IoT devices are increasingly present, both in the industry and in consumer markets, but their security remains weak, which leads to an unprecedented number of attacks against them. In order to reduce the attack surface, one approach is to analyze the binary code of these devices to early detect whether they contain potential security vulnerabilities. More specifically, knowing some vulnerable function, we can determine whether the firmware of an IoT device contains some security flaw by searching for this function. However, searching for similar vulnerable functions is in general challenging due to the fact that the source code is often not openly available and that it can be compiled for different architectures, using different compilers and compilation settings. In order to handle these varying settings, we can compare the similarity between the graph embeddings derived from the binary functions. In this paper, inspired by the recent advances in deep learning, we propose a new method – GESS (graph embeddings for similarity search) – to derive graph embeddings, and we compare it with various state-of-the-art methods. Our empirical evaluation shows that GESS reaches an AUC of 0.979, thereby outperforming the best known approach. Furthermore, for a fixed low false positive rate, GESS provides a true positive rate (or recall) about 36% higher than the best previous approach. Finally, for a large search space, GESS provides a recall between 50% and 60% higher than the best previous approach.

Today's radio frequency (RF) spectrum measurements are mainly performed by governmental agencies which drive around using bulky and expensive specialized hardware. This approach does not scale well, providing us with only a poor situational awareness of the actual RF spectrum usage around us. We have developed a wideband spectrum monitoring sensor for remote operation that builds upon portable and low-cost commercial off-the-shelf (COTS) hardware components with a total cost per sensor device below $100. This results in a stunning cost reduction factor of 50 to 500 comparing to professional equipment. Our sensor platform adopts the software-defined radio paradigm and performs all signal processing steps on the CPU and GPU of a low-cost single-board computer. We address the challenges of large frequency errors and long scanning times due to the hardware constraints by proposing new correction and optimization methods, providing a satisfactory level of accuracy in indoor and outdoor environments. Our remote sensing platform is envisioned to be used at larger scale for various applications such as dynamic spectrum access in cognitive radios, detecting regions with elevated electro-smog, or for policy enforcement in the electromagnetic space.

Today's spectrum measurements are mainly performed by governmental agencies which drive around using expensive specialized hardware. The idea of crowdsourcing spectrum monitoring has recently gained attention as an alternative way to capture the usage of wide portions of the wireless spectrum at larger geographical and time scales. To support this vision, we develop a flexible software-defined sensor architecture that enables distributed data collection in real-time over the Internet. Our sensor design builds upon low-cost commercial off-the-shelf (COTS) hardware components with a total cost per sensor device below $100. The low-cost nature of our sensor platform makes the sensing approach particularly suitable for large-scale deployments but imposes technical challenges regarding performance and quality. To circumvent the limits of our solution, we have implemented and evaluated different sensing strategies and noise reduction techniques. Our results suggest that our sensor architecture may be useful in application areas such as dynamic spectrum access in cognitive radios, detecting regions with elevated electro-smog, or simply to gain an understanding of the spectrum usage for advanced signal intelligence such as anomaly detection or policy enforcement.