Security at the Edge for Resource-Limited IoT Devices

The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy's modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.

Data set and machine learning models for the classification of network traffic originators

The widespread adoption of encryption in computer network traffic is increasing the difficulty of analyzing such traffic for security purposes. The data set presented in this data article is composed of network statistics computed on captures of TCP flows, originated by executing various network stress and web crawling tools, along with statistics of benign web browsing traffic. Furthermore, this data article describes a set of Machine Learning models, trained using the described data set, which can classify network traffic by the tool category (network stress tool, web crawler, web browser), the specific tool (e.g., Firefox), and also the tool version (e.g., Firefox 68) used to generate it. These models are compatible with the analysis of traffic with encrypted payload since statistics are evaluated only on the TCP headers of the packets. The data presented in this article can be useful to train and assess the performance of new Machine Learning models for tool classification.

Online-based intervention on LGBTI+ health promotion in Brazil: development and preliminary impacts

Background

Brazil reports public healthcare gaps concerning lesbian, gay, bisexual, transgender, and intersex (LGBTI+) people. Massive Open Online Courses (MOOCs) are online-based teaching interventions, however their use is still scarce in Brazil. We aim to describe the development and to analyze preliminary impacts of the first online-based intervention through MOOCs for LGBTI+ health promotion in Brazil.

Methods

We adopted mixed qualitative and quantitative approaches, based on participatory-action research: 1) to design the content and format of the MOOC, we conducted 10 focus groups during 2018 and 2019 with key stakeholders in three different Brazilian State capitals, identifying needs and challenges and linking it with guidelines of the National Policy for Comprehensive Health of LGBTI+ people; 2) based on step 1, we developed the MOOC, producing reading content, video-classes and multimedia; 3) we quantitatively analyzed, through a survey, its impact and reach after six months.

Results

The results showed that: a) focus groups were essentials to inform the priorities to design a tailored MOOC; b) the MOOC had 30 hours of autonomous learning activities divided into two modules, with problem-based evaluation; c) the first ten-month of the course had a total of 5,000 people enrolled with a completion rate of approximately 30%; d) 1,700 successfully completed the course; e) 582 participants joined the survey post-course evaluation and for 370 (63.6%) of them, this was the first training activity on LGBTI+ health.

Conclusions

Results point to a high interest in LGBTI+ health and high demand for training on this topic among health professionals. The MOOC format was successful and reliable in enabling the teaching-learning process.

Key messages

Participatory action research was able to inform the content of the first MOOC for LGBTI+ health in Brazil. The MOCC had high interest, and for the majority of concluding participants it was the first activity and formal education about LGBTI+ health.