Network Vulnerability Detection Using Ant Colony Optimization

Abstract

Security of the information in the computer networks has been one of the most important Research Area. To preserves the secure condition it is essential to be aware of the behavior of the incoming data. Is it a normal or abnormal data? It is a too vulnerable and complicated Question. Owing to the fact that intrusive data are in several and similar forms, distinguishing them from the normal ones is so astounding. Network Security is becoming an important issue for all the organizations, and with the increase in knowledge of hackers and intruders they have made many successful attempts to bring down high-pro le company networks and web services. Ant-colony optimization algorithm is an evolutionary learning algorithm which could be applied to solve the complex problems. ACO algorithm fundamental idea has been inspired by the behavior of the real ants. Ants deposit pheromone as a trace to direct the other ones in nding foods. They choose their path according to the congestion of the pheromone. The above behavior of the real ants has inspired an algorithm which a set of arti cial ants, as a group of simple agents, cooperate with each other to solve a problem by exchanging information via pheromone deposited on the edges of the graph. One of the most surprising behavioral patterns exhibited by ants is the ability of certain ant species to nd what computer scientists call shortest paths. Biologists have shown experimentally that this is possible by exploiting communication based only on pheromones.