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http://dx.doi.org/10.9717/kmms.2013.16.6.678

Implementation Of DDoS Botnet Detection System On Local Area Network  

Huh, Jun-Ho (부경대학교 컴퓨터공학과)
Hong, Myeong-Ho (부경대학교 컴퓨터멀티미디어공학 전공)
Lee, JeongMin (부경대학교 컴퓨터멀티미디어공학 전공)
Seo, Kyungryong (부경대학교 컴퓨터공학과)
Publication Information
Abstract
Different Different from a single attack, in DDoS Attacks, the botnets that are distributed on network initiate attacks against the target server simultaneously. In such cases, it is difficult to take an action while denying the access of packets that are regarded as DDoS since normal user's convenience should also be considered at the target server. Taking these considerations into account, the DDoS botnet detection system that can reduce the strain on the target server by detecting DDoS attacks on each user network basis, and then lets the network administrator to take actions that reduce overall scale of botnets, has been implemented in this study. The DDoS botnet detection system proposed by this study implemented the program which detects attacks based on the database composed of faults and abnormalities collected through analyzation of hourly attack traffics. The presence of attack was then determined using the threshold of current traffic calculated with the standard deviation and the mean number of packets. By converting botnet-based detection method centering around the servers that become the targets of attacks to the network based detection, it was possible to contemplate aggressive defense concept against DDoS attacks. With such measure, the network administrator can cut large scale traffics of which could be referred as the differences between DDoS and DoS attacks, in advance mitigating the scale of botnets. Furthermore, we expect to have an effect that can considerably reduce the strain imposed on the target servers and the network loads of routers in WAN communications if the traffic attacks can be blocked beforehand in the network communications under the router equipment level.
Keywords
DDoS; Botnet; SYN Flooding; Botnet Detection System;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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