Improving search space analysis of fuzzing mutators using cryptographic structures

Sadegh Bamohabbat Chafjiri; Phil Legg; Michail-Antisthenis Tsompanas; Jun Hong

doi:10.1007/978-981-97-3973-8_10

Improving search space analysis of fuzzing mutators using cryptographic structures

Sadegh Bamohabbat Chafjiri^*
, Phil Legg
, Michail-Antisthenis Tsompanas
, Jun Hong

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the existing HonggFuzz fuzzing library, we propose HonggFuzz+ and demonstrate its effectiveness over other leading fuzzers, such as how the method can uncover bugs and edges earlier due to enhanced search space optimisation. By introducing these two structures, we can diversify memory region relationships that can ultimately improve the performance of HonggFuzz. We demonstrate our approach on a range of common software examples from previous software fuzzing literature. Our results show better or as good performance across a range of software targets when compared to other leading fuzzing techniques. We discuss the relevance of the findings and consider future directions for improving software fuzzing search space analysis.

Original language	English
Title of host publication	AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023)
Editors	Chaminda Hewage, Liqaa Nawaf, Nishtha Kesswani
Place of Publication	Singapore
Publisher	Springer Nature
Pages	153-172
Volume	1032
Edition	1st
ISBN (Electronic)	9789819739738
ISBN (Print)	9789819739721
DOIs	https://doi.org/10.1007/978-981-97-3973-8_10
Publication status	Published (VoR) - 18 Sept 2024

Publication series

Name	Lecture Notes in Networks and Systems

Keywords

Fuzzing
Cryptographic Mutation
Memory Swap
Evolutionary Fuzzing
Coverage-guided Fuzzing
Cryptanalytic Fuzzing

Access to Document

10.1007/978-981-97-3973-8_10

Cite this

Bamohabbat Chafjiri, S., Legg, P., Tsompanas, M.-A., & Hong, J. (2024). Improving search space analysis of fuzzing mutators using cryptographic structures. In C. Hewage, L. Nawaf, & N. Kesswani (Eds.), AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023) (1st ed., Vol. 1032, pp. 153-172). (Lecture Notes in Networks and Systems). Springer Nature. https://doi.org/10.1007/978-981-97-3973-8_10

Bamohabbat Chafjiri, Sadegh ; Legg, Phil ; Tsompanas, Michail-Antisthenis et al. / Improving search space analysis of fuzzing mutators using cryptographic structures. AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023). editor / Chaminda Hewage ; Liqaa Nawaf ; Nishtha Kesswani. Vol. 1032 1st. ed. Singapore : Springer Nature, 2024. pp. 153-172 (Lecture Notes in Networks and Systems).

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title = "Improving search space analysis of fuzzing mutators using cryptographic structures",

abstract = "This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the existing HonggFuzz fuzzing library, we propose HonggFuzz+ and demonstrate its effectiveness over other leading fuzzers, such as how the method can uncover bugs and edges earlier due to enhanced search space optimisation. By introducing these two structures, we can diversify memory region relationships that can ultimately improve the performance of HonggFuzz. We demonstrate our approach on a range of common software examples from previous software fuzzing literature. Our results show better or as good performance across a range of software targets when compared to other leading fuzzing techniques. We discuss the relevance of the findings and consider future directions for improving software fuzzing search space analysis.",

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Bamohabbat Chafjiri, S, Legg, P, Tsompanas, M-A & Hong, J 2024, Improving search space analysis of fuzzing mutators using cryptographic structures. in C Hewage, L Nawaf & N Kesswani (eds), AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023). 1st edn, vol. 1032, Lecture Notes in Networks and Systems, Springer Nature, Singapore, pp. 153-172. https://doi.org/10.1007/978-981-97-3973-8_10

Improving search space analysis of fuzzing mutators using cryptographic structures. / Bamohabbat Chafjiri, Sadegh; Legg, Phil; Tsompanas, Michail-Antisthenis et al.
AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023). ed. / Chaminda Hewage; Liqaa Nawaf; Nishtha Kesswani. Vol. 1032 1st. ed. Singapore: Springer Nature, 2024. p. 153-172 (Lecture Notes in Networks and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bamohabbat Chafjiri, Sadegh

AU - Legg, Phil

AU - Tsompanas, Michail-Antisthenis

AU - Hong, Jun

PY - 2024/9/18

Y1 - 2024/9/18

N2 - This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the existing HonggFuzz fuzzing library, we propose HonggFuzz+ and demonstrate its effectiveness over other leading fuzzers, such as how the method can uncover bugs and edges earlier due to enhanced search space optimisation. By introducing these two structures, we can diversify memory region relationships that can ultimately improve the performance of HonggFuzz. We demonstrate our approach on a range of common software examples from previous software fuzzing literature. Our results show better or as good performance across a range of software targets when compared to other leading fuzzing techniques. We discuss the relevance of the findings and consider future directions for improving software fuzzing search space analysis.

AB - This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the existing HonggFuzz fuzzing library, we propose HonggFuzz+ and demonstrate its effectiveness over other leading fuzzers, such as how the method can uncover bugs and edges earlier due to enhanced search space optimisation. By introducing these two structures, we can diversify memory region relationships that can ultimately improve the performance of HonggFuzz. We demonstrate our approach on a range of common software examples from previous software fuzzing literature. Our results show better or as good performance across a range of software targets when compared to other leading fuzzing techniques. We discuss the relevance of the findings and consider future directions for improving software fuzzing search space analysis.

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KW - Coverage-guided Fuzzing

KW - Cryptanalytic Fuzzing

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BT - AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023)

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ER -

Bamohabbat Chafjiri S, Legg P, Tsompanas MA, Hong J. Improving search space analysis of fuzzing mutators using cryptographic structures. In Hewage C, Nawaf L, Kesswani N, editors, AI Applications in Cyber Security and Communication Networks: Proceedings of Ninth International Conference on Cyber Security, Privacy in Communication Networks (ICCS 2023). 1st ed. Vol. 1032. Singapore: Springer Nature. 2024. p. 153-172. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-97-3973-8_10

Improving search space analysis of fuzzing mutators using cryptographic structures

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