{"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/n-baiot-network-based-detection-of-iot-botnet","title":"N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders","arxiv_id":"1805.03409","date":"2018-05-09","proceeding":null,"authors":["Yair Meidan","Michael Bohadana","Yael Mathov","Yisroel Mirsky","Dominik Breitenbacher","Asaf Shabtai","Yuval Elovici"],"abstract":"The proliferation of IoT devices which can be more easily compromised than\ndesktop computers has led to an increase in the occurrence of IoT based botnet\nattacks. In order to mitigate this new threat there is a need to develop new\nmethods for detecting attacks launched from compromised IoT devices and\ndifferentiate between hour and millisecond long IoTbased attacks. In this paper\nwe propose and empirically evaluate a novel network based anomaly detection\nmethod which extracts behavior snapshots of the network and uses deep\nautoencoders to detect anomalous network traffic emanating from compromised IoT\ndevices. To evaluate our method, we infected nine commercial IoT devices in our\nlab with two of the most widely known IoT based botnets, Mirai and BASHLITE.\nOur evaluation results demonstrated our proposed method's ability to accurately\nand instantly detect the attacks as they were being launched from the\ncompromised IoT devices which were part of a botnet.","url_abs":"http://arxiv.org/abs/1805.03409v1","url_pdf":"http://arxiv.org/pdf/1805.03409v1.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":"n-baiot-network-based-detection-of-iot-botnet","repo_url":"https://github.com/sergts/botnet-traffic-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"n-baiot-network-based-detection-of-iot-botnet","repo_url":"https://github.com/hussein-ha1601589/N-BaIoT-reloaded","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}