Latent Diffusion for Internet of Things Attack Data Generation in Intrusion Detection
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A new study introduces a Latent Diffusion Model (LDM) for augmenting attack data in Machine Learning-based Intrusion Detection Systems (IDSs) tailored for IoT environments. Compared to traditional methods, LDM significantly improves performance against class imbalance, achieving F1-scores of up to 0.99 for DDoS and Mirai attacks, while enhancing sample diversity and reducing sampling time by 25%. This approach could be a game-changer for enhancing IDS effectiveness in real-world IoT applications.
Latent Diffusion Model Enhances Intrusion Detection in IoT Environments
Recent research has introduced a Latent Diffusion Model (LDM) for augmenting attack data in Machine Learning-based Intrusion Detection Systems (IDSs) in Internet of Things (IoT) environments. This approach significantly improves IDS performance, addressing class imbalances between benign and attack traffic.
Performance Evaluation and Results
The research involved experimentation with three IoT attack types: Distributed Denial-of-Service (DDoS), Mirai, and Man-in-the-Middle. Tests assessed the downstream performance of the IDSs and the generative quality of the samples produced by the LDM.
- LDM-generated samples resulted in enhanced IDS performance, achieving F1-scores of up to 0.99 for both DDoS and Mirai attacks.
- LDM consistently outperformed existing methods in various metrics, including distributional and dependency-based assessments.
- Qualitative analyses indicated that LDM preserves critical feature dependencies while generating diverse samples.
These results underscore the efficacy of using latent diffusion for synthetic IoT attack data generation, representing a scalable solution to enhance the effectiveness of ML-based IDSs in safeguarding IoT environments.
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📰 Original Source: https://arxiv.org/abs/2601.16976v1
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