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A Cross-Platform Association Detection Method for Firmware Vulnerability

A detection method and cross-platform technology, which is applied in the field of cross-platform correlation detection of firmware vulnerabilities, can solve certain problems such as accuracy, and achieve the effects of improving accuracy, high detection efficiency, and improving accuracy and efficiency

Inactive Publication Date: 2018-12-21
南京明博互联网安全创新研究院有限公司
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Problems solved by technology

[0005] The existing cross-platform vulnerability correlation detection method discovRE based on function numerical features and structural features uses kNN to quickly screen out a small number of functions, and uses the maximum common subgraph algorithm McGregor to further match candidate functions. To a certain extent, the efficiency of cross-platform vulnerability correlation detection has been improved, but there are certain problems in the accuracy of the function screening stage

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  • A Cross-Platform Association Detection Method for Firmware Vulnerability
  • A Cross-Platform Association Detection Method for Firmware Vulnerability
  • A Cross-Platform Association Detection Method for Firmware Vulnerability

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[0031] The present invention will be further described below in conjunction with accompanying drawing:

[0032] Such as figure 1 and figure 2 Shown: the present invention comprises the following steps:

[0033] S1: In the firmware acquisition stage, the firmware can be obtained by downloading the manufacturer's image or manually uploading by the user;

[0034] S2: Firmware decompression and disassembly stage, use the firmware decompression tool to obtain the binary files related to the firmware, and obtain the firmware disassembly function through firmware decompression and disassembly;

[0035] S3: extracting firmware function features;

[0036] S4: Use the weighted Euclidean distance-based kNN-SVM to quickly screen and accurately match the functions to be detected, and judge the accuracy of the correlation between the functions to be detected and known vulnerability functions;

[0037] S5: Confirm that the function to be detected is a suspected vulnerability.

[0038] ...

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Abstract

The invention provides a cross-platform association detection method for firmware vulnerability, which improves the accuracy and efficiency of the existing cross-platform firmware vulnerability association detection method, combines kNN with SVM, and achieves a better compromise between accuracy and efficiency. Based on the function attribute control flow graph, the bipartite graph matching algorithm is used to transform the function matching problem into the graph structure similarity computation problem, and the penalty term is added to the original algorithm, which improves the accuracy ofaccurate matching. The weighted Euclidean distance and the weighted Markov distance are used to improve the detection efficiency.

Description

technical field [0001] The invention relates to the technical field of correlation detection, in particular to a method for cross-platform correlation detection of firmware vulnerabilities. Background technique [0002] With the in-depth development of the Internet of Things, Internet of Things devices have gradually penetrated into all aspects of people's work and life. While bringing convenience, they have also brought increasingly serious security risks. In 2014, Tesla was found to have security vulnerabilities. In 2015, Hikvision’s monitoring equipment was detected as a serious security risk. In 2016, hackers used a large number of IoT devices to cause Dyn to encounter the most serious DDoS attack in history. These incidents make people deeply aware of the importance of IoT security. Among them, firmware security is an important aspect of IoT security. According to the 2017 OWASP survey report, firmware vulnerability attacks rank ninth among the vulnerability attacks o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06
CPCH04L63/1433H04L63/1416
Inventor 李超林冠洲
Owner 南京明博互联网安全创新研究院有限公司
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