On July 18, 1994, the Asociación Mutual Israelita Argentina (AMIA), the Jewish Community Center in Buenos Aires, was bombed in what has been called the worst terrorist attack on Argentina in history. The bombing killed an estimated 86 people and wounded over 200. The Argentine government began a judicial investigation and vowed to bring the perpetrators to justice. Twenty years later, the case remains unresolved. The investigation has been marked by inefficiency and allegations of corruption. The recent, suspicious death of the lead prosecutor of the case has further called into question the legitimacy of Argentina’s government and justice system. This thesis examines the Argentine government’s response to the AMIA bombing in the context of Argentine politics. This thesis discusses Argentina’s democratic stability, political performance, history of corruption, and economic situation as key factors in understanding the government’s response to the AMIA attack and investigation. Argentina’s response does not fit within the established models of a democracy’s response to terrorism. Argentina’s response, or lack thereof, to this terrorist attack is perplexing, but this surprising outcome may be explained by the country’s political problems. The findings of this thesis support the conclusion that the Argentine government’s response to the AMIA case can be attributed to its functioning but flawed democracy and faulty political performance. Argentina has a history of political and police corruption and a weak judicial branch, which has hindered the effectiveness of the justice system and complicated the AMIA investigation. The extent to which these factors have allowed corruption and economic interests to derail the investigation is still debated and offers an area for future research.

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AbstractThis study began with the idea that the drone attacks launched by the United States on the northwest region of Pakistan since 2004 have not helped in the expressed aim of the US to win the war on terror. The study asked three main questions. It wished to discover why drone attacks in Pakistan had not helped to win the war on terror, the main reasons that these attacks have not been successful and how these attacks have led to the increase of the anti-US feeling in Pakistan. The study used a case study methodology that focused on gaining a qualitative insight from a range of perspectives including official government stances, the reaction of media and social media and the public reactions in Pakistan. The study analysis is supported by the theory of neoliberalism and neo realism as it deemed the most appropriate in this type of work.Conducted within the neoliberal and positivist perspective, the study concluded that the drone strikes have not helped to win the war on terror and that they are actually a major part of why this victory has not yet occurred. The cold-hearted manner with which the US seem to launch drone strike attacks have led to the development of the views that the US does not care for international laws and has no desire to take Pakistan sovereignty into account. The role of the media has helped spread the anti-US feeling far more rapidly than would have been previously possible in the region. The access to the Internet, the use of social media websites and the global coverage of the situation means that reports of civilian casualties has been a common occurrence over the past 10 years, and this has seemingly strengthened the terrorist resolve, turned the public against the US strategy and also led in some cases to the further radicalization of the Pakistani youth. When assessed through a neoliberal perspective, it was apparent that the strategy does not fit with the concept of international co-operation and that the actions of the US have led to the growth of anti-US sentiment. The main failing of the drone strike strategy could be said to be the fact that it was devised using a neorealist attitude in an increasingly neoliberal global society. The study also presented a number of policy recommendations and future areas of study based on the findings from this work.

Cyber-physical systems (CPS) refer to a group of systems that combine the real physical world with cyber components. Traditionally, the applications of CPS in research and the real world mainly include smart power grid, autonomous automobile systems, and robotics systems. In recent years, due to the fast development of pervasive computing, sensor manufacturing, and artificial intelligence technologies, mobile cyber-physical systems that extend the application domains of traditional cyber-physical systems have become increasingly popular. In mobile cyber-physical systems, devices have rich features, such as significant computational resources, multiple communication radios, various sensor modules, and high-level programming languages. These features enable us to build more powerful and convenient applications and systems for mobile users. At the same time, such information can also be leveraged by attackers to design new types of attacks. The security and privacy issues can exist in any application of mobile CPS. In terms of defense systems, we focus on three important topics: voice liveness detection, face forgery detection, and securing PIN-based authentication. In terms of attack systems, we study the location privacy in augmented reality (AR) applications. We first investigate the voice replay attacks on smartphones. Voice input is becoming an important interface on smartphones since it can provide better user experience compared with traditional typing-based input methods. However, because the human voice is often exposed to the public, attackers can easily steal victims' voices and replay it to victims' devices to issue malicious commands. To defend the smartphone from voice replay attacks, we propose a novel liveness detection system, which can determine whether the incoming voice is from a live person or a loudspeaker. The key idea is that voices are produced and finalized at multiple positions in human vocal systems, while the audio signals from loudspeakers are from one position. By using two microphones on the smartphone to record the voice at two positions and measure their relationship, the proposed system can defend against voice replay attacks with a high success rate. Besides smartphones, voice replay attacks are also feasible on AR headsets. However, due to the special hardware positions, the current voice liveness detection system designed for smartphones cannot be deployed on AR headsets. To address this issue, we propose a novel voice liveness detection system for AR headsets. The key insight is that the human voice can propagate through the internal body. By attaching a contact microphone around the user's temple, we can collect the internal body voice. A voice is determined from a live person as long as the collected internal body voice has a strong relationship with the mouth voice. Since the contact microphone is cheap, tiny, and thin, it can be embedded in current AR headsets with minimal additional cost. Next, we propose a system to detect the fake face in real-time video chat. Recent developments in deep learning-based forgery techniques largely improved the ability of forgery attackers. With the help of face reenactment techniques, attackers can transfer their facial expressions to another person's face to create fake facial videos in real-time with very high quality. In our system, we find that the face of a live person can reflect the screen light, and this reflected light can be captured by the web camera. Moreover, current face forgery techniques cannot generate such light change with acceptable quality. Therefore, we can measure the correlation and similarity of the luminance changes between the screen light and the face-reflected light to detect the liveness of the face. We also study to leverage IoT devices to enhance the privacy of some daily operations. We find that the widely used personal identification number (PIN) is not secure and can be attacked in many ways. In some scenarios, it is hard to prevent attackers from obtaining the victim's PIN. Therefore, we propose a novel system to secure the PIN input procedure even if the victim's PIN has been leaked. The basic idea is that different people have different PIN input behavior even for the same PIN. Even though attackers can monitor the victim's PIN input behaviors and imitate it afterward, the biological differences among each person's hands still exist and can be used to differentiate them. To capture both PIN input behavior and the biological features, we install a tiny light sensor at the center of the PIN pad to transfer the information into a light signal. By extracting useful features from multiple domains, we can determine whether the PIN input is from the same person with high accuracy. Besides designing new defense systems, we also show that sensory data and side-channel information can be leveraged to launch new types of attacks. We conduct a study on the network traffic of location-based AR applications. We find that it is feasible to infer the real-time location of a user using the short-time network traffic if the downloading jobs are related to the current location. By carefully deploying fake AR contents at some locations, our attack system can infer the location of the user with high accuracy by processing noisy network traffic data. / Computer and Information Science

