# Traffic Detection on ISCX Tor 2016 Source: [Traffic Detection on ISCX Tor 2016]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:31:18.334Z Evaluation of traffic detection performance on the ISCX Tor 2016 dataset, typically measured using Accuracy, Mean Precision, Mean Recall, and Mean F1-Score. 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 | Traffic Detection | | Dataset (including any stated split) | ISCX Tor 2016 | | 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. - [Mean Precision](): Higher is better; unit: PERCENT. - [Mean Recall](): Higher is better; unit: PERCENT. - [Mean F1-Score](): Higher is better; unit: PERCENT. ## Recorded results | Method | Accuracy | Result date | Report paper | | --- | --- | --- | --- | | Traffic-MoE | 90.89 | Not recorded | 695b455a53fdd5c361742e4b | | AppScanner | 86.9 | Not recorded | 695b455a53fdd5c361742e4b | | NetGPT | 85.06 | Not recorded | 695b455a53fdd5c361742e4b | | TrafficFormer | 79.75 | Not recorded | 695b455a53fdd5c361742e4b | | ET-BERT | 79.32 | Not recorded | 695b455a53fdd5c361742e4b | | FS-Net | 66.79 | Not recorded | 695b455a53fdd5c361742e4b | | FlowPrint | 41.27 | Not recorded | 695b455a53fdd5c361742e4b | ### Traffic-MoE Accuracy: 90.89. Result ID: 6996f7bb5698665ec4447ae4. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### AppScanner Accuracy: 86.9. Result ID: 6996f7bb5698665ec4447ade. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Machine Learning (ML)"}. This record does not assert independent reproduction. ### NetGPT Accuracy: 85.06. Result ID: 6996f7bb5698665ec4447ae2. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### TrafficFormer Accuracy: 79.75. Result ID: 6996f7bb5698665ec4447ae3. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### ET-BERT Accuracy: 79.32. Result ID: 6996f7bb5698665ec4447ae1. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"paradigm":"Pre-training and Fine-tuning"}. This record does not assert independent reproduction. ### FS-Net Accuracy: 66.79. Result ID: 6996f7bb5698665ec4447ae0. [Reporting paper]() Recorded evaluation configuration: {"paradigm":"Deep Learning (DL)"}. This record does not assert independent reproduction. ### FlowPrint Accuracy: 41.27. Result ID: 6996f7bb5698665ec4447adf. [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.