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  • 标题:Defense against SYN-Flood Denial of Service Attacks Based on Learning Automata
  • 本地全文:下载
  • 作者:Masoud Bekravi ; Shahram Jamali ; Gholam Shaker
  • 期刊名称:International Journal of Computer Science Issues
  • 印刷版ISSN:1694-0784
  • 电子版ISSN:1694-0814
  • 出版年度:2012
  • 卷号:9
  • 期号:3
  • 出版社:IJCSI Press
  • 摘要:SYN-flooding attack uses the weakness available in TCPs three-way handshake process to keep it from handling legitimate requests. This attack causes the victim host to populate its backlog queue with forged TCP connections. In other words it increases Ploss (probability of loss) and Pa (buffer occupancy percentage of attack requests) and decreases Pr (buffer occupancy percentage of regular requests) in the victim host and results to decreased performance of the host. This paper proposes a self-managing approach, in which the host defends against SYN-flooding attack by dynamically tuning of its own two parameters, that is, m (maximum number of half-open connections) and h (hold time for each half-open connection). In this way, it formulates the defense problem to an optimization problem and then employs the learning automata (LA) algorithm to solve it. The simulation results show that the proposed defense strategy improves performance of the under attack system in terms of Ploss, Pa and Pr.
  • 关键词:SYN;flooding; DoS; TCP; Learning automata; queuing model.
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