Transport Layer Security is becoming the de facto standard to provide end-to-end security in the current Internet. IoT and M2M scenarios are not an exception since TLS is also being adopted there. The ability of TLS for negotiating any security parameter, its flexibility and extensibility are responsible for its wide adoption but also for several attacks. Moreover, as it relies on Public Key Infrastructure (PKI) for authentication, it is also affected by PKI problems. Considering the advent of IoT/M2M scenarios and their particularities, it is necessary to have a closer look at TLS history to evaluate the potential challenges of using TLS and PKI in these scenarios. According to this, the article provides a deep revision of several security aspects of TLS and PKI, with a particular focus on current Certificate Pinning solutions in order to illustrate the potential problems that should be addressed.

Adaptive Authentication allows a system to dynamically select the best mechanism(s) for authenticating a user depending on contextual factors, such as location, proximity to devices, and other attributes. Though this technology has the potential to change the current password-dominated authentication landscape, research to date has not led to practical solutions that transcend to our daily lives. Motivated to find out how to improve adaptive authentication design, we provide a structured survey of the existing literature to date and analyze it to identify and discuss current research challenges and future directions.

The Internet of Things will bring a scenario in which interaction between humans and devices will be critical to allow people to use, monitor or configure Internet of Things devices. Interactions in such applications are based on traditional graphical interfaces. Devices that accept interaction based on Natural Language, e.g., through voice commands, can understand basic human orders or answering questions whenever user expressions fit into a known language pattern. Some devices can understand natural language voice commands but require sophisticated voice assistants located in the cloud, which raises significant privacy concerns. Others devices which handle voice-processing locally can perform a very limited local recognition system, requiring users to be familiar with words the system can process. The purpose of this work is to diminish the complexity of Natural Language processing in the context of IoT. The solution posited in this article allows Internet of Things devices to offload Natural Language processing to a system that improves the use of Natural Language and alleviates the need to learn or remember specific words or terms intended for triggering device actions. We have evaluated the feasibility of the design with a proof-of-concept implemented in a home environment and it was tested by real users.

Collaborative healthcare environments offer potential benefits, including enhancing the healthcare quality delivered to patients and reducing costs. As a direct consequence, sharing of electronic health records (EHRs) among healthcare providers has experienced a noteworthy growth in the last years, since it enables physicians to remotely monitor patients' health and enables individuals to manage their own health data more easily. However, these scenarios face significant challenges regarding security and privacy of the extremely sensitive information contained in EHRs. Thus, a flexible, efficient, and standards-based solution is indispensable to guarantee selective identity information disclosure and preserve patient's privacy. We propose a privacy-aware profile management approach that empowers the patient role, enabling him to bring together various healthcare providers as well as user-generated claims into an unique credential. User profiles are represented through an adaptive Merkle Tree, for which we formalize the underlying mathematical model. Furthermore, performance of the proposed solution is empirically validated through simulation experiments.

Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.