WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914659349561344 |
|---|---|
| author | Yang, Liqun Li, Chunan Qiu, Yongxin Wei, Chaoren Yang, Jian Guo, Hongcheng Ma, Jinxin Li, Zhoujun |
| author_facet | Yang, Liqun Li, Chunan Qiu, Yongxin Wei, Chaoren Yang, Jian Guo, Hongcheng Ma, Jinxin Li, Zhoujun |
| contents | The importance of addressing security vulnerabilities is indisputable, with software becoming crucial in sectors such as national defense and finance. Consequently, The security issues caused by software vulnerabilities cannot be ignored. Fuzz testing is an automated software testing technology that can detect vulnerabilities in the software. However, most previous fuzzers encounter challenges that fuzzing performance is sensitive to initial input seeds. In the absence of high-quality initial input seeds, fuzzers may expend significant resources on program path exploration, leading to a substantial decrease in the efficiency of vulnerability detection. To address this issue, we propose WGAN-AFL. By collecting high-quality testcases, we train a generative adversarial network (GAN) to learn their features, thereby obtaining high-quality initial input seeds. To overcome drawbacks like mode collapse and training instability inherent in GANs, we utilize the Wasserstein GAN (WGAN) architecture for training, further enhancing the quality of the generated seeds. Experimental results demonstrate that WGAN-AFL significantly outperforms the original AFL in terms of code coverage, new paths, and vulnerability discovery, demonstrating the effective enhancement of seed quality by WGAN-AFL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN Yang, Liqun Li, Chunan Qiu, Yongxin Wei, Chaoren Yang, Jian Guo, Hongcheng Ma, Jinxin Li, Zhoujun Cryptography and Security The importance of addressing security vulnerabilities is indisputable, with software becoming crucial in sectors such as national defense and finance. Consequently, The security issues caused by software vulnerabilities cannot be ignored. Fuzz testing is an automated software testing technology that can detect vulnerabilities in the software. However, most previous fuzzers encounter challenges that fuzzing performance is sensitive to initial input seeds. In the absence of high-quality initial input seeds, fuzzers may expend significant resources on program path exploration, leading to a substantial decrease in the efficiency of vulnerability detection. To address this issue, we propose WGAN-AFL. By collecting high-quality testcases, we train a generative adversarial network (GAN) to learn their features, thereby obtaining high-quality initial input seeds. To overcome drawbacks like mode collapse and training instability inherent in GANs, we utilize the Wasserstein GAN (WGAN) architecture for training, further enhancing the quality of the generated seeds. Experimental results demonstrate that WGAN-AFL significantly outperforms the original AFL in terms of code coverage, new paths, and vulnerability discovery, demonstrating the effective enhancement of seed quality by WGAN-AFL. |
| title | WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2401.16947 |