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revention of Malware Installation in Dedicated Devices Built on General-Purpose Execution Environments
Doyeon Kim, Jione Choi, Kiseok Jeon, Wonjun Lee, Junghee Lee
http://doi.org/10.5626/JOK.2025.52.5.444
With digitalization of various industries, the demand for dedicated devices is increasing. Dedicated devices, such as digital banking branches, medical tablets, and educational tablets, are designed to perform specific tasks. Since they only run designated applications, they are them more secure with minimal the attack surface. Most of these devices are built on general-purpose execution environments like Android. Thus, they offer ease of development, usability, and high availability, contributing to their widespread adoption. At the same time, they may introduce new security vulnerabilities, necessitating security measures tailored to dedicated devices. This study analyed the vulnerabilities of dedicated devices operating in a general-purpose execution environment, evaluated the potential for vulnerabilities that could lead to malware installation, and proposed countermeasures. This research assumes that attackers do not have physical access to the device and that end users do not engage in malicious activities. The widely used Android environment was selected. Ten methods by which an attacker could remotely install malware on a Lenovo P11 device were identified. To mitigate these threats, a security mechanism optimized for dedicated devices was designed by implementing SELinux policies and installing a file integrity verification program.
Network-level Tracker Detection Using Features of Encrypted Traffic
Dongkeun Lee, Minwoo Joo, Wonjun Lee
http://doi.org/10.5626/JOK.2022.49.4.314
Third-party trackers breach users’ data privacy by compiling large amounts of personal data such as location or browsing history through web tracking techniques. Although previous research has proposed several methods to protect the users from web tracking via its detection and blockage, their effectiveness is limited in terms of dependency or performance. To this end, this paper proposes a novel approach to detect trackers at the network level using features of encrypted traffic. The proposed method first builds a classification model based on the features extracted from side-channel information of encrypted traffic generated by trackers. It then prevents leakage of user information by accurately detecting tracker traffic within the network independently from the user’s browsers or devices. We validate the feasibility of utilizing features of encrypted traffic in tracker detection by studying the distinctive characteristics of tracker traffic derived from real-world encrypted traffic analysis.
UHF RFID Tag Identification Method Based on Physical-layer Features of Backscatter Networks
Yoonseo Kim, Hoorin Park, Minwoo Joo, Wonjun Lee
http://doi.org/10.5626/JOK.2021.48.9.1061
Radio-frequency identification (RFID) systems are becoming essential components in Internet of Things (IoT) networks by virtue of their cost and energy efficiency. Especially, in ultra high frequency (UHF) RFID systems, the process of identifying individual tags is crucial because different tags communicate in a passive manner. However, the tag identifiers used in existing systems are vulnerable to be replicated or predicted due to limited tag operation resources and memory, which leads to severe security threats. In this paper, we propose a technology to extract the unique physical-layer characteristics, which are difficult to be forged, and utilize them for tag identification. The proposed method consists of a fingerprint extraction algorithm to obtain the physical-layer features of time interval error and phase by analyzing the backscatter signals of the tags, and a tag identification algorithm to distinguish tags based on their extracted fingerprints. We provide a model of backscatter signals and analyze the identification accuracy of the proposed method with varying signal-to-noise ratios.
HTTP/3 Stream Prioritization based on Web Object Dependency
http://doi.org/10.5626/JOK.2021.48.7.850
HTTP/3 is an application layer protocol that includes new features to meet the needs of the modern web. IETF standardization of HTTP/3 has come to its final stage. HTTP/3 provides transport layer level stream multiplexing and accordingly it has encountered stream prioritization problem. The problem states the determination of which stream to transmit amongst multiple streams on a connection within limited network resources and this contributes to the completion time of web object loading. Meanwhile, dependency relationship between web activities exists and this implies that dependency relationship between web object loading activities also exists. In order to transfer web objects in accordance with the web page load process at the browser, we proposed a HTTP/3 stream prioritization scheme based on web object dependency. Particularly, we conducted the evaluation on a browser-based testbed we built rather than on HTTP/3 library. The proposed prioritization scheme was evaluated using the testbed and it was shown that the application of the scheme could improve the user’s experience.
Deep Reinforcement Learning based Multipath Packet Scheduling
Minwoo Joo, Wonwoo Jang, Wonjun Lee
http://doi.org/10.5626/JOK.2019.46.7.714
Packet scheduling in multipath environments deals with the determination of the manner of distribution of data traffic over multiple network paths and is considered as one of the significant factors affecting the multipath transport performance. However, existing algorithms for packet scheduling rely on particular metrics, which leads to limited performance under dynamic network conditions. In this paper, we propose a deep reinforcement learning (DRL) based packet scheduler with an ability to adapt to dynamic network changes. We have designed a DRL model to automatically capture and discover the network states and effects from the scheduling decisions. The proposed packet scheduler is implemented based on a multipath extension of the Quick UDP Internet Connections (QUIC) network stack and evaluated through network emulation to verify the potential of autonomous networking.
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