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Greybox Fuzzing Based on Ant Colony Algorithm

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Advanced Information Networking and Applications (AINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1151))

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Abstract

Greybox fuzzing technology is a kind of fuzzing technology that is commonly used now and effective. This fuzzing technology can guide the direction of fuzzing by acquiring the execution information of some paths in the program. However, the gray box fuzzy testing technology commonly used in the market today evaluates the seed of a sample by its path depth, execution time, and whether there is a new path to judge the quality of a sample, which is often not comprehensive. This article will propose a sample seed screening technology that uses ant colony algorithm to control gray box fuzzy test. By estimating the transition probability between the basic block and the basic block, we can determine what kind of seed sample is more likely to mutate into a new sample file. Based on this, the order and degree of fuzzing of the samples are determined, so as to improve the efficiency of fuzzing.

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Author information

Authors and Affiliations

  1. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China

    Bowen Sun, Baojiang Cui & Yeqi Fu

  2. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China

    Bo Wang

  3. National Engineering Laboratory for Mobile Network Security, Beijing, China

    Bowen Sun & Baojiang Cui

Authors
  1. Bowen Sun
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  2. Bo Wang
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  3. Baojiang Cui
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  4. Yeqi Fu
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Corresponding author

Correspondence to Bowen Sun .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania “Luigi Vanvitelli”, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Sun, B., Wang, B., Cui, B., Fu, Y. (2020). Greybox Fuzzing Based on Ant Colony Algorithm. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_112

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