Abstract:

The existing network attack detection methods including static and dynamic types, and there are some shortcomings, such as too dependent on the rules, much false positives. In view of the traditional network attack detection, this paper introduces the convolution neural network technology into the field of network attack detection. In this paper, the basic principle of convolution neural network is explained in the related content of convolution neural network. In the subsequent chapters, this paper creatively maps the extracted log features to a set of gray scale images for anomaly detection, and creatively maps the network attack characteristics into a sheet of gray scale. This paper reads the application log in the large data platform every 10 minutes by Kafka, generates the latest signature library and maps it to the gray scale according to the corresponding characteristics of the local server, and can reduce the noise data by convolution operation. The original signal features are enhanced so that the features can better describe the details of the data and improve the ability to classify.

Key words: network security, network attack detection, convolution neural network