Distributed Detection of DDoS Attacks During the Intermediate Phase Through Mobile Agents

A Distributed Denial of Service attack is a large-scale, coordinated attack on the availability of services of a victim system, launched indirectly through many compromised computers on the Internet. Intrusion detection systems are network security tools that process local audit data or monitor network traffic to search for specific patterns or certain deviations from expected behavior, which indicate malicious activities against the protected network. In this study, we propose distributed intrusion detection methods to detect Distributed Denial of Service attacks in a special dataset and test these methods in a simulated-real time environment, in which the mobile agents are synchronized with the timestamp stated in the dataset. All of our methods use the alarms generated by SNORT, a signature-based network intrusion detection system. We use mobile agents in our methods on the Jade platform in order to reduce network bandwidth usage and to decrease the dependency on the central unit for a higher reliability. The methods are compared based on reliability, network load and mean detection time values

Commercial Regional Space/Airborne Imaging

In this work goal programming is used to solve a minimum cost multicommodity network flow problem with multiple goals. A single telecommunication network with multiple commodities (e.g., voice, video, data, etc.) flowing over it is analyzed. This network consists of: linear objective function, linear cost arcs, fixed capacities, specific origin-destination pairs for each commodity. A multicommodity network flow problem with goals can be successfully modeled using linear goal programming techniques. When properly modeled, network flow techniques may be employed to exploit the pure network structure of a multicommodity network flow problem with goals. Lagrangian relaxation captures the essence of the pure network flow problem as a master problem and sub-problems (McGinnis and Rao, 1977). A subgradient algorithm may optimize the Lagrangian function, or the Lagrangian relaxation could be decomposed into subproblems per commodity; each subproblem being a single commodity network flow problem. Parallel to the decomposition of the Lagrangian relaxation, Dantzig-Wolfe decomposition may be implemented to the linear program. Post-optimality analyses provide a variety of options to analyze the robustness of the optimal solution. The options of post-optimality analysis consist of sensitivity analysis and parametric analysis. This mix of modeling options and analyses provide a powerful method to produce insight into the modeling of a multicommodity network flow problem with multiple objectives