Present research proposes the application of unsupervised and supervised machine‐learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural‐projection method for dimensionality‐reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well‐known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real‐life Android malware data by means of the well‐known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.

Smart robotics is one of the fields that are been greatly enriched by the development of Cyber-Physical Systems (CPS). Under this frame, the software facet of robots is as important as the physical one. As it is widely known, the ever-increasing complexity of robots usually implies a parallel increase in the number of failures of such systems. Due to this, system monitoring and anomaly detection play a key role in the implementation of smart robotics and Artificial Intelligence (AI) can significantly contribute to this task. Accordingly, some Exploratory Projection Pursuit techniques, mainly implemented through neural networks, are applied an compared in the present paper to monitor the performance of a component-based robotic software. Thanks to the intuitive projections obtained by these techniques, anomaly detection can be also visually carried out. The visualizations are validated on an open and up-to-date dataset containing information about software anomalies that affect the middleware (a crucial component in CPS) of the analysed robot.