Simulation of Unknown Worm Detection in Network Communication Based on Greedy Algorithm
Network worms are intelligent and have comprehensive network attacks.They can run attack programs or codes without the intervention of computer users,and the attack spreads quickly.In this paper,a method of detec-ting unknown worms in network communication based on greedy algorithm was put forward.Firstly,a worm propagation model was built based on cloud security environment,for extracting unknown worm data features.Second-ly,a greedy algorithm was adopted to build autoencoder,thus reducing worm data features.Then,an improved ant col-ony algorithm and a SVM were used to construct a model to detect network attacks.Finally,the worm data features af-ter dimension reduction were input into the model,thus completing the detection of unknown worms.The experimental results show that the worm detection rate of the proposed method is higher,and the packet loss rate is lower than 0.5%.in addition,the decrease of host infection rate indicates that the application performance of the method is better.
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