BeCAPTCHA: Behavioral bot detection using touchscreen and mobile sensors benchmarked on HuMIdb

In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology and improve bot detection algorithms. For this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the touchscreen information obtained during a single drag and drop task in combination with the accelerometer data. The goal of BeCAPTCHA is to determine whether the drag and drop task was realized by a human or a bot. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb1 (Human Mobile Interaction database), a novel multimodal mobile database that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research communityThis work has been supported by projects: PRIMA, Spain (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN, Spain (H2020-MSCA-ITN-2019-860813), BIBECA RTI2018-101248-B-I00 (MINECO/FEDER), and BioGuard, Spain (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017). Spanish Patent Application P20203006

Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

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Machine Learning to Improve Security Operations Centers

Since the onset of the internet, the world has embraced this new technology and used it to collectively advance Humanity. Companies have followed the trend from the physical to the digital world, taking with them all their associated value. In order to safeguard this value, security needed to evolve, with enterprises employing departments of highly trained professionals. Nevertheless, the ever increasing amount of information in need of evaluation by these professionals requires the deployment of automation techniques, aiding in data analysis and bulk task processing, to reduce detection time and as such improve mitigation. This work proposes a novel tool designed to help in attack detection and alert aggregation, by leveraging machine learning techniques. The proposed solution is described in full and showcased using real data from an example implementation.Desde o aparecimento da internet, esta nova tecnologia tem sido usada para avançar a Humanidade. O mercado seguiu as tendências, passando do mundo físico para o digital e levando consigo todo o seu valor associado. De forma a salvaguardar este valor, a segurança precisou de se adaptar, com empresas a dedicarem departamentos inteiros com esse objetivo. No entanto, a quantidade cada vez mais elevada de informação a analisar exige o desenvolvimento de técnicas automáticas de processamento de dados e execução de tarefas em massa, para diminuir o tempo de deteção de ataques permitindo uma mitigação mais ágil dos mesmos. Este trabalho propõe uma ferramenta projetada para ajudar na deteção de ataques e agregação de alertas, usando técnicas de inteligência artificial. A solução proposta é descrita na íntegra e apresentada usando dados reais aplicados a uma implementação de exemplo