R-Locker: Thwarting Ransomware Action through a Honey le-based Approach

Ransomware has become a pandemic nowadays. Although some proposals exist to fight against this increasing type of extorsion, most of them are prevention like and rely on the assumption that early detection is not so effective once the victim is infected. This paper presents a novel approach intended not just to early detect ransomware but to completly thwart its action. For that, a set of honeyfiles are deployed around the target environment in order to catch the ransomware. Instead of being normal archives, honeyfiles are FIFO like, so that the ransomware is blocked once it starts reading the file. In addition to frustrate its action, our honeyfile solution is able to automatically launch countermeasures to solve the infection. Moreover, as it does not require previous training or knowledge, the approach allows fighting against unknown, zero-day ransomware related attacks. As a proof of concept, we have developed the approach for Unix platforms. The tool, named R-Locker, shows excellent performance both from the perspective of its accuracy as well as in terms of complexity and resource consumption. In addition, it has no special needs or privileges and does not affect the normal operation of the overall environment

Using Machine Learning Algorithms for Author Profiling In Social Media Notebook for PAN at CLEF 2016

Abstract. In this paper we present our approach of solving the PAN 2016 Author Profiling Task. It involves classifying users' gender and age using social media posts. We used SVM classifiers and neural networks on TF-IDF and verbosity features. Results showed that SVM classifiers are better for English datasets and neural networks perform better for Dutch and Spanish datasets