Browser sample set acquisition method based on deep learning and genetic algorithm

A deep learning and genetic algorithm technology, applied in the field of browser sample set acquisition, can solve problems such as inability to bypass protective measures, vulnerability mining sample set generation and optimization without general processes and frameworks, waste of computing resources, etc.

Inactive Publication Date: 2021-02-05
SICHUAN UNIV
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, this method also faces three problems. One is to perform specific optimization for known samples, and cannot universally mine specific types of vulnerabilities.
The second is that the protection measures in the target program cannot b...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Browser sample set acquisition method based on deep learning and genetic algorithm
  • Browser sample set acquisition method based on deep learning and genetic algorithm
  • Browser sample set acquisition method based on deep learning and genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] The present invention will be further described below in combination with specific embodiments and accompanying drawings.

[0058] In order to make the browser vulnerability mining sample acquisition method described in the present invention easier to understand and close to the real application, the following describes the overall process from the sample generation and optimization model and the actual browser vulnerability mining, including the core neural network of the present invention. Network structure and optimization genetic operator:

[0059] (1) Collect relevant samples and classify them, and filter out memory corruption exploit samples such as stack overflow, heap overflow, integer overflow, and reuse after free. When constructing the sample library, only select samples of memory corruption exploits. In order to improve the learning efficiency of the deep neural network and clarify the learning direction, artificial variation is used to expand the sample se...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a browser fuzzy test sample set acquisition method. The method comprises the following steps of (1) preprocessing a document, and counting html file elements to obtain an inputvector and element statistical data; and (2) performing deep learning by using an LSTM neural network, and decoding the generated sample to obtain a generated sample. (3) performing tree coding on the generated sample to obtain a parent population, and calculating a fitness function; and (4) optimizing the parent population by using the optimized genetic operator until a termination result is met. The method can be used for browser vulnerability mining, the mining direction is more targeted, and mining efficiency is higher.

Description

technical field [0001] The invention relates to a browser sample set acquisition method based on deep learning and genetic algorithm, belonging to the technical field of vulnerability mining. Background technique [0002] As a necessary tool for surfing the Internet, the browser occupies a very important position, and its frequency of security threats and attacks is higher than that of other software. In common attack scenarios, attackers attack browsers or other files containing links to make browsers parse data incorrectly, obtain cached data by attacking browser memory, or use browsers as springboards to analyze communication protocols, attack servers, and databases. The memory leaked by the browser may include sensitive information such as personal account numbers and passwords, and it is possible to obtain a large amount of user data by attacking the browser server and database. The vulnerability mining of the browser can expose the security risks of the browser in adv...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F11/36G06N3/08G06N3/12
CPCG06F11/3684G06N3/08G06N3/126
Inventor 方勇刘亮张磊朱光夏天
Owner SICHUAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap