With the advancement of technology, Unmanned Aerial Vehicles (UAVs), also known as drones, are being used in numerous applications. However, the illegal use of UAVs, such as in terrorism and spycams, has also increased, which has led to active research on anti-drone methods. Various anti-drone methods have been proposed over time; however, the most representative method is to apply intentional electromagnetic interference to drones, especially to their sensor modules. In this paper, we review various studies on the effect of intentional electromagnetic interference (IEMI) on the sensor modules. Various studies on IEMI sources are reviewed and classified on the basis of the power level, information needed, and frequency. To demonstrate the application of drone-sensor modules, major sensor modules used in drones are briefly introduced, and the setup and results of the IEMI experiment performed on them are described. Finally, we discuss the effectiveness and limitations of the proposed methods and present perspectives for further research necessary for the actual application of anti-drone technology.

This paper investigates the electromagnetic interference characteristics of the transition-minimized differential signaling (TMDS) scheme, which is a well-known technology of rapid serial data transmission, from a radio communication perspective. Such scrutiny regarding the leaking phenomenon inspires a pseudo model of the compromising signal model, which can simultaneously consider behavioral features of the software-defined radio with a simple RF front-end measuring the leakage. In this work, a conceptual explanation with mathematical formulations in implementing the pseudo model has been presented. Subsequently, by merging the model with various extra noises being in nature, the model can be utilized to facilitate and quantify the possibility of information extraction from the defected electromagnetic signatures. Furthermore, it is interesting to note that there are asynchronous problems due to inevitable timing errors in video display devices, even with a sophisticated acquisition system, and it turns out to be fatal for the frame-averaging scheme conducted before the signal demodulation. In view of these challenges, we have formulated a synchronizing scheme and verified the validity by utilizing the pseudo model with the extra noise to take into account the asynchronous problem. Moreover, the structural similarity (SSIM), a function of the signal-to-noise ratio, can provide the number of frames for the frame-averaging process and eventually give the minimum acquisition time to earn meaningful information from the compromising emanation. Finally, after the appropriate post-processing, the extracted information from the actual measurements is compared with the reconstruction from the pseudo-model results revealing excellent agreements.

This paper proposes a method to reconstruct an original video display from electromagnetic leakage by using the characteristics of digital video interfaces (e.g., digital visual interface (DVI) and high-definition multimedia interface (HDMI)). Moreover, it is proven that the analysis is applicable through a very narrow multiband pass filter when multiple video display units (VDUs) with identical video properties (resolution, refresh rate, etc.) are placed together and emanate simultaneously. The proposed method is verified with our reconstruction system and is expected to contribute to improving the security of video display signals by raising awareness regarding information leakage risks.

This study analyzes the phenomenon of electromagnetic (EM) leakage that occurs through cables and explores the potential for information forensics using deep learning-based image-processing algorithms. We focus on the transition-minimized differential signaling (TMDS) interface to analyze information leakage caused by the inherent differential signal synchronization errors in video graphics controllers (VGC). Our analysis includes detailed mathematical modeling of the EM leakage phenomena from the video display unit (VDU) interface that uses the TMDS protocol. Furthermore, this study presents mathematical models for distortions and alterations caused by the VDU characteristics and its associated RF front-end system. Utilizing mathematical models of EM phenomena, this paper presents a method for creating training datasets for deep learning-based signal processing algorithms by generating and augmenting pseudo leakage signals (PLS) that closely resemble actual leakage signals. This study confirms the practical utility of signal enhancement models trained with generated and augmented PLS in real-world scenarios. Validation involves applying the trained model to measured actual VDU leakage signals and evaluating the results using image quality metrics: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Ultimately, this study demonstrates the potential to develop deep learning models using theoretically generated PLS for VDU-targeted side-channel attacks, where collecting real training data poses a challenge. This suggests the potential for expanding into high-performance deep learning algorithms in future developments.

Analyzing unintentional electromagnetic (EM) emissions from contemporary devices remains a significant challenge due to the difficulty of identifying potential sources of vulnerability within modern integrated circuit design and the limited research on critical leakage points. These challenges are particularly pressing in today’s information-driven society, where such emissions pose substantial security risks. To address this issue, this paper focuses on spread spectrum clocking (SSC) schemes employed in information visualization devices (IVDs), offering an in-depth analysis of the diverse characteristics of EM leakage and highlighting their associated risks. To convey our contributions, we introduce a novel model based on a modified Fourier series that accurately captures SSC-induced EM waves, enhancing the understanding of emissions from SSC-based devices. Additionally, we propose complete coherent demodulation (CCD), an advancement of the periodic nonuniform sampling (PNS) framework that resolves byproduct-phase terms. Our work further integrates parameterized demodulation with singular value decomposition (PD-SVD) to refine the analysis of modulated instantaneous frequency terms within EM leakage observed over significant distances. These contributions advance security assessments and strengthen electromagnetic resilience.

This article treats an analysis of electromagnetic (EM) radiation signatures from devices employing spread spectrum clocking, primarily focusing on the widely used protocol named flat panel display link (FPD-link). FPD-link periodically changes its clock rate, radiating relatively broad bandwidth electromagnetic fields. This feature necessitates a different approach in the area of signal reconstruction using their EM signature. Previous research has relied on frame-averaging techniques to enhance the signal-to-noise ratio (SNR) in the presence of faint EM signal. However, coherent frame-averaging, once considered the most efficient method, is now physically unfeasible because of the clock periodicity variation, and accordingly, the only viable approach with SNR enhancement to some extent is incoherent frame-averaging providing definitely limited performance. In response to this challenge, the authors conduct a thorough analysis of the phenomena generated by spread spectrum clocking-induced EM signature, subsequently, proposes a circumventing scheme named as partial-coherent frame-averaging, aiming to partially overcome the limitations of unavailability of coherent synchronization. Finally, the effectiveness of proposed scheme is validated through multiple experiments, comparing it to incoherent frame-averaging method. This article contributes to a deeper understanding of signal reconstruction using EM signatures from spread spectrum clocking-based devices, and provides potential countermeasures based on their practicality.