Anomalous Network Traffic Detection: An Application of Deep Learning Algorithms

With the development of the electric power system, the safety of electric power industrial control network is increasingly prominent.Abnormal traffic detection is one of the important tasks in network security.To detect anomalous traffic quickly and accurately, the sine-cosine function and Levy flight are introduced into the locust algorithm to optimize its reduction factors.The improved locust algorithm is used to optimize the parameters of the anomalous network traffic model built on the basis of long short-term memory networks.Meanwhile, the optimized model is applied to the electric power industrial control system.The results showed that the accuracy of both the test and training sets was above 90%, proving that the designed model achieved good generalization capability and robustness.In the Dos attack type, the detection rate of the designed model reaches 98.15%, which is about 6% and 10% higher than that of other algorithms, which proves its high accuracy.These results prove the high efficiency and accuracy of the designed model in detecting network traffic attacks.The above results prove that the designed model can effectively identify anomalous flow and contribute to maintaining the safety of the power grid.