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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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    Application Performance Profiling in Android Dalvik Virtual Machines
    (2016-05-01)
    Chang, Hung-Cheng
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    Chang, Kuei-Chung
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    Lin, Ying-Dar
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    Lai, Yuan-Cheng
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    資訊工程學系
    Most Android applications are written in JAVA and run on a Dalvik virtual machine. For smartphone vendors and users who wish to know the performance of an application on a particular smartphone but cannot obtain the source code, we propose a new technique, Dalvik Profiler for Applications (DPA), to profile an Android application on a Dalvik virtual machine without the support of source code. Within a Dalvik virtual machine, we determine the entry and exit locations of a method, log its execution time, and analyze the log to determine the performance of the application. Our experimental results show an error ratio of less than 5% from the baseline tool Traceview which instruments source code. The results also show some interesting behaviors of applications and smartphones: the performance of some smartphones with higher hardware specifications is 1.5 times less than the phones with lower specifications. DPA is now publicly available as an open source tool.
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    基於 Google Android 平台之智慧型設備系統及其應用
    (2008)
    袁賢銘
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    YUAN SHYAN-MING
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    國立交通大學資訊工程學系(所)
    手機硬體性能提升，電信業者的上網基礎建設日益成熟，智慧型手機已成為兵家必爭 之地。近年來，Symbian、Windows Mobile、RIM、iPhone 等智慧型手機平台的普及， 顯示高階智慧型手機的市場競爭激烈；由 Android 帶動的新一波開放性手機平台熱 潮，更對台灣產業昇級有很大的意義。 智慧型手機的成功，有很大的成份取決於搭載軟體服務的易用性。智慧型手機的用途， 除了傳統手機的電信功能（通話、簡訊、即時訊息）之外，還必須提供 PDA（個人數 位助理）的 PIM (個人資訊管理) 功能，更要以有創意的方式整合兩者，並開發新的使 用情境。然而目前智慧型手機所內建的軟體，不僅在 PIM 部份落後許多，更缺少整合 的設計。 本研究擬針對上述缺失，加上我們在分散式系統多年的研究及實作經驗，在開放原始 碼的 Google Android 平台上設計一套「Android 個人資訊與通訊管理整合系統」。 在「個人資訊管理」部份，本計畫將以 PIM 典型功能出發，設計出較 Android 內建 軟體更完整的通訊錄及行事曆服務。在「通訊管理」部份，本計畫將以 PIM 整合觀點 出發，將手機的通信功能（telephony、SMS、IM）依照人、時、事等屬性，與 PIM 的 通訊錄、行事曆等服務進行 seamless 的串接，以發揮智慧型手機該有的整體性，而不 是手機與 PDA 兩部門各自為政的下場。
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    惡意程式偵測與樣本資料庫之建置與研究
    (2011)
    林盈達
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    Lin Ying-Dar
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    國立交通大學資訊工程學系（所）
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    Calibrating parameters and formulas for process-level energy consumption profiling in smartphones
    (2014-09-01)
    Lin, Ying-Dar
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    Rattagan, Ekarat
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    Lai, Yuan-Cheng
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    Chang, Li-Pin
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    Yo, Yun-Chien
    Battery-powered mobile devices substantially constrain energy resources. Process-level energy profiling tools can identify the most energy-consuming process and detail the energy usage of hardware components. With the help of energy profiling tools, programmers can fine-tune the energy consumption of processes to extend battery lifetime. However, profiling tools are highly dependent on hardware and must be calibrated for each hardware platform. Furthermore, for any new hardware components, new energy estimation formulas must be created. To solve these two problems regarding off-the-shelf products, this work proposes a two-phase calibrating approach. The first phase reconstructs the power table with a power meter, while the second creates new energy estimation formulas using linear regression analysis. The accuracy of the calibrated tool was evaluated in five scenarios and its error ratio is proven to be below 10%, occasionally less than 5%. Hence, this proposed approach to energy consumption profiling represents a major step in off-the-shelf devices. (C) 2014 Elsevier Ltd. All rights reserved.
