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Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a contextmanipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniquesfeatures-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases. arXiv:1511.00905v1 [cs.CR] 3 Nov 2015 1 ch 2 CP CV Prover (P) (Unattended) Verifier (V)

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The Internet of Things (IoT) offers an incredible innovation potential for developing smarter applications and services. However, today we see solutions in the development of vertical applications and services reflecting what used to be the early days of the Web, leading to fragmentation and intra-nets of Things. To achieve an open IoT ecosystem of systems and platforms, several key enablers are needed for effective, adaptive and scalable mechanisms for exploring and discovering IoT resources and their data/capabilities. This paper discusses our work in the EU H2020 IoTCrawler project. Its focus is on the integration and interoperability across different platforms, through dynamic and reconfigurable solutions for discovery and integration of data and services from legacy and new systems. This is complemented with adaptive, privacy-aware and secure solutions for crawling, indexing and searching in distributed IoT systems. IoTCrawler targets IoT development and demonstrations with a focus on Industry 4.0, Social IoT, Smart City and Smart Energy use cases.

Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.