Exploiting the Physical Environment for Securing the Internet of Things

Abstract

Using the randomness provided by the physical environment to build security solutions has received much attention recently. In particular, the shared entropy provided by measuring ambient audio, luminosity modalities or electromagnetic emanations has been used to build locationbased, proximity-based, or context-based security mechanisms. The majority of those protocols is based on a standard model consisting channel probing, quantization, information reconciliation, privacy amplification, and key verification. The main problem for almost all approaches is the limited understanding of the security that is provided. For example, security analyses often only address single components and not the entire system or are based on broad abstractions of the physical source of randomness. Further, a big open question is the feasibility of such systems for low-resource platforms. Our first contribution is a detailed, optimized realization of a key establishment system. We demonstrate the feasibility of deriving a shared secret from correlated quantities on resource-constrained devices with tight power budget. Our system was realized on the popular ARM Cortex-M3 processor that reports detailed resource requirements. The second major contribution is a summary and abstraction of previous works together with a detailed security analysis using attack trees. We substantiate our investigation by presenting practical attack results. CCS Concepts•Security and privacy → Key management; Mobile and wireless security; Security requirements; Formal security models; Embedded systems security; Usability in security and privacy; •Software and its engineering → Software prototyping;

Reconfigurable Intelligent Surface for Physical Layer Key Generation: Constructive or Destructive?

“…These steps are similar to existing works in the field of CRKG and are thus not particularly designed in this paper. We refer the interested reader to [25]- [28].…”

Performance Analysis of Loss Multilevel Quantization on the Secret Key Generation Scheme in Indoor Wireless Environment

“…Some of the parameters that determine c T are maximum Doppler shift D f as shown in Equation 6 . Probing channel mechanism affected by the probing rate is p r and the sampling rate is s r [32]. Probing rate is the interval between the probing request and reply, and the sampling rate is the interval between the probing requests with one another.…”

An Efficient Key Generation for the Internet of Things Based Synchronized Quantization

“…The Doppler Frequency f obtained is 9.6 GHz (the speed of movement multiplied by the carrier frequency divided by the speed of light) so the coherence time obtained is 104.2 ms (1/|f|). Randomness requirements can be fulfilled if the time interval tm exceeds coherence time [46] so we select time interval 110 ms. In this paper, there are 4000 RSS channel characteristics captured by each node.…”

“…The lower the computing time and the communication overhead required, the more efficient the built key generation system. There are 4 existing key generation systems that will be used for comparison, i.e., system [9,31,33,46,48]. The system [9] uses four phases where the RSS channel characteristics will be quantized using the cumulative distribution function (CDF) method.…”