Synthetic Network Packet Generation through Statistical Learning and Genetic Algorithms
Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions across both benign and malicious activity. Existing public datasets suffer from fixed activity distributions and extreme class imbalance, while deep generative models (GANs, VAEs) provide no mechanism to enforce that synthetic packets remain within physically valid feature ranges. This paper proposes and compares two constraint-enforcing approaches for synthetic IoT network packet generation: (i) a statistical learning method combining PCA-based latent space sampling with dual One-Class SVM (OCSVM) and Isolation Forest (IF) boundary enforcemen...
| Authors | Mayank Raj, Nathaniel D. Bastian, Lance Fiondella et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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