{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/feature-based-transfer-learning-for-network","title":"Feature-Based Transfer Learning for Network Security","arxiv_id":null,"date":"2017-12-11","proceeding":"MILCOM 2017 12","authors":["Juan Zhao","Sachin Shetty","Jan Wei Pan"],"abstract":"New and unseen network attacks pose a great threat\r\nto the signature-based detection systems. Consequently, machine\r\nlearning-based approaches are designed to detect attacks, which\r\nrely on features extracted from network data. The problem is\r\ncaused by different distribution of features in the training and\r\ntesting datasets, which affects the performance of the learned\r\nmodels. Moreover, generating labeled datasets is very time-consuming and expensive, which undercuts the effectiveness of\r\nsupervised learning approaches. In this paper, we propose using\r\ntransfer learning to detect previously unseen attacks. The main\r\nidea is to learn the optimized representation to be invariant\r\nto the changes of attack behaviors from labeled training sets\r\nand non-labeled testing sets, which contain different types of\r\nattacks and feed the representation to a supervised classier.\r\nTo the best of our knowledge, this is the first effort to use\r\na feature-based transfer learning technique to detect unseen\r\nvariants of network attacks. Furthermore, this technique can be\r\nused with any common base classier. We evaluated the technique\r\non publicly available datasets, and the results demonstrate the\r\neffectiveness of transfer learning to detect new network attacks.","url_abs":"https://ieeexplore.ieee.org/document/8170749","url_pdf":"https://ieeexplore.ieee.org/document/8170749","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"feature-based-transfer-learning-for-network","repo_url":"https://github.com/zhaojuanwendy/transfer_learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}