IoT devices are present in many, especially corporate and sensitive, networks and regularly introduce security risks due to slow vendor responses to vulnerabilities and high difficulty of patching. In this paper, we want to evaluate to what extent the development of future risk of IoT devices due to new and unpatched vulnerabilities can be predicted based on historic information. For this analysis, we build on existing prediction algorithms available in the SAFER framework (prophet and ARIMA) which we evaluate by means of a large data-set of vulnerabilities and patches from 793 IoT devices. Our analysis shows that the SAFER framework can predict a correct future risk for 91% of the devices, demonstrating its applicability. We conclude that this approach is a reliable means for network operators to efficiently detect and act on risks emanating from IoT devices in their networks.

Internet of Things (IoT) devices are becoming more widespread not only in areas such as smart homes and smart cities but also in research and office environments. The sheer number, heterogeneity, and limited patch availability provide significant challenges for the security of both office networks and the Internet in general. The systematic estimation of device risks, which is essential for mitigation decisions, is currently a skill-intensive task that requires expertise in network vulnerability scanning, as well as manual effort in firmware binary analysis. This article introduces SAFER,1 the Security Assessment Framework for Embedded-device Risks, which enables a semi-automated risk assessment of IoT devices in any network. SAFER combines information from network device identification and automated firmware analysis to estimate the current risk associated with the device. Based on past vulnerability data and vendor patch intervals for device models, SAFER extrapolates those observations into the future using different automatically parameterized prediction models. Based on that, SAFER also estimates an indicator for future security risks. This enables users to be aware of devices exposing high risks in the future. One major strength of SAFER over other approaches is its scalability, achieved through significant automation. To demonstrate this strength, we apply SAFER in the network of a large multinational organization, to systematically assess the security level of hundreds of IoT devices on large-scale networks. Results indicate that SAFER successfully identified 531 out of 572 devices leading to a device identification rate of 92.83 %, analyzed 825 firmware images, and predicted the current and future security risk for 240 devices.

Users of Internet of Things (IoT) devices are often unaware of their security risks and cannot sufficiently factor security considerations into their device selection. This puts networks, infrastructure and users at risk. We developed and evaluated SAFER, an IoT device risk assessment framework designed to improve users' ability to assess the security of connected devices. We deployed SAFER in a large multinational organization that permits use of private devices. To evaluate the framework, we conducted a mixed-method study with 20 employees. Our findings suggest that SAFER increases users' awareness of security issues. It provides valuable advice and impacts device selection. Based on our findings, we discuss implications for the design of device risk assessment tools, with particular regard to the relationship between risk communication and user perceptions of device complexity.

The introduction of the Internet of Things (IoT), i.e., the interconnection of embedded devices over the Internet, has changed the world we live in from the way we measure, make calls, print information and even the way we get energy in our offices or homes. The convenience of IoT products, like closed circuit television (CCTV) cameras, internet protocol (IP) phones, and oscilloscopes, is overwhelming for end users. In parallel, however, security issues have emerged and it is essential for infrastructure providers to assess the associated security risks. In this paper, we propose a novel method to detect IoT devices and identify the manufacturer, device model, and the firmware version currently running on the device using the page source from the web user interface. We performed automatic scans of the large-scale network at the European Organization for Nuclear Research (CERN) to evaluate our approach. Our tools identified 233 IoT devices that fell into eleven distinct device categories and included 49 device models manufactured by 26 vendors from across the world.

The number of devices of the so-called Internet of Things (IoT) is heavily increasing. One of the main challenges for operators of large networks is to autonomously and automatically identify any IoT device within the network for the sake of computer security and, subsequently, being able to better protect and secure those. In this paper, we propose a novel approach to identify IoT devices based on the unchangeable IoT hardware setup through device specific clock behavior. One feature we use is the unavoidable fact that clocks experience "clock skew" , which results in running faster or slower than an exact clock. Clock skew along with twelve other clock related features are suitable for our approach, because we can measure these features remotely through TCP timestamps which many devices can add to their packets. We show that we are able to distinguish device models by Machine Learning only using these clock characteristics. We ensure that measurements of our approach do not stress a device or causes fault states at any time. We evaluated our approach in a large-scale real-world installation at the European Organization for Nuclear Research (CERN) and show that the above-mentioned methods let us identify IoT device models within the network.

This paper presents a single event burnout (SEB) sensitivity characterization for power MOSFETs, independent from tests, through a prediction model issued from TCAD analysis and the knowledge of device topology. The methodology is applied to a STRIPFET device and compared to proton data obtained at PSI, showing a good agreement in the order of magnitude of proton SEB cross section, and thus validating the prediction model as an alternative device characterization with respect to SEB.

CERN (European Organization for Nuclear Research) is an international particle physics laboratory located in Geneva, Switzerland. CERN carries out fundamental research in particle and nuclear physics using several large particle accelerators, including the Large Hadron Collider (CERN 2015).