HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents

Mas, Jordi; Rodríguez, José; Karatzas, Dìmosthenis; Sánchez, Gemma; Lladós, Josep

doi:10.1109/das.2008.70

Cited by 3 publications

(2 citation statements)

References 19 publications

(16 reference statements)

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“…During the process of converting the provenance graph to a histogram, node labels that have not appeared may be generated, resulting in an indeterminate number of abscissas of the histogram. The HistoSketch (Mas et al, 2008) algorithm can effectively solve this problem, and successfully convert the histogram into a fixed-length feature vector.…”

Section: Feature Sequence Extractionmentioning

confidence: 99%

Unicorn: Runtime Provenance-Based Detector for Advanced Persistent Threats

Han¹,

Pasquier²,

Bates³

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

129

111

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Advanced Persistent Threats (APTs) are difficult to detect due to their "low-and-slow" attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomalybased APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without predefined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects reallife APT scenarios with high accuracy.

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Section: Feature Sequence Extractionmentioning

confidence: 99%

Unicorn: Runtime Provenance-Based Detector for Advanced Persistent Threats

Han¹,

Pasquier²,

Bates³

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

129

111

View full text Add to dashboard Cite

show abstract

“…Nowadays, several works as [1], [2], [3] perform layout analysis for historical printed documents. For instance, they assist librarians to annotate the physical layout and logical structure of historical documents, as well as to transcript them.…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic Annotation Tool for Medieval Manuscripts

Baechler

Bloechle

Ingold

2010

2010 12th International Conference on Frontiers in Handwriting Recognition

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Medieval manuscript layouts are quite complex. They contain textual elements such as insertions, annotations, and corrections. They may be richly decorated with ornaments, illustrations, and decorative initials making their layout even more complex. In this paper we describe a semi-automatic tool which annotates medieval manuscripts using our generic format. This format allows to represent the physical structure of such manuscripts. Our semi-automatic tool is composed of two parts. The first part achieves a layout analysis which automatically segments manuscripts into text blocks and text lines. That is, a Multi-Layer Perceptron (MLP) identifies layout elements by using color features; it extracts the textual content image of the manuscript. Then, a segmentation based on Connected Component (CC) is performed on the textual content in order to retrieve text blocks and lines. The second part provides an interactive interface allowing the user to customize the automatic analysis, to visualize its results, and to correct them. Our tool is still a prototype, nevertheless, first experiments give encouraging results. Thus, in this paper, we show how to generate a ground truth for medieval manuscripts layouts.

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