Mobile Crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of the smart devices to improve the sensing capacity and reduce the sensing costs in large regions. Due to the ubiquitous nature of MCS, smart devices require cyber protection against adversaries that are becoming smarter with the objective of clogging the resources and spreading misinformation in such a non-dedicated sensing environment. In an MCS setting, one of the various adversary types has the primary goal of keeping participant devices occupied by submitting fake/illegitimate sensing tasks so as to clog the participant resources such as the battery, sensing, storage, and computing. With this in mind, this thesis proposes a systematical study of fake task injection in MCS, including modeling, detection, and mitigation of such resource clogging attacks. We introduce modeling of fake task attacks in MCS intending to clog the server and drain battery energy from mobile devices. We creatively grant mobility to the tasks for more extensive coverage of potential participants and propose two take movement patterns, namely Zone-free Movement (ZFM) model and Zone-limited Movement (ZLM) model. Based on the attack model and task movement patterns, we design task features and create structured simulation settings that can be modified to adapt different research scenarios and research purposes. Since the development of a secure sensing campaign highly depends on the existence of a realistic adversarial model. With this in mind, we apply the self-organizing feature map (SOFM) to maximize the number of impacted participants and recruits according to the user movement pattern of these cities. Our simulation results verify the magnified effect of SOFM-based fake task injection comparing with randomly selected attack regions in terms of more affected recruits and participants, and increased energy consumption in the recruited devices due to the illegitimate task submission. For the sake of a secure MCS platform, we introduce Machine Learning (ML) methods into the MCS server to detect and eliminate the fake tasks, making sure the tasks arrived at the user side are legitimate tasks. In our work, two machine learning algorithms, Random Forest and Gradient Boosting are adopted to train the system to predict the legitimacy of a task, and Gradient Boosting is proven to be a more promising algorithm. We have validated the feasibility of ML in differentiating the legitimacy of tasks in terms of precision, recall, and F1 score. By comparing the energy-consuming, effected recruits, and impacted candidates with and without ML, we convince the efficiency of applying ML to mitigate the effect of fake task injection.

在了解一特定國家金融危機的成因時，除了目前已有的三代金融危機理論之外，我們覺得尚應有一個國家別論述架構才能更完整的解釋特定金融危機的前因後果。 參與韓國自1970年代以來大力推動的以出口帶動經濟成長策略的「韓國政府」、「韓國大企業財團」，以及基本上是為此等財團服務的「韓國金融機構」三者間所共同形成的運作邏輯及其利益交換的三角關係，乃是韓國特有的國家別論述架構。 此一韓國獨有的特殊三角運作，僅管一直存在政府擴張性政策所造成信用過度擴張、金融機構受制於政府及貸款企業體質而致的經營沒有效率與道德危機，以及因此累積的對外舉債過多等諸多風險因子，但由於國際貿易與國際資本市場一直對韓國有利，1990年代中期前此一三角運作堪稱良好。 然而自1990年代中期起，韓國受外在因素接連影響，一路從國際貿易市場優勢不再、企業財團獲利急速惡化，進而拖累本國金融機構體質等基本面因素上為金融危機奠下惡因，最後在1997年東南亞國家連串發生金融危機的那個時間點上，沒能撐過國際資金短期內自韓國全面撤離的衝擊，終於發生金融危機。 1997年韓國金融危機儘管是個危機，但它也帶給韓國一個來自IMF援救計畫的外來改革動力，成功的克服過去「政府、企業財團及金融機構」三角運作中所累積的沉疴以及抗拒改革的慣性，讓進一步提升韓國經濟發展的結構性改造得以全面推動。 / Three generations of financial crises theories have been evolved since Paul Krugman developed his speculative attack models in 1979. Krugman emphasized the failure of the fundamentals while the later theories including the exit clause models addressing the conditional commitment of the central banks, the moral hazards of the financial institutes and the self-fulfilling effect of the markets. We looked into the 1997 Korean financial crisis with these prevailing theoretical principles and also developed a framework incorporating three closely related Korean parties, namely the government, the chaebols（conglomerates）and the financial institutes to unveil this specific Korean case.