Newest papers

Mal-AAEGAN: Adversarial Autoencoder with Double-Critic WGAN-GP for Malware Generation

Nam Hoang Thanh, Trung Pham Duy, Lam Bui Thu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26488 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

没有机械学习的连续二进制(ML)恶意软件探测器被称为易于对抗的样本。 MalGAN、Mal-LSGAN 和我们以前的 Mal-D2GAN 等生成对抗网络(GAN)可以制造对抗的单个恶意软件来探测和硬化这些探测器,但它们可以直接在高尺寸、分散的二进制特征空间中操作,导致不稳定的训练、消失的基层和有限多样性的对抗样本。 为了解决这些问题,我们提出了 Mal-AAEGAN。 首先,一个对抗的自动编码器(AAE)会学习低尺寸的、规律化的和连续的对 $ M$ 二进制二进制特征特征的隐形表示,而发电器在这种隐形空间中执行一个附加干

Machine translation (Meta M2M100); the English original follows.

Abstract

Machine-learning (ML) malware detectors are known to be vulnerable to adversarial samples. Generative adversarial networks (GANs) such as MalGAN, Mal-LSGAN, and our previous Mal-D2GAN can craft adversarial malware to probe and harden these detectors, yet they operate directly on a high-dimensional, discrete binary feature space, which leads to unstable training, vanishing gradients, and adversarial samples of limited diversity. To address these problems we propose Mal-AAEGAN. First, an adversarial autoencoder (AAE) learns a low-dimensional, regularized and continuous latent representation of the $M$-dimensional binary API feature vector, and the generator performs an additive perturbation in this latent space rather than in the raw binary space. Second, training follows the Wasserstein GAN with gradient penalty (WGAN-GP) scheme using two critics: a mimic-black-box critic that inherits the role of the substitute detector and a naive critic that inherits the role of the additional detector. A decode-binarize-OR pipeline guarantees that the original API calls of the malware are preserved. The model is evaluated on two malware datasets of different input dimensionality ($M\in\{139,215\}$) against eight common ML detectors and compared, under an identical protocol, with MalGAN, Mal-LSGAN and Mal-D2GAN. Mal-AAEGAN trains stably in the continuous latent space and attains an evasion rate on par with or higher than these binary-space baselines across both datasets, while its generator stays responsive to the noise input rather than reducing to a single fixed perturbation per malware.

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Topics: Computer science, Malware, Adversarial system, Autoencoder, Artificial intelligence, Discriminator

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