Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Robust Scareware Image Detection

Christian Seifert, Jack W. Stokes, Christina Colcernian, John C. Platt, and Long Lu

Abstract

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.

Details

Publication typeInproceedings
Published inProceedings IEEE Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE SPS
> Publications > Robust Scareware Image Detection