A Novel Byte-Level Flow-to-Image Encoding Method for Network Intrusion Detection Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913096728051712 |
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| author | Mu, Ziyu Yan, Zihui Shi, Xiyu Dogan, Safak |
| author_facet | Mu, Ziyu Yan, Zihui Shi, Xiyu Dogan, Safak |
| contents | Network-based Intrusion Detection Systems (IDS) are predominantly trained on tabular flow records, whose one-dimensional representations limit convolutional architectures from exploiting inter-feature spatial correlations. This paper presents a novel byte-level flow-to-image encoding method that converts each network-flow record into a fixed-size RGB image. Continuous features are serialised using IEEE-754 single-precision format and packed sequentially into pixels along an inverted-L shaped trajectory, while discrete features are mapped to byte values and placed contiguously in the middle image row's centre. The encoding is deterministic and reversible, preserving a fixed spatial layout across all samples. Four IDS models are evaluated on NSL-KDD and UNSW-NB15 datasets with both flow and image-based configurations. The image-based representation yields consistent accuracy gains of up to 15.6\% and 12.8\% for binary and multi-classification on UNSW-NB15, and up to 3.5\% and 3.2\% on NSL-KDD, highlighting the potential of byte-level visual encoding to strengthen AI-driven intrusion detection in local computer networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05275 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Novel Byte-Level Flow-to-Image Encoding Method for Network Intrusion Detection Systems Mu, Ziyu Yan, Zihui Shi, Xiyu Dogan, Safak Cryptography and Security Network-based Intrusion Detection Systems (IDS) are predominantly trained on tabular flow records, whose one-dimensional representations limit convolutional architectures from exploiting inter-feature spatial correlations. This paper presents a novel byte-level flow-to-image encoding method that converts each network-flow record into a fixed-size RGB image. Continuous features are serialised using IEEE-754 single-precision format and packed sequentially into pixels along an inverted-L shaped trajectory, while discrete features are mapped to byte values and placed contiguously in the middle image row's centre. The encoding is deterministic and reversible, preserving a fixed spatial layout across all samples. Four IDS models are evaluated on NSL-KDD and UNSW-NB15 datasets with both flow and image-based configurations. The image-based representation yields consistent accuracy gains of up to 15.6\% and 12.8\% for binary and multi-classification on UNSW-NB15, and up to 3.5\% and 3.2\% on NSL-KDD, highlighting the potential of byte-level visual encoding to strengthen AI-driven intrusion detection in local computer networks. |
| title | A Novel Byte-Level Flow-to-Image Encoding Method for Network Intrusion Detection Systems |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.05275 |