Vincent Falconieri

Vincent Falconieri

Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, no open-source and turnkey library was found able to reach this goal. The present paper introduces an Open-Source modular library for the specific cases of visual correlation and Image Matching named Douglas-Quaid. The design of the library, chosen tradeoffs, encountered challenges, envisioned solutions as well as quality and speed results are presented in this paper. We also explore researches directions and future potential developments of the library. Our claim is that even partial automation of screenshots classification would reduce the burden on security teams and that Douglas-Quaid is a step forward in this direction.

Vincent Falconieri

Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. Many Image-Matching algorithms are presented in the litterature. The present paper introduces and provides a Open-Source benchmarking and evaluation tool for these algorithms. Is this paper, the framework evaluates algorithms on illustrative datasets, which are constituted of phishing and onion websites. Datasets are provided as Open-Data.

Vincent Falconieri

Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, the main resources to develop these tools are datasets, which are introduced and provided by the present paper, for the specific cases of visual correlation of phishing and onion websites. CIRCL's Open-Source tools are the sources of these screenshots, which had been manually verified against personal information leaks. Usage examples of these datasets are proposed in the current paper. These researches directions are, however, not the main contribution of the paper. The main contribution is the availability of the two datasets.

Vincent Falconieri

Analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, classified data is a prerequisite to develop these tools. Labelling tools are of great use in case of already known classes, but seemed limited for Open Set Classification. This paper presents a manual and collaborative classification tool, which uses graph representation.