Generative AI creates synthetic malware to boost cybersecurity defenses

Researchers have developed a novel system to generate synthetic malware samples using generative AI, addressing the challenge of scarce and imbalanced datasets in cybersecurity. By treating malware binaries as mnemonic opcode sequences and applying natural language processing techniques, the system employs Generative Adversarial Networks, WGAN-GP, and a modified Diffusion model. Augmenting existing datasets with Diffusion-based synthetic data significantly boosted malware classification performance, particularly for minor classes, leading to an overall improvement of 8% and achieving 96% accuracy. AI

IMPACT Enhances malware detection capabilities by improving classification accuracy for underrepresented malware types.

RANK_REASON Academic paper proposing a new method for generating synthetic malware using generative AI.

Read on arXiv cs.LG →

• arXiv
• Diffusion model
• Generative Adversarial Networks
• Generative AI
• Malware
• Wasserstein Generative Adversarial Networks with Gradient Penalty
• WGAN-GP

• paper
• safety

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