In recent years, social networks and their related theories and applications attract widespread attentions in computer science. Many applications are designed by exploring the social information among users, such as social peer-to-peer systems, mobile cloud, and online recommendation systems. Most of the existing works only focus on how to use social information but ignore the fact that social information itself may cause severe security and privacy problems. In this dissertation, we first present some social information-assisted application systems that we have designed, and then, we present several social information-involved privacy and security risks and their countermeasures. Generally speaking, the design procedure of any social information-assisted application involves three tasks: publishing, accessing, and using social information. However, all of these tasks contain privacy and security issues. Social information can be published from a centralized system or a distributed one. For the centralized scheme, the social information is directly published from online social networking systems, such as Facebook or Twitter. However, we found that the data of a social network essentially is a time-evolving graph. Most of the existing approaches fail to preserve users' identity privacy once a malicious attacker has the external knowledge about the victim's time-varying behaviors. For avoiding the new privacy issue, we propose a time-based anonymization scheme. For the distributed social information-sharing scheme, each user's information is propagated from friend to friend's friends, and so on. We design a new scheme to gradually enhance the privacy protection along a propagation path, in the meanwhile, maximally preserve the overall utility of the user's data. From a data accessing aspect, social information can be used by malicious users for launching new attacks. In this dissertation, we find a friendship-based privacy disclosure attack, and a corresponding defense approach is designed. Location-based service has been widely adopted. In order to preserve location privacy, users usually turn off the corresponding applications when visiting sensitive locations. However, once social relationships are known, attackers are able to infer these hidden locations, which disclose users' location privacy. For preserving the location privacy, we design a fake location-based approach, which efficiently disorders the social-geographic relationships among users. From the data usage aspect, social information and its related data may come from users. A system may lose functioning if some malicious users inject plenty of fake information. Mobile clouds and Friend Locator are two typical systems, which are vulnerable to the fake information-related attacks. Mobile clouds explore the idle computing resources of surrounding devices by recruiting nearby friends to participate in the same task. However, malicious users may inject wrong friendships information to mess up the system. When visiting a new place, Friend Locator provides navigation services for participators by creating a map based their trajectories. The functioning of the system is based on the trust among participators. Once a user's device is controlled by attackers, all other users may receive wrong navigation. For defending these attacks, we provide different countermeasure. / Computer and Information Science

As Machine Learning (ML) algorithms are deployed to solve a wide variety of tasks in today’s world, data poisoning attack poses a significant threat to ML applications. Although numerous defence algorithms against data poisoning attack have been proposed and shown to be effective, most defence algorithms are analyzed under the assumption of fixed attack strategies, without accounting for the strategic interactions between the attacker and the defender. In this work, we perform game theoretic analysis of defence algorithms against data poisoning attacks on Machine Learning. We study the defence strategy as a competitive game between the defender and the adversary and analyze the game characteristics for several defence algorithms. We propose a game model for the poisoning attack scenario, and prove the characteristics of the Nash Equilibrium (NE) defence strategy for all distance-based defence algorithms. Based on the NE characteristics, we develop an efficient algorithm to approximate for the NE defence strategy. Using fixed attack strategies as the benchmark, we then experimentally evaluate the impact of strategic interactions in the game model. Our approach does not only provide insights about the effectiveness of the analyzed algorithms under optimal poisoning attacks, but also serves as a method for the modellers to determine capable defence algorithms and optimal strategies to employ on their ML models. / Thesis / Master of Science (MSc) / As Machine Learning (ML) algorithms are deployed to solve a wide variety of tasks in today’s world, data poisoning attack poses a significant threat to ML applications. In this work, we study the defence against poisoning attack scenario as a competitive game between the defender and the adversary and analyze the game characteristics for several defence algorithms. Our goal is to identify the optimal defence strategy against poisoning attacks, even when the adversary responds optimally to the defence strategy. We propose a game model for the poisoning attack scenario, and develop an efficient algorithm to approximate for the Nash Equilibrium defence strategy. Our approach does not only provide insights about the effectiveness of the analyzed algorithms under optimal poisoning attacks, but also serves as a method for the modellers to determine capable defence algorithms and optimal strategies to employ on their ML models.