{"title":"异常网络入侵检测系统的深度强化学习方法","authors":"Ying-Feng Hsu, Morito Matsuoka","doi":"10.1109/CloudNet51028.2020.9335796","DOIUrl":null,"url":null,"abstract":"Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In this paper, we propose a deep reinforcement learning-based (DRL) for anomaly network intrusion detection system. Our system has the ability of self-updating to reflect new types of network traffic behavior. This study includes three major contributions. First, to show the overall applicability of our approach, we demonstrate our work through two well-known NIDS benchmark datasets: NSL-KDD and UNSW-NB15 and a real campus network log. Second, to show the feasibility of our approach, we compared our method with three other classic machine learning methods and two related published results. Third, our model is capable of processing a million scale of network traffic on a real-time basis.","PeriodicalId":156419,"journal":{"name":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","volume":"37 15","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System\",\"authors\":\"Ying-Feng Hsu, Morito Matsuoka\",\"doi\":\"10.1109/CloudNet51028.2020.9335796\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In this paper, we propose a deep reinforcement learning-based (DRL) for anomaly network intrusion detection system. Our system has the ability of self-updating to reflect new types of network traffic behavior. This study includes three major contributions. First, to show the overall applicability of our approach, we demonstrate our work through two well-known NIDS benchmark datasets: NSL-KDD and UNSW-NB15 and a real campus network log. Second, to show the feasibility of our approach, we compared our method with three other classic machine learning methods and two related published results. Third, our model is capable of processing a million scale of network traffic on a real-time basis.\",\"PeriodicalId\":156419,\"journal\":{\"name\":\"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)\",\"volume\":\"37 15\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CloudNet51028.2020.9335796\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 9th International Conference on Cloud Networking (CloudNet)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CloudNet51028.2020.9335796","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System
Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In this paper, we propose a deep reinforcement learning-based (DRL) for anomaly network intrusion detection system. Our system has the ability of self-updating to reflect new types of network traffic behavior. This study includes three major contributions. First, to show the overall applicability of our approach, we demonstrate our work through two well-known NIDS benchmark datasets: NSL-KDD and UNSW-NB15 and a real campus network log. Second, to show the feasibility of our approach, we compared our method with three other classic machine learning methods and two related published results. Third, our model is capable of processing a million scale of network traffic on a real-time basis.