An Abnormal Traffic Detection Method Based on Grayscale Image

Abstract

The invention discloses a method for detecting abnormal traffic based on a grayscale image, which includes the following steps: S1: performing visual processing on the original traffic of the network, and converting the original traffic into a grayscale image; S2: performing feature extraction on the grayscale image; S3: Based on the Apache Spark framework, use the distributed extreme learning machine to train the features of the grayscale image, output the weight matrix β, obtain the training parameters, and complete the abnormal traffic detection of the grayscale image. The present invention effectively solves the problem of abnormal traffic detection in various network environments, solves the difficulty in extracting original traffic features through the visual processing of network traffic, converts the difficulty in extracting original traffic features into image feature extraction, and makes the training results more accurate higher. Moreover, the present invention effectively solves the problem of anomaly detection of high-speed and massive network traffic in the era of big data, and adapts to the modern actual network environment.

Description

technical field [0001] The invention belongs to the technical field of network detection, and in particular relates to an abnormal flow detection method based on a grayscale image. Background technique [0002] In recent years, with the continuous development of Internet technology, people use the Internet more and more widely, the frequency and intensity of attacks in the network have been increasing, and the network environment has also deteriorated. Network attack refers to the attack on the hardware, software and data of the network system by using network loopholes and security defects. From the perspective of destructiveness to information, attack types can be divided into passive attack and active attack. Active attacks can lead to the tampering of certain data streams and the generation of false data streams. Passive attacks usually include eavesdropping, traffic analysis, and cracking of weakly encrypted data streams. In order to resist these attacks and improve ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of detecting network intrusion, and propose a method for detecting abnormal traffic based on grayscale images

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