Radio-frequency identification (RFID) system, successfully adopted in many industrial applications, suffers from security issues due to the inherent weakness of wireless communication, such as eavesdropping, replay attack, impersonation attack, and traceability issues. A lot of research efforts based on cryptographic primitives have been conducted in a decade, however, most of the existing security protocols depending on cryptosystems are not feasible to be applied due to the minimalist design of passive tags. A lightweight cryptographic authentication is one of the practical solutions, but it has traceability issues from physical layer information. The other approach is to use the properties of the physical layer of RFID systems. However, since the physical-layer characteristics cannot be intentionally updated, they are vulnerable to situations where an adversary actively obtains authentic data for traceability attacks or replay attacks. Therefore, to resist the security threats, we propose Tagora, a cross-layer authentication protocol, which is the first integration work of two-layer approaches that harness the unpredictable properties of tag's collision responses at both the physical and application layers. Our protocol design is composed of a collision recovery algorithm with a random offset scheme and phase encryption in the physical layer, and authentication process based on a challenge-response mechanism in the application layer. We evaluate Tagora in terms of the untraceability and reliability, and also provide security analysis on how Tagora can defend against plausible attacks while meeting security requirements.

Recent studies figure out that hidden eavesdroppers can be detected through RF (radio frequency) leakage from their local oscillators. Inspired by this finding, this letter presents Phantom Eavesdropping, an emerging wireless signal eavesdropping technique which renders hidden eavesdroppers immune to the RF leakage-based detection to further conceal their presence. At its heart is a dynamic alteration process regarding the local oscillator's oscillation frequency. As a result, the RF leakage from the local oscillator is whitened in the frequency domain and thus the corresponding eavesdropper becomes footprint-less. Practical experiment shows that Phantom Eavesdropping does not affect normal Wi-Fi eavesdropping while further rendering eavesdroppers transparent to eavesdropper detectors.

This letter presents detect Wi-Fi signals out of ZigBee (DeWOZ), a clean-slate technique to discern Wi-Fi packets drowning in ZigBee interference. Unlike existing solutions that are restricted to the cases where Wi-Fi signal strength is much higher than ZigBee, Wi-Fi signals only interfere with ZigBee payload, or beforehand clear Wi-Fi transmissions are done to obtain transmitter-specific information, DeWOZ aims at achieving the detection of relatively weak Wi-Fi signal hidden in both ZigBee header and payload portions only based on a single corrupted signal. DeWOZ re-explores the Wi-Fi-oriented Schmidl-Cox algorithm for corrupted signal synchronization followed by two key operations, autocorrelation anomaly detection and adaptive discrete Fourier transform, to sniff Wi-Fi out of ZigBee header and payload, respectively. The feasibility of DeWOZ is validated via practical experiment.

The ever-increasing deployment of Wi-Fi and ZigBee networks aggravates cross-technology interference (CTI) in the 2.4 GHz unlicensed spectrum. This letter presents a clean-slate technique, DOTA, to combat the CTI and enable ZigBee/Wi-Fi co-transmission. Unlike existing solutions merely handling Wi-Fi-to-ZigBee interference, DOTA accounts for the true sense of CTI wherein ZigBee and Wi-Fi interfere with each other. At the heart of DOTA lies two operations, signal polishing and pulse disambiguation, for decomposing and threading signal samples to recover contaminated ZigBee and Wi-Fi signals. Experiment results validate the feasibility of superiority of DOTA.

