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    Please use this identifier to cite or link to this item: http://140.128.103.80:8080/handle/310901/21755


    Title: Detection workload in a dynamic grid-based intrusion detection environment
    Authors: Leu, F.-Y., Li, M.-C., Lin, J.-C., Yang, C.-T.
    Keywords: DDoS;Detector;DoS;Fault tolerance;Intrusion detection system
    Date: 2008-04
    Issue Date: 2013-05-15T09:05:29Z (UTC)
    Publisher: Department of Computer Science, Tunghai University
    Abstract: Denial-of-service (DoS) and distributed denial-of-service (DDoS) are two of the most serious and destructive network threats on the Internet. Hackers, exploiting all kinds of malicious packages to attack and usurp network hosts, servers and bandwidth, have seriously damaged enterprise, campus and government network systems. Many network administrators employ intrusion detection systems (IDSs) and/or firewalls to protect their systems. However, some systems lose most of their detection and/or protection capabilities when encountering a huge volume of attack packets. In addition, some detection resources may fail due to hardware and/or software faults. In this paper, we propose a Grid-based platform, named the dynamic grid-based intrusion detection environment (DGIDE), which exploits Grid's abundant computing resources to detect a massive amount of intrusion packets and to manage a dynamic environment. A detector, a node that detects attacks, can dynamically join or leave the DGIDE. A newly joined detector is tested so that we can obtain its key performance curves, which are used to balance detection workload among detectors. The DGIDE backs up network packets. When, for some reason, a detector cannot continue its detection thus leaving an unfinished detection task, the DGIDE allocates another available detector to take over. Therefore, the drawbacks of ordinary security systems as mentioned above can be avoided. ? 2007 Elsevier Inc. All rights reserved.
    Relation: Journal of Parallel and Distributed Computing 68 (4) , pp. 427-442
    Appears in Collections:[Department of Computer Science and Information ] Periodical Articles

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