Internet worms and its detection

A worm is a standalone program, which is self-replicating malware that distributes itself to other computers and networks. An Internet worm can spread across the network and infect millions of computers in very little time. Damages caused by such attacks are considered very high. Worms affect the network packet and its performance. In this paper, different methods are analyzed to detect Internet worms. Algorithms like Bayesian Network, Back Propagation Neural Network etc., were used to develop some detection techniques. These techniques led to a better understanding of the spreading of worms and are also successful in identifying the internet worms. They were found to be effective in preventing network attacks with high accuracy. The proposed techniques are low cost and provide counter measures to control the spread of internet worms.