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    Identifying android malicious repackaged applications by thread-grained system call sequences
    (2013-11-01)
    Lin, Ying-Dar
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    Lai, Yuan-Cheng
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    Chen, Chien-Hung
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    Tsai, Hao-Chuan
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    資訊工程學系
    Android security has become highly desirable since adversaries can easily repackage malicious codes into various benign applications and spread these malicious repackaged applications (MRAs). Most MRA detection mechanisms on Android focus on detecting a specific family of MRAs or requiring the original benign application to compare with the malicious ones. This work proposes a new mechanism, SCSdroid (System Call Sequence Droid), which adopts the thread-grained system call sequences activated by applications. The concept is that even if MRAs can be camouflaged as benign applications, their malicious behavior would still appear in the system call sequences. SCSdroid extracts the truly malicious common subsequences from the system call sequence 6 of MRAs belonging to the same family. Therefore, these extracted common subsequences can be used to identify any evaluated application without requiring the original benign application. Experimental results show that SCSdroid falsely detected only two applications among 100 evaluated benign applications, and falsely detected only one application among 49 evaluated malicious applications. As a result, SCSdroid achieved up to 95.97% detection accuracy, i.e., 143 correct detections among 149 applications. (C) 2013 Elsevier Ltd. All rights reserved.
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    Booting, browsing and streaming time profiling, and bottleneck analysis on android-based systems
    (2013-07-01)
    Lin, Ying-Dar
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    Ho, Cheng-Yuan
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    Lai, Yuan-Cheng
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    Du, Tzu-Hsiung
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    Chang, Shun-Lee
    Android-based systems perform slowly in three scenarios: booting, browsing, and streaming. Time profiling on Android devices involves three unique constraints: (1) the execution flow of a scenario invokes multiple software layers, (2) these software layers are implemented in different programming languages, and (3) log space is limited. To compensate for the first and second constraints, we assumed a staged approach using different profiling tools applied to different layers and programming languages. As for the last constraint and to avoid generating enormous quantities of irrelevant log data, we began profiling scenarios from an individual module, and then iteratively profiled an increased number of modules and layers, and finally consolidated the logs from different layers to identify bottlenecks. Because of this iteration, we called this approach a staged iterative instrumentation approach. To analyze the time required to boot the devices, we conducted experiments using off-the-shelf Android products. We determined that 72% of the booting time was spent initializing the user-space environment, with 44.4% and 39.2% required to start Android services and managers, and preload Java classes and resources, respectively. Results from analyzing browsing performance indicate that networking is the most significant factor, accounting for at least 90% of the delay in browsing. With regard to online streaming, networking and decoding technologies are two most important factors occupying 77% of the time required to prepare a 22 MB video file over a Wi-Fi connection. Furthermore, the overhead of this approach is low. For example, the overhead of CPU loading is about 5% in the browsing scenario. We believe that this proposed approach to time profiling represents a major step in the optimization and future development of Android-based devices. (c) 2013 Elsevier Ltd. All rights reserved.
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    Connect6 Programs on Mobile Devices
    (2012)
    Zheng, Ji-Hong
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    Hu, Chia-Yun
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    Wu, I-Chen
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    Tseng, Wen-Jie
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    Wei, Ching-Hsuan
    In this demonstration, we port our current Connect6 program, NCTU6, to mobile devices, such as iOS devices, Android devices. However, computing powers of mobile devices are also relatively weaker when compared with high-end machines. For example, the CPU speeds of mobile devices are about 1/5 to 1/8 of those of desktops; and the memory of mobile devices is usually limited to several megabytes. In this demonstration, we tune the program NCTU6 to fit in mobile devices, while maintaining reasonable strength. The program also won the silver medal in TCGA 2012. In addition, we also support Connect6 puzzles in the program.