Due to the lack of effective techniques for detecting and mitigating property violations, existing approaches to ensure the safety and security of software systems are often labor intensive and error prone. Furthermore, they focus primarily on functional correctness of the software code while ignoring micro-architectural details of the underlying processor, such as cache and speculative execution, which may undermine their soundness guarantees. To fill the gap, I propose a set of new methods and tools for ensuring the safety and security of software systems. Broadly speaking, these methods and tools fall into three categories. The first category is concerned with static program analysis. Specifically, I develop a novel abstract interpretation framework that considers both speculative execution and a cache model, and guarantees to be sound for estimating the execution time of a program and detecting side-channel information leaks. The second category is concerned with static program transformation. The goal is to eliminate side channels by equalizing the number of CPU cycles and the number of cache misses along all program paths for all sensitive variables. The third category is concerned with runtime safety enforcement. Given a property that may be violated by a reactive system, the goal is to synthesize an enforcer, called the shield, to correct the erroneous behaviors of the system instantaneously, so that the property is always satisfied by the combined system. I develop techniques to make the shield practical by handling both burst error and real-valued signals. The proposed techniques have been implemented and evaluated on realistic applications to demonstrate their effectiveness and efficiency. / Doctor of Philosophy / It is important for everything around us to follow some rules to work correctly. That is the same for our software systems to follow the security and safety properties. Especially, softwares may leak information via unexpected ways, e.g. the program timing, which makes it more difficult to be detected or mitigated. For instance, if the execution time of a program is related to the sensitive value, the attacker may obtain information about the sensitive value. On the other side, due to the complexity of software, it is nearly impossible to fully test or verify them. However, the correctness of software systems at runtime is crucial for critical applications. While existing approaches to find or resolve properties violation problem are often labor intensive and error prone, in this dissertation, I first propose an automated tool for detecting and mitigating the security vulnerability through program timing. Programs processed by the tool are guaranteed to be time constant under any sensitive values. I have also taken the influence of speculative execution, which is the cause behind recent Spectre and Meltdown attack, into consideration for the first time. To enforce the correctness of programs at runtime, I introduce an extra component that can be attached to the original system to correct any violation if it happens, thus the entire system will still be correct. All proposed methods have been evaluated on a variety of real world applications. The results show that these methods are effective and efficient in practice.

Following proper guidelines and recommendations are crucial in software security, which is mostly obstructed by accidental human errors. Automatic screening tools have great potentials to reduce the gap between the theory and the practice. However, the goal of scalable automated code screening is largely hindered by the practical difficulty of reducing false positives without compromising analysis quality. To enable compile-time security checking of cryptographic vulnerabilities, I developed highly precise static analysis tools (CryptoGuard and TaintCrypt) that developers can use routinely. The main technical enabler for CryptoGuard is a set of detection algorithms that refine program slices by leveraging language-specific insights, where TaintCrypt relies on symbolic execution-based path-sensitive analysis to reduce false positives. Both CryptoGuard and TaintCrypt uncovered numerous vulnerabilities in real-world software, which proves the effectiveness. Oracle has implemented our cryptographic code screening algorithms for Java in its internal code analysis platform, Parfait, and detected numerous vulnerabilities that were previously unknown. I also designed a specification language named SpanL to easily express rules for automated code screening. SpanL enables domain experts to create domain-specific security checking. Unfortunately, tools and guidelines are not sufficient to ensure baseline security in internet-wide ecosystems. I found that the lack of proper compliance checking induced a huge gap in the payment card industry (PCI) ecosystem. I showed that none of the PCI scanners (out of 6), we tested are fully compliant with the guidelines, issuing certificates to merchants that still have major vulnerabilities. Consequently, 86% (out of 1,203) of the e-commerce websites we tested, are non-compliant. To improve the testbeds in the light of our work, the PCI Security Council shared a copy of our PCI measurement paper to the dedicated companies that host, manage, and maintain the PCI certification testbeds. / Doctor of Philosophy / Automatic screening tools have great potentials to reduce the gap between the theory and the practice of software security. However, the goal of scalable automated code screening is largely hindered by the practical difficulty of reducing false positives without compromising analysis quality. To enable compile-time security checking of cryptographic vulnerabilities, I developed highly precise static analysis tools (CryptoGuard and TaintCrypt) that developers can use routinely. Both CryptoGuard and TaintCrypt uncovered numerous vulnerabilities in real-world software, which proves the effectiveness. Oracle has implemented our cryptographic code screening algorithms for Java in its internal code analysis platform, Parfait, and detected numerous vulnerabilities that were previously unknown. I also designed a specification language named SpanL to easily express rules for automated code screening. SpanL enables domain experts to create domain-specific security checking. Unfortunately, tools and guidelines are not sufficient to ensure baseline security in internet-wide ecosystems. I found that the lack of proper compliance checking induced a huge gap in the payment card industry (PCI) ecosystem. I showed that none of the PCI scanners (out of 6), we tested are fully compliant with the guidelines, issuing certificates to merchants that still have major vulnerabilities. Consequently, 86% (out of 1,203) of the e-commerce websites we tested, are non-compliant. To improve the testbeds in the light of our work, the PCI Security Council shared a copy of our PCI measurement paper to the dedicated companies that host the PCI certification testbeds.

