GAN-based low-cost adversarial network attack sample generation method

Abstract

The invention discloses a GAN-based low-cost adversarial network attack sample generation method, comprising the steps: 1, encoding attributes and labels of samples in a sample set; 2, calculating aninformation gain according to the attribute of the coded sample; 3, training an adversarial sample generation model; and 4, generating an adversarial sample for attacks. Based on the basic thought ofGAN, the GAN-based low-cost adversarial network attack sample generation method realizes automatic selection of disturbance characteristics by calculating attributes, labels and information gain training models of samples, and can generate efficient adversarial network attack samples at the minimum cost.

Description

technical field [0001] The invention relates to the field of network security, in particular to a GAN-based low-cost adversarial network attack sample generation method. Background technique [0002] In recent years, artificial intelligence algorithms have been gradually applied in the field of network security, and have shown good performance in malware detection, intrusion detection, and vulnerability mining. However, because artificial intelligence algorithms are vulnerable to adversarial attacks, systems such as malware detection and intrusion detection are vulnerable to threats from malicious attackers. To this end, researchers have proposed methods to defend against adversarial attacks, among which methods based on generative adversarial network (GAN) are the majority, and have achieved good defense effects. However, most of these methods have the following three problems: [0003] 1. Most methods focus on how to use GAN to improve the accuracy of the detection algor...

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Problems solved by technology

[0003] 1. Most methods focus on how to use GAN to improve the accuracy of the detection algorithm, and do not pay attention to the ability of GAN-based adversarial attacks to network attack detection algorithms;

[0004] 2. Most of the methods are derived from the generation methods of adversarial samples for images. These methods do not take into account the differences between image samples and network data samples, that is, in network data, different attributes have different importance to classification results;

[0005] Third, these methods do not take into account the cost of generating adversarial samples

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