Radio-frequency identification (RFID) system, successfully adopted in many industrial applications, suffers from security issues due to the inherent weakness of wireless communication, such as eavesdropping, replay attack, impersonation attack, and traceability issues. A lot of research efforts based on cryptographic primitives have been conducted in a decade, however, most of the existing security protocols depending on cryptosystems are not feasible to be applied due to the minimalist design of passive tags. A lightweight cryptographic authentication is one of the practical solutions, but it has traceability issues from physical layer information. The other approach is to use the properties of the physical layer of RFID systems. However, since the physical-layer characteristics cannot be intentionally updated, they are vulnerable to situations where an adversary actively obtains authentic data for traceability attacks or replay attacks. Therefore, to resist the security threats, we propose Tagora, a cross-layer authentication protocol, which is the first integration work of two-layer approaches that harness the unpredictable properties of tag's collision responses at both the physical and application layers. Our protocol design is composed of a collision recovery algorithm with a random offset scheme and phase encryption in the physical layer, and authentication process based on a challenge-response mechanism in the application layer. We evaluate Tagora in terms of the untraceability and reliability, and also provide security analysis on how Tagora can defend against plausible attacks while meeting security requirements.

Recent studies figure out that hidden eavesdroppers can be detected through RF (radio frequency) leakage from their local oscillators. Inspired by this finding, this letter presents Phantom Eavesdropping, an emerging wireless signal eavesdropping technique which renders hidden eavesdroppers immune to the RF leakage-based detection to further conceal their presence. At its heart is a dynamic alteration process regarding the local oscillator's oscillation frequency. As a result, the RF leakage from the local oscillator is whitened in the frequency domain and thus the corresponding eavesdropper becomes footprint-less. Practical experiment shows that Phantom Eavesdropping does not affect normal Wi-Fi eavesdropping while further rendering eavesdroppers transparent to eavesdropper detectors.

This letter presents detect Wi-Fi signals out of ZigBee (DeWOZ), a clean-slate technique to discern Wi-Fi packets drowning in ZigBee interference. Unlike existing solutions that are restricted to the cases where Wi-Fi signal strength is much higher than ZigBee, Wi-Fi signals only interfere with ZigBee payload, or beforehand clear Wi-Fi transmissions are done to obtain transmitter-specific information, DeWOZ aims at achieving the detection of relatively weak Wi-Fi signal hidden in both ZigBee header and payload portions only based on a single corrupted signal. DeWOZ re-explores the Wi-Fi-oriented Schmidl-Cox algorithm for corrupted signal synchronization followed by two key operations, autocorrelation anomaly detection and adaptive discrete Fourier transform, to sniff Wi-Fi out of ZigBee header and payload, respectively. The feasibility of DeWOZ is validated via practical experiment.

The ever-increasing deployment of Wi-Fi and ZigBee networks aggravates cross-technology interference (CTI) in the 2.4 GHz unlicensed spectrum. This letter presents a clean-slate technique, DOTA, to combat the CTI and enable ZigBee/Wi-Fi co-transmission. Unlike existing solutions merely handling Wi-Fi-to-ZigBee interference, DOTA accounts for the true sense of CTI wherein ZigBee and Wi-Fi interfere with each other. At the heart of DOTA lies two operations, signal polishing and pulse disambiguation, for decomposing and threading signal samples to recover contaminated ZigBee and Wi-Fi signals. Experiment results validate the feasibility of superiority of DOTA.

Backscatter networks are widely applied in real world such as inventory management, RFID-enabled passport, and access control. The security issues rapidly grow in its importance with the widespread uses. Especially, if the access control application identifies a user using a message from a backscatter device, the message can be used for tracking or replay attacks. However, most of the existing security protocols depending on cryptosystems cannot be applied into the backscatter networks due to the minimalist design of backscatter devices. In order to alleviate the security menaces, we propose a novel concept of authentication scheme for backscatter networks. Our scheme uses an unpredictable collision by applying our own data structure called Shifted Counting Bloom Filter (SCBF). Based on the SCBF, the scheme can reduce the overhead of backscatter devices required for security. Our performance evaluation shows the information-theoretic secrecy and the reliability of our scheme.

