京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第25466号 / 地環博第252号 / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 宇佐美 誠, 准教授 TRENCHER Gregory, 教授 竹内 憲司 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

This study concerns the dynamics of Chinese strategic minerals policy and its effects on Zambian copper mining between 2016-2020. The essay employs a complementary—theories congruence analysis using Resource Security Theory, Debt Trap Diplomacy, and the Pollution Haven Hypothesis in order to analyse China’s actions and ascertain their effects on Zambia. The study found that China has a large presence within Zambian copper mining through the use of state-owned enterprises, aiding China in its goal of supplying domestic copper demand, and thus addressing resource security. These state-owned enterprises act within the Belt and Road Initiative framework. China is not using Debt Trap Diplomacy in Zambia, but may be able to in the future. China’s presence is negative for the Zambian environment. The moving of mining from China to Zambia has a positive impact on the Chinese environment.Chinese state-owned enterprises are far less stringent in abiding by environmental laws in Zambia than in China. The study shows that Resource Security Theory and the Pollution Haven Hypothesis hold strong explanatory value for the case of China in Zambian copper mining. Debt Trap Diplomacy proved a weaker theory, as natural resources have not been exchanged for debt write-off.

Smartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All which are currently hidden in a large number of benign apps in official Android markets, such as Google PlayStore, and Amazon. Ransomware is a malware that once infected the victim’s device. It will encrypt files, unlock device system, and display a popup message which asks the victim to pay ransom in order to unlock their device or system which may include medical devices that connect through the internet. In this research, we propose to combine permission and API calls, then use Deep Learning techniques to detect ransomware apps from the Android market. Permissions setting and API calls are extracted from each app file by using a python library called AndroGuard. We are using Permissions and API call features to characterize each application, which can identify which application has potential to be ransomware or is benign. We implement our Android Ransomware Detection framework based on Keras, which uses MLP with back-propagation and a supervised algorithm. We used our method with experiments based on real-world applications with over 2000 benign applications and 1000 ransomware applications. The dataset came from ARGUS’s lab [1] which validated algorithm performance and selected the best architecture for the multi-layer perceptron (MLP) by trained our dataset with 6 various of MLP structures. Our experiments and validations show that the MLPs have over 3 hidden layers with medium sized of neurons achieved good results on both accuracy and AUC score of 98%. The worst score is approximately 45% to 60% and are from MLPs that have 2 hidden layers with large number of neurons. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

京都大学 / 新制・課程博士 / 博士(エネルギー科学) / 甲第23291号 / エネ博第416号 / 新制||エネ||79(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻 / (主査)教授 手塚 哲央, 教授 黒崎 健, 准教授 MCLELLAN Benjamin / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DFAM