Recent trends in digital text forensics and its evaluation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40802-1_28This paper outlines the concepts and achievements of our evaluation lab on digital text forensics, PAN 13, which called for original research and development on plagiarism detection, author identification, and author profiling. We present a standardized evaluation framework for each of the three tasks and discuss the evaluation results of the altogether 58 submitted contributions. For the first time, instead of accepting the output of software runs, we collected the softwares themselves and run them on a computer cluster at our site. As evaluation and experimentation platform we use TIRA, which is being developed at the Webis Group in Weimar. TIRA can handle large-scale software submissions by means of virtualization, sandboxed execution, tailored unit testing, and staged submission. In addition to the achieved evaluation results, a major achievement of our lab is that we now have the largest collection of state-of-the-art approaches with regard to the mentioned tasks for further analysis at our disposal.This work was partially supported by the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action.Gollub, T.; Potthast, M.; Beyer, A.; Busse, M.; Rangel Pardo, FM.; Rosso, P.; Stamatatos, E.... (2013). Recent trends in digital text forensics and its evaluation. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 282-302. https://doi.org/10.1007/978-3-642-40802-1_28S282302Aleman, Y., Loya, N., Vilarino Ayala, D., Pinto, D.: Two Methodologies Applied to the Author Profiling Task—Notebook for PAN at CLEF 2013. In: Forner, et al. (eds.) [15]Argamon, S., Juola, P.: Overview of the International Authorship Identification Competition at PAN-2011. 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Identifying Authorship Style in Malicious Binaries: Techniques, Challenges & Datasets

Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to identify authorship style. Our survey explores malicious author style and the adversarial techniques used by them to remain anonymous. We examine the adversarial impact on the state-of-the-art methods. We identify key findings and explore the open research challenges. To mitigate the lack of ground truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 15,660 malware labeled to 164 threat actor groups