Sungkyunkwan University · 情報科学
Professor Hyoungshick Kim's research lab specializes in network science and cybersecurity, focusing on dynamic and resilient network systems. The lab investigates models for information diffusion, anomaly detection in industrial and IoT environments, and secure authentication frameworks for emerging technologies like drones and the Internet of Drones. A central theme is developing lightweight, efficient, and scalable solutions for real-world deployment under security and performance constraints. The lab also explores strategic network attacks and defenses with cost-aware models to enhance system resilience.
Figures are computed from collected data and may differ slightly.
Many networks are dynamic in that their topology changes rapidly--on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered gra
Extensive use of unmanned aerial vehicles (commonly referred to as a “drone”) has posed security and safety challenges. To mitigate security threats caused by flights of unauthorized drones, we present a framework called SENTINEL (Secure and Efficient autheNTIcation for uNmanned aErial vehicLes) under the Internet of Drones (IoD) infrastructure. SENTINEL is specifically designed to minimize the computational and traffic overheads caused by certificate exchanges and asymmetric cryptography comput
Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to confusion about choosing the best model in a real-world situation. In other words, there still needs to be a comprehensive comparison of state-of-the-art anomaly detection models with common experime
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. Moreover, IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers. As IoT ecosystems expand, the conv
The problem of maximizing information diffusion through a network is a topic of considerable recent interest. A conventional problem is to select a set of any arbitrary k nodes as the initial influenced nodes so that they can effectively disseminate the information to the rest of the network. However, this model is usually unrealistic in online social networks since we cannot typically choose arbitrary nodes in the network as the initial influenced nodes. From the point of view of an individual
Models of conflict in networks provide insights into applications ranging from epidemiology to guerilla warfare. Barabási, Albert, and Jeong modeled selective attacks on networks in which an attacker targets high-order nodes to destroy connectivity; Nagaraja and Anderson extended this to iterated attacks where the attacker and defender take turns removing and rebuilding nodes and edges according to given strategies. We extend the iterative model by introducing the cost required to perform networ
Instant messaging applications store users' personal data (e.g., user profile, chat messages, photos and video clips). Because those data typically include privacy sensitive information, most instant messaging applications are trying to protect the stored data in an encrypted form so that the authorized messaging application itself can only access the data. In this paper, we analyzed the locations and file formats of personal data files in three instant messaging applications (KakaoTalk, NateOn,
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