Developers often need to re-engineer distributed applications to address changes in requirements, made only after deployment. Much of the complexity of inspecting and evolving distributed applications lies in their distributed nature, while the majority of mature program analysis and transformation tools works only with centralized software. Inspired by business process re-engineering, in which remote operations can be insourced back in house to restructure and outsource anew, this dissertation brings an analogous approach to the re-engineering of distributed applications. Our approach introduces a novel automatic refactoring---Client Insourcing---that creates a semantically equivalent centralized version of a distributed application. This centralized version is then inspected, modified, and redistributed to meet new requirements. This dissertation demonstrates the utility of Client Insourcing in helping meet the changed requirements in performance, reliability, and security. We implemented Client Insourcing in the important domain of full-stack JavaScript applications, in which both the client and server parts are written in JavaScript, and applied our implementation to re-engineer mobile web applications. Client Insourcing reduces the complexity of inspecting and evolving distributed applications, thereby facilitating their re-engineering. This dissertation is based on 4 conference papers and 2 doctoral symposium papers, presented at ICWE 2019, SANER 2020, WWW 2020, and ICWE 2021. / Doctor of Philosophy / Modern web applications are distributed across a browser-based client and a remote server. Software developers need to optimize the performance of web applications as well as correct and modify their functionality. However, the vast majority of mature development tools, used for optimizing, correcting, and modifying applications work only with non-distributed software, written to run on a single machine. To facilitate the maintenance and evolution of web applications, this dissertation research contributes new automated software transformation techniques. These contributions can be incorporated into the design of software development tools, thereby advancing the engineering of web applications.

Exfiltrating or tampering with certain business logic, algorithms, and data can harm the security and privacy of both organizations and end users. Collectively referred to as sensitive program information (SPI), these building blocks are part and parcel of modern software systems in domains ranging from enterprise applications to cyberphysical setups. Hence, protecting SPI has become one of the most salient challenges of modern software development. However, several fundamental obstacles stand on the way of effective SPI protection: (1) understanding and locating the SPI for any realistically sized codebase by hand is hard; (2) manually isolating SPI to protect it is burdensome and error-prone; (3) if SPI is passed across distributed components within and across devices, it becomes vulnerable to security and privacy attacks. To address these problems, this dissertation research innovates in the realm of automated program analysis, code transformation, and novel programming abstractions to improve the state of the art in SPI protection. Specifically, this dissertation comprises three interrelated research thrusts that: (1) design and develop program analysis and programming support for inferring the usage semantics of program constructs, with the goal of helping developers understand and identify SPI; (2) provide powerful programming abstractions and tools that transform code automatically, with the goal of helping developers effectively isolate SPI from the rest of the codebase; (3) provide programming mechanism for distributed managed execution environments that hides SPI, with the goal of enabling components to exchange SPI safely and securely. The novel methodologies, techniques, and software tools, supported by programming abstractions, automated program analysis, and code transformation of this dissertation research lay the groundwork for establishing a secure, understandable, and efficient foundation for protecting SPI. This dissertation is based on 4 conference papers, presented at TrustCom'20, GPCE'20, GPCE'18, and ManLang'17, as well as 1 journal paper, published in Journal of Computer Languages (COLA). / Doctor of Philosophy / Some portions of a computer program can be sensitive, referred to as sensitive program information (SPI). By compromising SPI, attackers can hurt user security/privacy. It is hard for developers to identify and protect SPI, particularly for large programs. This dissertation introduces novel methodologies, techniques, and software tools that facilitate software developments tasks concerned with locating and protecting SPI.

