Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912039316750336 |
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| author | Pitsiorlas, Ioannis Arvanitakis, George Kountouris, Marios |
| author_facet | Pitsiorlas, Ioannis Arvanitakis, George Kountouris, Marios |
| contents | This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space Pitsiorlas, Ioannis Arvanitakis, George Kountouris, Marios Cryptography and Security Artificial Intelligence Machine Learning This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security. |
| title | Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.13774 |