method: Baseline - DREBIN2024-09-03
Authors: .
Affiliation: .
Description: Reproduction of the detector from Arp, Daniel, et al. “Drebin: Effective and explainable detection of android malware in your pocket.” NDSS. Vol. 14. 2014.
method: DREBIN basic algorithm2025-03-05
Authors: Vo Minh Thien Long
Affiliation: Le Quy Don Technical University
Email: thienlongtpct@mta.edu.vn
Description: It just a simple DREBIN solution. I want to test uploading submission.
method: DeepTrust2025-04-06
Authors: Daniel Pulido, Daniel Gibert
Affiliation: Artificial Intelligence Research Insititute (IIIA-CSIC)
Email: danielpulidocortazar@gmail.com
Description: DeepTrust is compound of two Multilayer Perceptron models which leverage adversarial training and fuzzy labelling distilled by a Random Forest Model. Both models are configured in a cascade fashion governed by an Isolation Random Forest that decides which model to use based on anomaly detection at the embedding level of the models.
To download the pretrained model: https://drive.google.com/drive/folders/1MzppCM60UBRjTAZ5jBm32Pfo0if21YmX?usp=sharing
| Area Under Time - F1 score | |||||
|---|---|---|---|---|---|
| Date | Method | Area Under Time - F1 score | |||
| 2024-09-03 | Baseline - DREBIN | 0.7927 | |||
| 2025-03-05 | DREBIN basic algorithm | 0.7927 | |||
| 2025-04-06 | DeepTrust | 0.7819 | |||
| 2024-09-03 | Baseline - SecSVM | 0.7705 | |||
| 2025-02-27 | SVM-CB (b=0.2, n=100) | 0.7597 | |||
| 2025-02-27 | SVM-CB (b=0.8, n=100) | 0.7594 | |||
| 2025-03-22 | DREBIN with SVM and features selection | 0.6873 | |||
| 2025-02-21 | Continual-Positive Congruent Training | 0.6155 | |||