WasmMal-15K: a Dataset of Wasm malware
If you use this Dataset in your research, please cite our paper on WWW’25:
ACM Reference Format:
Yuxia Sun, Huihong Chen, Zhixiao Fu, Wenjian Lv, Zitao Liu, and Haolin Liu. WasmGuard: Enhancing Web Security through Robust Raw-Binary Detection of WebAssembly Malware. In Proceedings of the ACM Web Conference 2025 (WWW ’25), April 28–May 2, 2025, Sydney, NSW, Australia. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3696410.3714696
####[Abstract] WebAssembly (Wasm), a binary instruction format designed for efficient cross-platform execution, has rapidly become a foundational web standard, widely adopted in browsers, client-side, and server-side applications. However, its growing popularity has led to an increase in Wasm-targeted malware, including cryptojackers and obfuscated malicious scripts, which pose significant threats to web security. In spite of progress in deep learning based detection methods for Wasm malware, such as MINOS, these approaches face substantial performance degradation in adversarial environments. In our experiments, MINOS’s detection accuracy dropped to 49.90% under adversarial attacks, revealing critical vulnerabilities. To address this, we introduce \textbf{WasmGuard}, a robust malware detection framework tailored for Wasm. WasmGuard employs FGSM-based adversarial training with prior-based initialization for perturbation bytes in customized sections, coupled with a novel adversarial contrastive learning objective. Using our large-scale dataset, \textbf{WasmMal-15K} (publicly available at https://github.com/Yuxia-Sun/WasmMal), WasmGuard outperforms six competing methods, achieving up to 99.20% Robust Accuracy and 99.93% Standard Accuracy under PGD-50 adversarial attacks, while maintaining low training overhead. Additionally, we have released \textbf{WebChecker}, a WasmGuard-powered browser plugin, providing real-time protection against malicious Wasm files, at https://github.com/Yuxia-Sun/WasmGuard.-Sun/WasmMal).