The proliferation of embedded systems within the Internet of Things (IoT) has heightened the difficulty of detecting vulnerabilities due to their inherent resource constraints. This paper introduces GJALLARHORN, a framework extending electromagnetic side-channel analysis (EM SCA) for early-stage vulnerability detection in embedded systems. Unlike conventional methods requiring code access or imposing computational overhead, GJALLARHORN non-invasively analyses EM emissions to identify anomalous patterns indicating potential security vulnerabilities. By observing hardware-level manifestations of software execution, GJALLARHORN complements software-level analysis, revealing vulnerabilities that might otherwise remain undetected. The framework adapts to device complexity, enabling categorisation of up to 16 distinct vulnerability types, including buffer overflows, memory leaks, and arithmetic errors. Evaluations on both low-end (STM NUCLEO-144) and high-end (Raspberry Pi 3B) architectures demonstrate GJALLARHORN’s effectiveness, achieving a recall of 95.94% and F 1 score of 96.39% on the low-end system, and 73.33% recall with 84.61% F 1 score on the high-end system. Our results reveal that memory-related vulnerabilities produce more distinguishable EM signatures than arithmetic errors, offering valuable insights for externally detecting vulnerabilities. By enabling detection during development, GJALLARHORN helps mitigate risks before deployment, potentially reducing the economic impact of security incidents in IoT infrastructure.

Fuzzing is nowadays one of the most widely used bug hunting techniques. By automatically generating malformed inputs, fuzzing aims to trigger unwanted behavior on its target. While fuzzing research has matured considerably in the last years, the evaluation and comparison of different fuzzing proposals remain challenging, as no standard set of metrics, data, or experimental conditions exist to allow such observation. This paper aims to fill that gap by proposing a standard set of features to allow such comparison. For that end, it first reviews the existing evaluation methods in the literature and discusses all existing metrics by evaluating seven fuzzers under identical experimental conditions. After examining the obtained results, it recommends a set of practices –particularly on the metrics to be used–, to allow proper comparison between different fuzzing proposals.

With a growing number of embedded devices that create, transform, and send data autonomously at its core, the Internet of Things (IoT) is a reality in different sectors, such as manufacturing, healthcare, or transportation. With this expansion, the IoT is becoming more present in critical environments, where security is paramount. Infamous attacks, such as Mirai, have shown the insecurity of the devices that power the IoT, as well as the potential of such large-scale attacks. Therefore, it is important to secure these embedded systems that form the backbone of the IoT. However, the particular nature of these devices and their resource constraints mean that the most cost-effective manner of securing these devices is to secure them before they are deployed, by minimizing the number of vulnerabilities they ship. To this end, fuzzing has proved itself as a valuable technique for automated vulnerability finding, where specially crafted inputs are fed to programs in order to trigger vulnerabilities and crash the system. In this survey, we link the world of embedded IoT devices and fuzzing. For this end, we list the particularities of the embedded world as far as security is concerned, we perform a literature review on fuzzing techniques and proposals, studying their applicability to embedded IoT devices and, finally, we present future research directions by pointing out the gaps identified in the review.