| _version_ | 1866902164185546752 |
|---|---|
| author | Matejek, Brian Gehani, Ashish Bastian, Nathaniel Clouse, Daniel Kline, Bradford Jha, Susmit |
| author_facet | Matejek, Brian Gehani, Ashish Bastian, Nathaniel Clouse, Daniel Kline, Bradford Jha, Susmit |
| contents | <p>These datasets provide packet-level labeling of the payloads in the CIC-IDS-2017 and UNSW-NB15 network intrusion detection datasets. A full discussion of the data processing can be found in our Transactions on Machine Learning Research journal paper <em>SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection</em>. Code for additional processing and experimentation can be found <a href="https://github.com/SRI-CSL/trinity-packet">here</a>. The UNSW-NB15 dataset contains over 50 million non-empty payloads coming from nine attack classes with benign background traffic. The CIC-IDS-2017 dataset contains over 30 million non-empty payloads coming from fourteen attack classes with benign background traffic. Both datasets are highly imbalanced, with 20-25x more benign packets than malicious ones.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15046995 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection Dataset Matejek, Brian Gehani, Ashish Bastian, Nathaniel Clouse, Daniel Kline, Bradford Jha, Susmit <p>These datasets provide packet-level labeling of the payloads in the CIC-IDS-2017 and UNSW-NB15 network intrusion detection datasets. A full discussion of the data processing can be found in our Transactions on Machine Learning Research journal paper <em>SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection</em>. Code for additional processing and experimentation can be found <a href="https://github.com/SRI-CSL/trinity-packet">here</a>. The UNSW-NB15 dataset contains over 50 million non-empty payloads coming from nine attack classes with benign background traffic. The CIC-IDS-2017 dataset contains over 30 million non-empty payloads coming from fourteen attack classes with benign background traffic. Both datasets are highly imbalanced, with 20-25x more benign packets than malicious ones.</p> |
| title | SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection Dataset |
| url | https://doi.org/10.5281/zenodo.15046995 |