Typical cybersecurity solutions emphasize on achieving defense functionalities. However, execution efficiency and scalability are equally important, especially for real-world deployment. Straightforward mappings of cybersecurity applications onto HPC platforms may significantly underutilize the HPC devices' capacities. On the other hand, the sophisticated implementations are quite difficult: they require both in-depth understandings of cybersecurity domain-specific characteristics and HPC architecture and system model. In our work, we investigate three sub-areas in cybersecurity, including mobile software security, network security, and system security. They have the following performance issues, respectively: 1) The flow- and context-sensitive static analysis for the large and complex Android APKs are incredibly time-consuming. Existing CPU-only frameworks/tools have to set a timeout threshold to cease the program analysis to trade the precision for performance. 2) Network intrusion detection systems (NIDS) use automata processing as its searching core and requires line-speed processing. However, achieving high-speed automata processing is exceptionally difficult in both algorithm and implementation aspects. 3) It is unclear how the cache configurations impact time-driven cache side-channel attacks' performance. This question remains open because it is difficult to conduct comparative measurement to study the impacts. In this dissertation, we demonstrate how application-specific characteristics can be leveraged to optimize implementations on various types of HPC for faster and more scalable cybersecurity executions. For example, we present a new GPU-assisted framework and a collection of optimization strategies for fast Android static data-flow analysis that achieve up to 128X speedups against the plain GPU implementation. For network intrusion detection systems (IDS), we design and implement an algorithm capable of eliminating the state explosion in out-of-order packet situations, which reduces up to 400X of the memory overhead. We also present tools for improving the usability of Micron's Automata Processor. To study the cache configurations' impact on time-driven cache side-channel attacks' performance, we design an approach to conducting comparative measurement. We propose a quantifiable success rate metric to measure the performance of time-driven cache attacks and utilize the GEM5 platform to emulate the configurable cache. / Doctor of Philosophy / Typical cybersecurity solutions emphasize on achieving defense functionalities. However, execution efficiency and scalability are equally important, especially for the real-world deployment. Straightforward mappings of applications onto High-Performance Computing (HPC) platforms may significantly underutilize the HPC devices’ capacities. In this dissertation, we demonstrate how application-specific characteristics can be leveraged to optimize various types of HPC executions for cybersecurity. We investigate several sub-areas, including mobile software security, network security, and system security. For example, we present a new GPU-assisted framework and a collection of optimization strategies for fast Android static data-flow analysis that achieve up to 128X speedups against the unoptimized GPU implementation. For network intrusion detection systems (IDS), we design and implement an algorithm capable of eliminating the state explosion in out-of-order packet situations, which reduces up to 400X of the memory overhead. We also present tools for improving the usability of HPC programming. To study the cache configurations’ impact on time-driven cache side-channel attacks’ performance, we design an approach to conducting comparative measurement. We propose a quantifiable success rate metric to measure the performance of time-driven cache attacks and utilize the GEM5 platform to emulate the configurable cache.

