Browsing by Author "Wang, Chia-Wei"

Now showing 1 - 8 of 8

Cloudebug: A Programmable Online Malware Testbed
(2014-07-01)
Wang, Chi-Wei
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Wang, Chia-Wei
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Hsieh, Chu-An
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Shieh, Shiuhpyng Winston
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資訊工程學系
Virtual machines for malware analysis in current cloud-based debugging services are not easily accessible or operable by end users. Cloudebug allows users to operate its VM directly in the cloud.
Combating Software Piracy in Public Clouds
(2015-10-01)
Wang, Chia-Wei
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Cho, Michael Cheng Yi
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Wang, Chi-Wei
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Shieh, Shiuhpyng Winston
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資訊工程學系
CodeMist is an innovative security framework that leverages both passive and active approaches to prevent piracy of cloud-based rental software.
Dynamic Power Management in Cognitive Radio Networks based on Constrained Stochastic Games
(2009)
Wang, Chia-Wei
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Hsu, Yu-Pin
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Feng, Kai-Ten
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
Recent studies have been conducted to indicate the ineffective usage of licensed bands due to the static spectrum allocation. In order to improve the spectrum utilization, the cognitive radio is therefore suggested to dynamically exploit the opportunistic primary frequency spectrums. The interference from the secondary users to the primary user consequently draws the attention to the spectrum and power management for the cognitive radio networks. In this paper, the constrained stochastic games are utilized to exploit the optimal policies for power management by considering the variations from both the channel gain and the primary traffic. Both the underlay and overlay waveforms are considered within the network scenarios for the proposed power management scheme. Constraints for allowable interferences will be applied in order to preserve the communication quality among the primary and the secondary users. According to the formulation of the constrained stochastic games, the existence of the constrained Nash equilibrium will be validated with rigorous proofs, which will be acquired as the optimal policies for the power management problem. Simulation results further validate the correctness of the theoretically-derived policies for dynamic power management.
ECOMSNet - An edge computing-based sensory network for real-time water level prediction and correction
(2020-09-01)
Yang, Tsun-Hua
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Wang, Chia-Wei
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Lin, Sheng-Jhe
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土木工程學系
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Department of Civil Engineering
Water level monitoring and forecasting are essential tasks in flood emergency response. This study proposes an Edge COMputing-based Sensory NETwork (ECOMSNet), an innovative decentralized early warning system (EWS), for water level monitoring and prediction. A sensor-embedded algorithm integrates the direct step method (DSM) with a microgenetic algorithm (MGA). This algorithm predicts the water surface profile and corrects it once water level observations are available. It also meets efficiency requirements to accommodate sensor computation limitations. The errors in the predicted water surface profiles in channels with gradually varied flows are 5% in a laboratory flume experiment and below 10% in a field experiment. The ECOMSNet is an achievement of edge computing-based Internet of Things. It shows potential to increase emergency response efficiency. However, the system requires further refinement and testing if it is to adequately address rapidly varied unsteady flow in a scaled-up implementation.
Identifying and Filling Occlusion Holes on Planar Surfaces for 3-D Scene Editing
(2017-01-01)
Wang, Chia-Wei
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Yang, Tzu-Chieh
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Chiang, Sheng-Ho
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Wang, Tsaipei
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交大名義發表
While it is very convenient to acquire 3-D models of indoor environments today, to edit such an environment is not as easy, as the moving of any object will likely result in exposed occlusion holes. This paper presents a method of automatically identifying and filling such occlusion holes on planar surfaces in 3-D scenes. Steps of hole extraction, classification, and filling using a 2-D image inpainting technique are discussed.
IoT Security: Ongoing Challenges and Research Opportunities
(2014-01-01)
Zhang, Zhi-Kai
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Cho, Michael Cheng Yi
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Wang, Chia-Wei
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Hsu, Chia-Wei
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Chen, Chong-Kuan
The Internet of Things (IoT) opens opportunities for wearable devices, home appliances, and software to share and communicate information on the Internet. Given that the shared data contains a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we begin with general information security background of IoT and continue on with information security related challenges that IoT will encountered. Finally, we will also point out research directions that could be the future work for the solutions to the security challenges that IoT encounters.
MrKIP: Rootkit Recognition With Kernel Function Invocation Pattern
(2015-03-01)
Wang, Chi-Wei
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Chen, Chong-Kuan
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Wang, Chia-Wei
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Shieh, Shiuhpyng Winston
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資訊工程學系
Existing mechanisms tracing user-level activities such as system calls and APIs can be circumvented by the kernel-level rootkits. In this paper, a novel system, MrKIP, is proposed to recognize rootkits based on their kernel-level activities. Our scheme semiautomatically generates suitable locations for analysts to implement checkpoints, which are used to profile kernel-space activities. Then, collected rootkits are executed in an emulator with these checkpoints for behavior profiling. The collected behaviors are clustered and used for model construction. The constructed model can be used to recognize new variants of rootkit families. Our scheme differs from conventional tracers due to its ability to cover kernel-space malware and the whole-system scope. In addition, monitoring at the kernel level raises high barrier for malware to evade, since all tasks are eventually executed through the basic kernel functions.
ProbeBuilder: Uncovering Opaque Kernel Data Structures for Automatic Probe Construction
(2016-09-10)
Wang, Chi-Wei
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Wang, Chia-Wei
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Shieh, Shiuhpyng
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資訊工程學系
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Department of Computer Science
VM-based inspection tools generally implement probes in the hypervisor to monitor events and the state of kernel of the guest system. The most important function of a probe is to carve information of interest out of the memory of the guest when it is triggered. Implementing probes for a closed-source OS demands manually reverse-engineering the undocumented code/data structures in the kernel binary image. Furthermore, the reverse-engineering result is often non-reusable between OS versions or even kernel updates due to the rapid change of these structures. In this paper, we propose ProbeBuilder, a system automating the process to inference kernel data structures. Based on dynamic execution, ProbeBuilder searches for data structures matching the "pointer-offset-pointer" pattern in guest memory. The sequences of these offsets, which are referred to as dereferences, are then verified by ProbeBuilder with instruction evidence that traverse them. The experiment on Windows kernel shows that ProbeBuilder efficiently narrows thousands of choices for kernel-level probes down to dozens. The finding allows analysts to quickly implement probes, facilitating rapid development/update of inspection tools for different OSes. With these features, ProbeBuilder is the first system capable of automatically generating practical probes that extracts information through dereferences to opaque kernel data structures.