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The rise of the social web has brought a series of privacy concerns and threats. In particular, data leakage is a risk that affects the privacy of not only companies but individuals. Although there are tools that can prevent data losses, they require a prior step that involves the sensitive data to be properly identified. In this paper, we propose a new automatic approach that applies Named Entity Recognition (NER) to prevent data leaks. We conduct an empirical study with realworld data and show that this NER-based approach can enhance the prevention of data losses. In addition, we present and detail the implementation of a prototype built with these techniques and show how it can be used by both particulars and companies in order to handle data losses.

Malware is any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing at a faster rate every year and poses a serious global security threat. Although signaturebased detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new malware. Supervised machinelearning models have been used to address this issue. However, the use of supervised learning is limited because it needs a large amount of malicious code and benign software to first be labelled. In this paper, we propose a new method that uses single-class learning to detect unknown malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either malware or legitimate software. We performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy. * Corresponding author Email addresses: isantos@deusto.es (Igor Santos), felix.brezo@deusto.es (Felix Brezo), borja.sanz@deusto.es (Borja Sanz), claorden@deusto.es (Carlos Laorden), pablo.garcia.bringas@deusto.es (Pablo G. Bringas)