Robust scareware image detection

In this paper, we propose an image-based detection method to identify web-based scareware attacks that is robust to evasion techniques. We evaluate the method on a large-scale data set that resulted in an equal error rate of 0.018%. Conceptually, false positives may occur when a visual element, such...

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Bibliographic Details
Main Authors: Seifert, Christian, Stokes, Jack W., Colcernian, Christina, Platt, John C., Long Lu
Format: Conference Proceeding
Language:English
Subjects:
Animation
Computers
Malware
Robustness
scareware
security
social engineering
Training
Visualization
Web pages
Online Access:Request full text
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Description
Summary:In this paper, we propose an image-based detection method to identify web-based scareware attacks that is robust to evasion techniques. We evaluate the method on a large-scale data set that resulted in an equal error rate of 0.018%. Conceptually, false positives may occur when a visual element, such as a red shield, is embedded in a benign page. We suggest including additional orthogonal features or employing graders to mitigate this risk. A novel visualization technique is presented demonstrating the acquired classifier knowledge on a classified screenshot.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2013.6638192