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The key objective of a Distributed Denial of Service (DDoS) attack is to compile multiple systems across the Internet with infected zombies/agents and form botnets of networks. Such zombies are designed to attack a particular target or network with different types of packets. The infected systems are remotely controlled either by an attacker or by self-installed Trojans (e.g. roj/Flood-IM) that are programmed to launch packet floods. Within this context, the purpose of this paper is to detect and mitigate known and unknown DDoS attacks in real time environments. We have chosen an Artificial Neural Network (ANN) algorithm to detect DDoS attacks based on specific characteristic features (patterns) that separate DDoS attack traffic from genuine traffic. [ABSTRACT FROM AUTHOR]

A black hole is a highly harmful stationary process residing in a node of a network and destroying all mobile agents visiting the node without leaving any trace. The Black Hole Search is the task of locating all black holes in a network by exploring it with mobile agents. We consider the problem of designing the fastest Black Hole Search, given the map of the network and the starting node. We study the version of this problem that assumes that there is at most one black hole in the network and there are two agents, which move in synchronized steps. We prove that this problem is NP-hard in arbitrary graphs (even in planar graphs), solving an open problem stated in [J. Czyzowicz, D. Kowalski, E. Markou, A. Pelc, Searching for a black hole in tree networks, in: Proc. 8th Int. Conf. on Principles of Distributed Systems, OPODIS 2004, 2004, pp. 34–35. Also: Springer LNCS, vol. 3544, pp. 67–80]. We also give a -approximation algorithm, showing the first non-trivial approximation ratio upper bound for this problem. Our algorithm follows a natural approach of exploring networks via spanning trees. We prove that this approach cannot lead to an approximation ratio bound better than 3/2. [Copyright &y& Elsevier]