COMPUTER SCIENCE AND TECHNOLOGY |
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From automation to intelligence: Survey of research on vulnerability discovery techniques |
ZOU Quanchen1, ZHANG Tao1, WU Runpu1, MA Jinxin1, LI Meicong1, CHEN Chen2,3, HOU Changyu4 |
1. China Information Technology security Evaluation Center, Beijing 100085, China; 2. School of Information and Navigation, Air Force Engineering University, Xi'an 710077, China; 3. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China; 4. Beijing Central Security Evaluation Technology Co. Ltd., Beijing 100085, China |
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Abstract In recent years, the increasing size and complexity of software packages has led to vulnerability discovery techniques gradually becoming more automatic and intelligent. This paper reviews the search characteristics of both traditional vulnerability discovery techniques and learning-based intelligent vulnerability discovery techniques. The traditional techniques include static and dynamic vulnerability discovery techniques which involve model checking, binary comparisons, fuzzing, symbolic execution and vulnerability exploitability analyses. This paper analyzes the problems of each technique and the challenges for realizing full automation of vulnerability discovery. Then, this paper also reviews machine learning and deep learning techniques for vulnerability discovery that include binary function identification, function similarity detection, test input generation, and path constraint solutions. Some challenges are the security and robustness of machine learning algorithms, algorithm selection, dataset collection, and feature selection. Finally, future research should focus on improving the accuracy and efficiency of vulnerability discovery algorithms and improving the automation and intelligence.
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Keywords
vulnerability discovery
fuzzing
symbolic execution
machine learning
deep learning
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Issue Date: 13 December 2018
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