A Scheme of Anomalous Detection Based on Reinforcement Learning for Load Balancing
作者单位：School of GamesHongik University
会议日期：2019年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2019R1A2C1008533) Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT&Future Planning(No.2016RIA2B4012386) supported by 2019 Hongik University Research Fund
摘 要：In recent,both researchers and developers have great interests in anomalous ***,it is still difficult to implement a uniform framework for anomalous ***,the network anomalous detection using deep learning methods has been discussed with potential limitations and *** anomalous detection in wireless or wired network is extremely important because it is caused by flood traffic of network and *** of malicious network loads are defined,while anomalous detections is more suitable for detecting normal and anomalous network loads by means of deep *** important goal of these issues is to recognize the anomalous detections for better preparation against future load balancing of *** this paper,we propose an agent Detectbot that processes anomalous detection for load balancing based on reinforcement *** simulation results show that the reinforcement learning scheme is effective for anomalous detection in load balancing.