{"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/gee-a-gradient-based-explainable-variational","title":"GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection","arxiv_id":"1903.06661","date":"2019-03-15","proceeding":null,"authors":["Quoc Phong Nguyen","Kar Wai Lim","Dinil Mon Divakaran","Kian Hsiang Low","Mun Choon Chan"],"abstract":"This paper looks into the problem of detecting network anomalies by analyzing\nNetFlow records. While many previous works have used statistical models and\nmachine learning techniques in a supervised way, such solutions have the\nlimitations that they require large amount of labeled data for training and are\nunlikely to detect zero-day attacks. Existing anomaly detection solutions also\ndo not provide an easy way to explain or identify attacks in the anomalous\ntraffic. To address these limitations, we develop and present GEE, a framework\nfor detecting and explaining anomalies in network traffic. GEE comprises of two\ncomponents: (i) Variational Autoencoder (VAE) - an unsupervised deep-learning\ntechnique for detecting anomalies, and (ii) a gradient-based fingerprinting\ntechnique for explaining anomalies. Evaluation of GEE on the recent UGR dataset\ndemonstrates that our approach is effective in detecting different anomalies as\nwell as identifying fingerprints that are good representations of these various\nattacks.","url_abs":"http://arxiv.org/abs/1903.06661v1","url_pdf":"http://arxiv.org/pdf/1903.06661v1.pdf","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":"gee-a-gradient-based-explainable-variational","repo_url":"https://github.com/mhwong2007/GEE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}