Published Paper Details:

There is significant climb in cyber attacks, a numerous organizational entities are presently making progress toward better information mining strategies to examine security logs that has acquired from the various software framework to guarantee avoiding the bot attacks based on the location. Machine Learning approach based security frameworks are evolving to detect the patterns of attack by extracting the payload data from the network resources. These uncover the threats that are targeted towards the operational infrastructure with minimizing the cost for detection of these attacks. This paper introduces PSO Algorithm for detecting bot attacks on a network infrastructure. PSO Particle swarm optimization is a rudimentary variant which operates by consuming a populace of network infrastructure (assumed as a swarm) of network data (assumed as particles). These network particles are simulated and propagated in the investigation computational coordinates of the network infrastructure domain. The movements of the network particles result in a upward trajectory pattern of upper boundary and its consequent downward pattern lower trajectory of lower boundary. These boundaries constitute of the network traffic movements and each packet movement in the network space, when the data of each packet crosses upward trajectory pattern which will constitute of the traffic deviations generally a bot attack. These patterns are trained and tested on the network infrastructure to efficient detection of the attack.