Message anomaly detection method based on deep learning

Abstract

The invention discloses a message anomaly detection method based on deep learning. According to the method, firstly, data is preprocessed into a two-dimensional matrix form, and in order to reduce anoverfitting phenomenon easily occurring in a general algorithm model, a shuffle function is used for randomly disturbing the data to prevent local optimum, so that the model is easier to converge; then effective features are learned from the preprocessed data by using a convolutional neural network (CNN), and finally classification processing is performed on the obtained vectors through a supportvector machine (SVM) classifier. According to the method, the advantage of high-dimensional learning of the convolutional neural network of deep learning is utilized to learn the effective features ofthe network transmission message; the model provided by the invention is subjected to experimental verification on a Beijing University data set and is subjected to experimental comparison with othertwo models with better effects verified on the data set, experiments prove that the accuracy and the stability are greatly improved, and the training and testing time consumption is obviously reduced.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses advanced techniques such as Deep Learning (DL) or Support Vector Machine(SVM). These methods can efficiently learn complex patterns from massive amounts of data without being affected by any factors like noise levels or spikes during training time. They also have excellent performance even at very fast speeds compared to traditional algorithms due to their ability to handle both regular and irregular signals simultaneously.

Problems solved by technology

Technological Problem addressed in this patents relates to improving the accuracy and efficiency of identifying novel cyberattacks by analyzing messages that contain different characteristics or patterns associated with them.

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