# IoT/IoMT Attack Detection on CICIoMT 2024 (test) Source: [IoT/IoMT Attack Detection on CICIoMT 2024 (test)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:31:18.366Z Evaluation of model performance for general IoT/IoMT Attack Detection using the test split of the CICIoMT 2024 dataset, reporting macro-averaged precision, recall, F1 score, and overall accuracy. A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | IoT/IoMT Attack Detection | | Dataset (including any stated split) | CICIoMT 2024 (test) | | Metric | Accuracy | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 7 of 7 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Accuracy](): Higher is better; unit: PERCENT. - [Macro Precision](): Higher is better; unit: PERCENT. - [Macro Recall](): Higher is better; unit: PERCENT. - [Macro F1 Score](): Higher is better; unit: PERCENT. ## Recorded results | Method | Accuracy | Result date | Report paper | | --- | --- | --- | --- | | ET-BERT | 97.69 | Not recorded | 695b455a53fdd5c361742e4b | | Traffic-MoE | 97.69 | Not recorded | 695b455a53fdd5c361742e4b | | NetGPT | 89.58 | Not recorded | 695b455a53fdd5c361742e4b | | TrafficFormer | 89.12 | Not recorded | 695b455a53fdd5c361742e4b | | AppScanner | 69.29 | Not recorded | 695b455a53fdd5c361742e4b | | FS-Net | 57.88 | Not recorded | 695b455a53fdd5c361742e4b | | FlowPrint | 1.32 | Not recorded | 695b455a53fdd5c361742e4b | ### ET-BERT Accuracy: 97.69. Result ID: 6996f7bb5698665ec4447af6. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### Traffic-MoE Accuracy: 97.69. Result ID: 6996f7bb5698665ec4447af9. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### NetGPT Accuracy: 89.58. Result ID: 6996f7bb5698665ec4447af7. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### TrafficFormer Accuracy: 89.12. Result ID: 6996f7bb5698665ec4447af8. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### AppScanner Accuracy: 69.29. Result ID: 6996f7bb5698665ec4447af3. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Machine Learning (ML)"}. This record does not assert independent reproduction. ### FS-Net Accuracy: 57.88. Result ID: 6996f7bb5698665ec4447af5. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Deep Learning (DL)"}. This record does not assert independent reproduction. ### FlowPrint Accuracy: 1.32. Result ID: 6996f7bb5698665ec4447af4. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Machine Learning (ML)"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.