Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection.

The increasing proliferation of Androidbased devices, which currently dominate the market with a staggering 72% global market share, has made them a prime target for attackers. Consequently, the detection of Android malware has emerged as a critical research area. Both academia and industry have explored various approaches to develop robust and efficient solutions for Android malware detection and classification, yet it remains an ongoing challenge. In this study, we present a supervised learning technique that demonstrates promising results in Android malware detection. The key to our approach lies in the creation of a comprehensive labeled dataset, comprising over 18,000 samples classified into five distinct categories: Adware, Banking, SMS, Riskware, and Benign applications. The effectiveness of our proposed model is validated using well-established datasets such as CICMalDroid2020, CICMalDroid2017, and CICAndMal2017. Comparing our results with state-of-the-art techniques in terms of precision, recall, efficiency, and other relevant factors, our approach outperforms other semi-supervised methods in specific parameters. However, we acknowledge that our model does not exhibit significant deviations when compared to alternative approaches concerning certain aspects. Overall, our research contributes to the ongoing efforts in the development of advanced techniques for Android malware detection and classification. We believe that our findings will inspire further investigations, leading to enhanced security measures and protection for Android devices in the face of evolving threats.

Nowadays one of the main focuses of the Spanish University system is achieving the active learning paradigm in the context of its integration into the European Higher Education Area. This goal is being addressed by means of the application of novel teaching mechanisms. Among a wide variety of learning approaches, the present work focuses on peer review, understood as a collaborative learning technique where students assess other student’s work and provide their own feedback. In this way, peer review has the overarching goal of improving the student learning during this process. Peer review has been successfully applied and analyzed in the literature. Indeed, many authors also recommend improving the design and implementation of self and peer review, which has been our main goal. This paper presents an empirical study based on the application of peer review assessment in different higher education BSc and MSc courses. In this way, six courses from different studies at the University of Malaga in Spain are subject to the application of peer review strategies to promote student learning and develop cross-wise skills such as critical thinking, autonomy and responsibility. Based on these experiences, a deep analysis of the results is performed, showing that a proper application of the peer review methodology provides reliable reviews (with close scores to the ones from the teacher) as well as an improvement in the students’ performance.

Security is considered one of the crucial issues for the widespread adoption of cloud computing. Despite all research done in preventive security for cloud computing, the high complexity and the interdependence of many software layers and infrastructures mean that in practice there are always chances for something going wrong. For this reason, there is a need to complement preventive security measures with reactive measures. Among these, monitoring is the most relevant approach. In this paper, we introduce a new and robust architecture for dynamic security monitoring and enforcement specially designed for cloud computing scenarios. Our solution is therefore a complete one including a three-layered architecture, a new language for expressing monitoring rules and a strategy based on the generation of a finite-state machine to improve the performance of the monitoring engine.

Security is gaining importance in the daily life of every citizen. The advent of Internet of Things devices in our lives is changing our conception of being connected through a single device to a multiple connection in which the centre of connection is becoming the devices themselves. This conveys the attack vector for a potential attacker is exponentially increased. This paper presents how the concatenation of several attacks on communication protocols (WiFi, Bluetooth LE, GPS, 433 Mhz and NFC) can lead to undesired situations in a domestic environment. A comprehensive analysis of the protocols with the identification of their weaknesses is provided. Some relevant aspects of the whole attacking procedure have been presented to provide some relevant tips and countermeasures.