self-Supervised Learning Approaches in Neural Networks for Intelligent Vehicle Intrusion Detection and Analysis","volume":"12","author":"Zhang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Verma, M.E., Bridges, R.A., Iannacone, M.D., Hollifield, S.C., Moriano, P., Hespeler, S.C., Kay, B., and Combs, F.L. (2024). A comprehensive guide to CAN IDS data and introduction of the ROAD dataset. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0296879"},{"key":"ref_159","first-page":"98","article-title":"Host-Based Intrusion Detection System with System Calls: Review and Future Trends","volume":"51","author":"Liu","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1109\/OJCOMS.2026.3667851","article-title":"Extending Memory-Based Obfuscated Malware Detection With Network Behavior","volume":"7","author":"Mercado","year":"2026","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_161","unstructured":"(2025, May 26). LID-DS (Leipzig Intrusion Detection-Data Set). Available online: https:\/\/github.com\/LID-DS\/LID-DS."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"76614","DOI":"10.1109\/ACCESS.2021.3082160","article-title":"Host-Based Intrusion Detection Model Using Siamese Network","volume":"9","author":"Park","year":"2021","journal-title":"IEEE Access"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Ji, I.H., Lee, J.H., Kang, M.J., Park, W.J., Jeon, S.H., and Seo, J.T. (2024). Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review. Sensors, 24.","DOI":"10.3390\/s24030898"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"100299","DOI":"10.1016\/j.hcc.2025.100299","article-title":"Reinforcement learning for an efficient and effective malware investigation during cyber incident response","volume":"5","author":"Dunsin","year":"2025","journal-title":"High Confid. Comput."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Dener, M., Ok, G., and Orman, A. (2022). Malware Detection Using Memory Analysis Data in Big Data Environment. Appl. Sci., 12.","DOI":"10.3390\/app12178604"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Beyah, R., Chang, B., Li, Y., and Zhu, S. (2018). Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild. Proceedings of the Security and Privacy in Communication Networks, Springer.","DOI":"10.1007\/978-3-030-01704-0"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42400-019-0043-x","article-title":"An emerging threat Fileless malware: A survey and research challenges","volume":"3","author":"Sudhakar","year":"2020","journal-title":"Cybersecurity"},{"key":"ref_168","unstructured":"(2025, April 28). VirusTotal. Available online: https:\/\/www.virustotal.com."},{"key":"ref_169","unstructured":"Tisf Nativ, Y., and Shalev, S. (2025, April 28). TheZoo\u2014A Live Malware Repository. Available online: https:\/\/github.com\/ytisf\/theZoo."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ins.2019.05.035","article-title":"A survey on fake news and rumour detection techniques","volume":"497","author":"Bondielli","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_171","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Fighting Fake News: Image Splice Detection via Learned Self-Consistency. Proceedings of the Computer Vision\u2014ECCV 2018, Springer."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Agrawal, S., Kumar, P., Seth, S., Parag, T., Singh, M., and Babu, V. (2022). SISL:Self-Supervised Image Signature Learning for Splicing Detection & Localization. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW56347.2022.00012"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Manolache, A., Brad, F., and Burceanu, E. (2021). DATE: Detecting Anomalies in Text via Self-Supervision of Transformers. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL.","DOI":"10.18653\/v1\/2021.naacl-main.25"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TNNLS.2021.3068344","article-title":"Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning","volume":"33","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"12220","DOI":"10.1109\/TKDE.2021.3119326","article-title":"Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection","volume":"35","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., and Chen, H. (2021). Decoupling Representation Learning and Classification for GNN-based Anomaly Detection. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM.","DOI":"10.1145\/3404835.3462944"},{"key":"ref_177","unstructured":"Kipf, T.N., and Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"125216","DOI":"10.1016\/j.eswa.2024.125216","article-title":"FIAD: Graph anomaly detection framework based feature injection","volume":"259","author":"Chen","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, S., and Chen, S. (2022, January 23\u201329). Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria.","DOI":"10.24963\/ijcai.2022\/330"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.future.2015.01.001","article-title":"A survey of anomaly detection techniques in financial domain","volume":"55","author":"Ahmed","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Wang, C., Dou, Y., Chen, M., Chen, J., Liu, Z., and Yu, P.S. (2021). Deep Fraud Detection on Non-attributed Graph. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), IEEE.","DOI":"10.1109\/BigData52589.2021.9672028"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Schreyer, M., Sattarov, T., and Borth, D. (2022, January 3\u20135). Multi-View Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks. Proceedings of the Second ACM International Conference on AI in Finance, New York, NY, USA.","DOI":"10.1145\/3490354.3494373"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1145\/3641283","article-title":"Credit Card Fraud Detection via Intelligent Sampling and Self-supervised Learning","volume":"15","author":"Chen","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Moschini, G., Houssou, R., Bovay, J., and Robert-Nicoud, S. (2021). Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model. Eng. Proc., 5.","DOI":"10.3390\/engproc2021005056"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Jiang, S., Dong, R., Wang, J., and Xia, M. (2023). Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network. Systems, 11.","DOI":"10.3390\/systems11060305"},{"key":"ref_186","unstructured":"Grover, P., Xu, J., Tittelfitz, J., Cheng, A., Li, Z., Zablocki, J., Liu, J., and Zhou, H. (2023). Fraud Dataset Benchmark and Applications. arXiv."},{"key":"ref_187","unstructured":"(2024, August 12). Addison Howard, Bernadette Bouchon-Meunier, IEEE CIS, inversion, John Lei, Lynn@Vesta, Marcus2010, and Prof. Hussein Abbass. IEEE-CIS Fraud Detection. Available online: https:\/\/kaggle.com\/competitions\/ieee-fraud-detection."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/OJCS.2025.3570600","article-title":"Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning","volume":"6","author":"Botero","year":"2025","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_189","first-page":"18853","article-title":"SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning","volume":"Volume 34","author":"Ranzato","year":"2021","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Chehri, A., Fofana, I., and Yang, X. (2021). Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence. Sustainability, 13.","DOI":"10.3390\/su13063196"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"7193","DOI":"10.1109\/ACCESS.2020.3047139","article-title":"Regional Smart City Development Focus: The South Korean National Strategic Smart City Program","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"68319","DOI":"10.1109\/ACCESS.2022.3184710","article-title":"A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities","volume":"10","author":"Singh","year":"2022","journal-title":"IEEE Access"},{"key":"ref_193","unstructured":"Zeng, H., Zhou, P., Lou, X., Ng, Z.W., Yau, D.K.Y., and Winslett, M. (2024). Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids. arXiv."},{"key":"ref_194","first-page":"14756","article-title":"Lightweight Encryption and Authentication for Controller Area Network of Autonomous Vehicles","volume":"72","author":"Cui","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"104195","DOI":"10.1016\/j.trc.2023.104195","article-title":"Forecasting passenger flows and headway at train level for a public transport line: Focus on atypical situations","volume":"153","author":"Bapaume","year":"2023","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Almaraz-Rivera, J.G., Cantoral-Ceballos, J.A., Botero, J.F., Mu\u00f1oz, F.J., and Martinez, B.D. (2025). A Multimodal Learning Approach for Protecting the Metro System of Medellin Colombia Against Corrupted User Traffic Data. Smart Cities, 8.","DOI":"10.3390\/smartcities8060198"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"104031","DOI":"10.1016\/j.jnca.2024.104031","article-title":"Security risks and countermeasures of adversarial attacks on AI-driven applications in 6G networks: A survey","volume":"232","author":"Hoang","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"4622","DOI":"10.1109\/TITS.2020.3036085","article-title":"Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach","volume":"22","author":"Yu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_199","unstructured":"(2025, December 08). ECML\/PKDD 15: Taxi Trajectory Prediction (I). Available online: https:\/\/kaggle.com\/competitions\/pkdd-15-predict-taxi-service-trajectory-i."},{"key":"ref_200","unstructured":"Zheng, Y. (2025, December 08). T-Drive Trajectory Data Sample. Available online: https:\/\/www.microsoft.com\/en-us\/research\/publication\/t-drive-trajectory-data-sample\/."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1109\/TMC.2013.27","article-title":"Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset","volume":"13","author":"Uppoor","year":"2014","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"102964","DOI":"10.1016\/j.cose.2022.102964","article-title":"GramBeddings: A New Neural Network for URL Based Identification of Phishing Web Pages Through N-gram Embeddings","volume":"124","author":"Bozkir","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"102638","DOI":"10.1016\/j.inffus.2024.102638","article-title":"PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention network","volume":"113","author":"Liu","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Ma, W., Cui, Y., Si, C., Liu, T., Wang, S., and Hu, G. (2020, January 8\u201313). CharBERT: Character-aware Pre-trained Language Model. Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.4"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"106304","DOI":"10.1016\/j.dib.2020.106304","article-title":"Malicious and Benign Webpages Dataset","volume":"32","author":"Singh","year":"2020","journal-title":"Data Brief"},{"key":"ref_206","unstructured":"Siddhartha, M. (2026, February 09). Malicious URLs Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/sid321axn\/malicious-urls-dataset."},{"key":"ref_207","unstructured":"Malibari, S. (2026, February 09). Benign and Malicious URLs. Available online: https:\/\/www.kaggle.com\/datasets\/samahsadiq\/benign-and-malicious-urls."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Hu, H., and Pang, J. (2021). Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks. Proceedings of the 37th Annual Computer Security Applications Conference, ACM.","DOI":"10.1145\/3485832.3485838"},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Saha, A., Tejankar, A., Koohpayegani, S.A., and Pirsiavash, H. (2022). Backdoor Attacks on Self-Supervised Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01298"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"102303","DOI":"10.1016\/j.inffus.2024.102303","article-title":"Adversarial attacks and defenses in explainable artificial intelligence: A survey","volume":"107","author":"Baniecki","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_211","unstructured":"Kowalczuk, A., Dubi\u0144ski, J., Ghomi, A.A., Sui, Y., Stein, G., Wu, J., Cresswell, J.C., Boenisch, F., and Dziedzic, A. (2024). Benchmarking robust self-supervised learning across diverse downstream tasks. arXiv."},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Silva, H.P., Becattini, F., and Seidenari, L. (2025). Attacking Attention of Foundation Models Disrupts Downstream Tasks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, IEEE.","DOI":"10.1109\/CVPRW67362.2025.00338"},{"key":"ref_213","first-page":"5757","article-title":"On the Difficulty of Defending Self-Supervised Learning against Model Extraction","volume":"Volume 162","author":"Chaudhuri","year":"2022","journal-title":"Proceedings of the 39th International Conference on Machine Learning; PMLR"},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"131762","DOI":"10.1109\/ACCESS.2024.3440647","article-title":"A Systematic Literature Review on AI Safety: Identifying Trends, Challenges, and Future Directions","volume":"12","author":"Salhab","year":"2024","journal-title":"IEEE Access"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1145\/3555803","article-title":"Trustworthy AI: From Principles to Practices","volume":"55","author":"Li","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_216","doi-asserted-by":"crossref","unstructured":"Feldman, V. (2020). Does learning require memorization? a short tale about a long tail. Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, ACM.","DOI":"10.1145\/3357713.3384290"},{"key":"ref_217","first-page":"60475","article-title":"Localizing Memorization in SSL Vision Encoders","volume":"Volume 37","author":"Globerson","year":"2024","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_218","first-page":"11033","article-title":"VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_219","first-page":"507","article-title":"Why do tree-based models still outperform deep learning on typical tabular data?","volume":"Volume 35","author":"Koyejo","year":"2022","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","article-title":"Tabular data: Deep learning is not all you need","volume":"81","author":"Armon","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Sahoo, D., Pham, Q., Lu, J., and Hoi, S.C.H. (2018). Online Deep Learning: Learning Deep Neural Networks on the Fly. Proceedings of the 27th International Joint Conference on Artificial Intelligence, AAAI Press.","DOI":"10.24963\/ijcai.2018\/369"},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TLA.2023.10068850","article-title":"An Anomaly-based Detection System for Monitoring Kubernetes Infrastructures","volume":"21","year":"2023","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"126640","DOI":"10.1016\/j.neucom.2023.126640","article-title":"Model-based explanations of concept drift","volume":"555","author":"Hinder","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_224","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":"ref_225","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1109\/OJCOMS.2024.3396077","article-title":"Resource-Efficient Spectrum-Based Traffic Classification on Constrained Devices","volume":"5","author":"Fletscher","year":"2024","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"8470","DOI":"10.1109\/TNNLS.2022.3229897","article-title":"Tiny Machine Learning for Concept Drift","volume":"35","author":"Disabato","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"75546","DOI":"10.1109\/ACCESS.2025.3564834","article-title":"Self-Supervised Pretraining and Quantization for Fault Tolerant Neural Networks: Friend or Foe?","volume":"13","author":"Milazzo","year":"2025","journal-title":"IEEE Access"},{"key":"ref_228","doi-asserted-by":"crossref","unstructured":"Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., and Hassner, T. (2022). Synergistic Self-supervised and Quantization Learning. Proceedings of the Computer Vision\u2014ECCV 2022, Springer.","DOI":"10.1007\/978-3-031-20068-7"},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11831-023-09884-2","article-title":"Self-supervised Learning: A Succinct Review","volume":"30","author":"Rani","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_230","unstructured":"Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., and Tian, Y. (2023). A Cookbook of Self-Supervised Learning. arXiv."},{"key":"ref_231","unstructured":"El-Nouby, A., Izacard, G., Touvron, H., Laptev, I., Jegou, H., and Grave, E. (2021). Are Large-scale Datasets Necessary for Self-Supervised Pre-training?. arXiv."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1038\/s41551-022-00914-1","article-title":"Self-supervised learning in medicine and healthcare","volume":"6","author":"Krishnan","year":"2022","journal-title":"Nat. Biomed. Eng."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/5\/121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:17:14Z","timestamp":1778818634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/5\/121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,1]]},"references-count":232,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["make8050121"],"URL":"https:\/\/doi.org\/10.3390\/make8050121","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,1]]}}}