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Adaptive techniques based on machine learning and data mining are gaining relevance in selfmanagement and self-defense for networks and distributed systems. In this paper, we focus on early detection and stopping of distributed flooding attacks and network abuses. We extend the framework proposed by Zhang and Parashar (2006) to cooperatively detect and react to abnormal behaviors before the target machine collapses and network performance degrades. In this framework, nodes in an intermediatedoi:10.1145/1456377.1456389 dblp:conf/ccs/BerralPAGTP08 fatcat:mfzxcklxfbg4lapx7y7hsdcswy