We report about a HTTP botnet detection strategy based on a behavioral analysis of raw traffic data with the aim at minimizing resources necessary for the detection. It involves the selective choice of traffic characteristical features and their extraction with engineered probes, in a context of evolving malicious traffic. We develop the extraction software for eight selected features and experiment with a Multilayer Perceptron Classifier (MLP) over a benchmark traffic dataset for botnet detection, achieving a good 98.03% accuracy. In the effort to optimize the classifier overall performance by reducing data redundancy, we compute a statistics on Decision Tree Classifiers (DT) in order to rank features and observe that, by selecting out few of the lowest ranked ones (3), we can maintain MLP accuracy at 97.54% yet reducing probing resources and costs. We obtain a small further improvement in MLP performance, avoid the lengthy process of running the statistics of the DTs on actual data, and boost the ranking/selecting-out process by means of mutual partion entropy computation.

Sarcopenia, a condition characterised by the progressive decline in skeletal muscle mass and function, presents significant challenges in geriatric healthcare. Despite advances in its management, complex etiopathogenesis and the heterogeneity of diagnostic criteria underlie the limited precision of existing assessment methods. Therefore, efforts are needed to improve the knowledge and pave the way for more effective management and a more precise diagnosis. To this purpose, emerging technologies such as artificial intelligence (AI) can facilitate the identification of novel and accurate biomarkers by modelling complex data resulting from high-throughput technologies, fostering the setting up of a more precise approach. Based on such considerations, this review explores AI’s transformative potential, illustrating studies that integrate AI, especially machine learning and deep learning, with heterogeneous data such as clinical, anthropometric and molecular data. Overall, the present review will highlight the relevance of large-scale, standardised studies to validate biomarker signatures using AI-driven approaches.

Data science has emerged as an important field in higher education, presenting complex challenges for students due to its interdisciplinary nature involving statistical and computational concepts. In previous research, we introduced the rDSA system (R Data Science Assignments) to support data science students during formative assessments by providing exercises and feedback. As known, formative assessment has evolved from an “interim” evaluation method into a continuous process that enhances student understanding and informs educators to adjust their teaching strategies. It is closely linked to scaffolding, i.e., the method aiming to guide students from their current knowledge to the skills they can develop next. The research reported in this paper builds on previous findings that showed improved learning outcomes and good student engagement. We explored how to enhance student feedback by incorporating Large Language Models (LLMs) with traditional AI into the rDSA system. The results show that students extensively used the system, submitting numerous solutions and attempting various exercises, with activity peaking before exams. The system exceeded expectations in providing useful insights and feedback. However, students faced challenges related to focused attention, probably due to the verbosity of feedback. Learning outcomes, on average, improved, with students who solved more exercises using the tool, performing better than the others.