Name
Title | ||
|---|---|---|
A King'S Ransom For Encryption: Ransomware Classification Using Augmented One-Shot Learning And Bayesian Approximation |
Abstract | ||
|---|---|---|
Newly emerging variants of ransomware pose an ever-growing threat to computer systems governing every aspect of modern life through the handling and analysis of big data. While various recent security-based approaches have focused on ransomware detection at the network or system level, easy-to-use post-infection ransomware classification for the lay user has not been attempted before. In this paper, we investigate the possibility of classifying the ransomware a system is infected with simply based on a screenshot of the splash screen or the ransom note captured using a consumer camera commonly found in any modern mobile device. To train and evaluate our system, we create a sample dataset of the splash screens of 50 well-known ransomware variants. In our dataset, only a single training image is available per ransomware. Instead of creating a large training dataset of ransomware screenshots, we simulate screenshot capture conditions via carefully-designed data augmentation techniques, enabling simple and efficient one-shot learning. Moreover, using model uncertainty obtained via Bayesian approximation, we ensure special input cases such as unrelated non-ransomware images and previously-unseen ransomware variants are correctly identified for special handling and not mis-classified. Extensive experimental evaluation demonstrates the efficacy of our work, with accuracy levels of up to 93.6% for ransomware classification. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9005540 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | Field | DocType |
Machine Learning, Ransomware Classification, Model Uncertainty, Bayesian Approximation, One-Shot Learning | Ransomware,Computer science,Encryption,Artificial intelligence,One-shot learning,Ransom,Machine learning,Bayesian probability | Conference |
ISSN | Citations | PageRank |
2639-1589 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Atapour Abarghouei | 1 | 4 | 4.15 |
| Stephen Bonner | 2 | 44 | 7.88 |
| Andrew Stephen Mcgough | 3 | 105 | 11.18 |