Since the COVID-19 pandemic, the rise of video con-ferencing has introduced new information security challenges, particularly the unintentional leakage of sensitive information via reflected text on eyeglass surfaces captured by webcams. While prior studies have shown the possibility of webcam peeking attacks, they typically rely on manual analysis or costly equipment, limiting scalability. To address these challenges, we propose Optical Character Recognition (OCR) of Reflected text, called ORT, an automated attack model that extracts re-flected text using advanced OCR techniques. ORT is composed of customized preprocessing steps and optimized Scene Text Detection (STD) and Scene Text Recognition (STR) models, with the STD model retrained on a custom reflected text dataset to enhance performance under conditions such as blur, low contrast, and distortion. We evaluated ORT in various video conferencing environments, taking into account variables such as lighting, text size, background color, and the presence or absence of prescriptions for eyeglasses. The results show high recognition accuracy, demonstrating that ORT significantly reduces adversary effort and raises the threat level of webcam peeking attacks.

Quality of service (QoS) has evolved to ensure performance across various computing environments, focusing on data bandwidth, response time, throughput, and stability. Traditional QoS schemes primarily target DRAM-based homogeneous memory systems, exposing limitations when applied to diverse memory configurations. Moreover, the emergence of nonvolatile memories (NVMs) has made achieving QoS even more challenging due to their differing characteristics. While QoS schemes have been proposed for DRAM-based memory systems or hybrid memory systems combining DRAM and a single NVM type, there is a lack of research on QoS techniques for memory systems that incorporate multiple types of NVM simultaneously. Ensuring QoS in these heterogeneous memory environments is challenging due to significant differences in memory characteristics. In this paper, we propose a novel technique, dynamic affinity-based resource pairing (DARP), designed to enhance QoS in multicore heterogeneous memory systems. The proposed approach dynamically monitors the memory access patterns of applications and leverages the specific read/write characteristics of NVM devices. Detailed information from monitoring is used to optimally allocate memory data to the most suitable memory devices, ensuring stable memory response times and mitigating bottlenecks. Extensive experiments validate the efficiency and scalability of DARP across various workloads and heterogeneous memory configurations, including memory systems with multiple types of NVM. The results show that our technique significantly outperforms state-of-the-art QoS methods in terms of memory response time consistency and overall QoS in heterogeneous memory environments. DARP achieved a memory response time variability of 74.4% in six different memory configurations compared to the baseline on average, demonstrating its high scalability and effectiveness in enhancing QoS across various heterogeneous memory systems.

With the rise of cyber threats, identifying APT groups becomes increasingly crucial for enterprise security experts. This paper introduces a comprehensive framework for profiling APT groups, focusing on Lazarus and APT29. It underscores the critical role of malware hash unit profiling in contemporary cyber security efforts, aiming to fortify organizational defenses against evolving APT threats.

AI 추천 알고리즘의 부작용으로 인해 영상 플랫폼 사용자가 사고 영상 시청 시 트라우마가 유발될 우려가 있다. 그러나 사고 영상에 대한 규제 기준이 미흡하다. 본 논문에서는 YouTube와 같은 영상 플랫폼에서 트라우마를 유발할 수 있는 사고 영상을 효과적으로 탐지하고 규제하기 위해 네 가지 사고 카테고리를 통합한 검출 시스템을 제안한다. 또한 제안한 시스템에 적합하게 활용할 수 있는 CNN 기반 화재 및 침수 탐지 모델, two-stream 학습을 이용한 폭행탐지 모델, 그리고 YOLOv4를 기반으로 한 교통사고 탐지 기법을 구현하였다. 화재, 침수, 폭행 탐지 모델은 각각 95%, 96%, 97%의 정확도를, 교통사고 탐지 기법은 97.1%의 탐지율과 2.86%의 오탐률을 달성하였다. 본 연구는 기존 규제 항목이 아닌 사고 영상을 탐지하여 사용자 트라우마를 방지하고, 안전한 미디어 환경을 조성하는 데 기여할 것으로 기대한다.