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Malware is any kind of program explicitly designed to harm, such as viruses, trojan horses or worms. Since the amount of malware is growing exponentially, it already poses a serious security threat. Therefore, every incoming code must be analysed in order to classify it as malware or benign software. These tests commonly combine static and dynamic analysis techniques in order to extract the major amount of information from distrustful files. Moreover, the increment of the number of attacks hinders manually testing the thousands of suspicious archives that every day reach antivirus laboratories. Against this background, we address here an automatised system for malware behaviour analysis based on emulation and simulation techniques. Hence, creating a secure and reliable sandbox environment allows us to test the suspicious code retrieved without risk. In this way, we can also generate evidences and classify the samples with several machine-learning algorithms. We have developed the proposed solution, testing it with real malware. Finally, we have evaluated it in terms of reliability and time performance, two of the main aspects for such a system to work.

In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure.