Huy Kang Kim

Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional underground actors; however previous studies overlooked such information as a feature in detecting and classifying malware and in attributing malware to creators. Guided by this insight, we propose a method to improve the performance of Android malware detection by incorporating the creator'

Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle's control component. Prior studies proposed various fault detection approaches leveraging heuristics-based rules and supervised learning-based models, but there were several drawbacks. The rule-based approaches required an engineer to update the rules on every type of fault, and the supervised learning-based approaches necessitated the acquisition of a

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMO

Industrial control systems (ICSs) used to be operated in closed networks, that is, separated physically from the Internet and corporate networks, and independent protocols were used for each manufacturer. Thus, their operation was relatively safe from cyberattacks. However, with advances in recent technologies, such as big data and internet of things, companies have been trying to use data generated from the ICS environment to improve production yield and minimize process downtime. Thus, ICSs ar

As intelligent mobile devices become more popular, security threats targeting them are increasing. The resource constraints of mobile devices, such as battery life and computing power, however, make it harder to handle such threats effectively. The existing physical and behavioural biometric identification methods - looked upon as good alternatives - are unsuitable for the current mobile environment. This paper proposes a specially designed biometric identification method for intelligent mobile

The electric power system infrastructure has begun to adopt digital information technologies such as transmission control protocol/internet protocol (TCP/IP) and Ethernet infrastructures. With this adoption, information technology-centric network and system management (NSM) tools are used to manage the intermediate communication systems and electric devices in digital substations. However, the technology to monitor the cyber–physical System (CPS) statistics for the intelligent electronic devices