Various vulnerabilities in software applications become easy targets for attackers. The trend constantly being observed in the evolution of advanced modern exploits is their growing sophistication in stealthy attacks. Code-reuse attacks such as return-oriented programming allow intruders to execute mal-intended instruction sequences on a victim machine without injecting external code. Successful exploitation leads to hijacked applications or the download of malicious software (drive-by download attack), which usually happens without the notice or permission from users. In this dissertation, we address the problem of host-based system anomaly detection, specifically by predicting expected behaviors of programs and detecting run-time deviations and anomalies. We first introduce an approach for detecting the drive-by download attack, which is one of the major vectors for malware infection. Our tool enforces the dependencies between user actions and system events, such as file-system access and process execution. It can be used to provide real time protection of a personal computer, as well as for diagnosing and evaluating untrusted websites for forensic purposes. We perform extensive experimental evaluation, including a user study with 21 participants, thousands of legitimate websites (for testing false alarms), 84 malicious websites in the wild, as well as lab reproduced exploits. Our solution demonstrates a usable host-based framework for controlling and enforcing the access of system resources. Secondly, we present a new anomaly-based detection technique that probabilistically models and learns a program's control flows for high-precision behavioral reasoning and monitoring. Existing solutions suffer from either incomplete behavioral modeling (for dynamic models) or overestimating the likelihood of call occurrences (for static models). We introduce a new probabilistic anomaly detection method for modeling program behaviors. Its uniqueness is the ability to quantify the static control flow in programs and to integrate the control flow information in probabilistic machine learning algorithms. The advantage of our technique is the significantly improved detection accuracy. We observed 11 up to 28-fold of improvement in detection accuracy compared to the state-of-the-art HMM-based anomaly models. We further integrate context information into our detection model, which achieves both strong flow-sensitivity and context-sensitivity. Our context-sensitive approach gives on average over 10 times of improvement for system call monitoring, and 3 orders of magnitude for library call monitoring, over existing regular HMM methods. Evaluated with a large amount of program traces and real-world exploits, our findings confirm that the probabilistic modeling of program dependences provides a significant source of behavior information for building high-precision models for real-time system monitoring. Abnormal traces (obtained through reproducing exploits and synthesized abnormal traces) can be well distinguished from normal traces by our model. / Ph. D.

One of the greatest achievements of Scratch as an educational tool is the eager willingness of programmers to use existing projects as the starting point for their own projects, a practice known as remixing. Despite the importance of remixing as a foundation of collaborative and communal learning, the practice remains poorly understood. Without a clear picture of how and why Scratch programmers remix a project as a starting point of their own projects, this programming community would remain in the dark about which programming practices encourage and facilitate remixing. The designers of programming environments for blocks lack feedback on how the remixing facility is used in the wild. To gain a deeper insight into remixing, this thesis presents the results of a comprehensive study of this practice in Scratch that investigates the following heretofore unexplored dimensions of remixing: (1) the prevailing modifications that remixes perform on existing projects, (2) the impact of the original project's code quality on the granularity, extent, and development time of the modifications in the remixes, and (3) the propensity of the dominant programming practices in the original project to remain so in the remixes. Our findings can be used to promote those programming practices in the Scratch community that encourage remixing while also improving this practice's effectiveness, thus benefiting the educational and end-user programming communities. / Master of Science / The Scratch programming language has become an intrinsically important tool in introductory CS education. A visual, block-based language, Scratch is web-based, featuring an enormous online programming community, through which projects are eagerly shared. One of the unique learning provisions of Scratch is the ability to easily start a project by modifying someone else’s project, a practice referred to as remixing. Despite the central role that remixing plays in enabling the communal and collaborative learning styles in the Scratch community, the practice of remixing remains inadequately understood. This knowledge gap leaves the Scratch community in the dark about which programming practices encourage and facilitate remixing, as well as deprives Scratch environment designers from actionable feedback on how the remixing facility is used in the wild. To address this problem, this thesis reports on the results of an exploratory study of remixing in Scratch that investigates three heretofore unexplored dimensions of this practice. First, we study the general remixing trends in terms of how remixes modify the original projects. Second, we infer the impact of a project’s code quality on the modifications in its remixes and the development time. Finally, we investigate whether programmers adopt the techniques and practices of the remixed projects. Computing educators can apply our findings to enhance the educational effectiveness of Scratch by encouraging the practice and magnitude of remixing.

The increasing prevalence of multi and many core processors has brought the issues of concurrency and parallelism to the forefront of everyday computing. Even for applications amenable to traditional parallelization techniques, the subtleties of concurrent programming are known to introduce concurrency bugs. Due to the potential of concurrency bugs, programmers find it hard to write correct concurrent code. To take full advantage of parallel shared memory platforms, application programmers need safe and efficient mechanisms that can support a wide range of parallel applications. In addition, a large body of applications are inherently hard-to-parallelize; their data and control dependencies impose execution order constraints that preclude the use of traditional parallelization techniques. Sensitive to their input data, a substantial number of applications fail to scale well, leaving cores idle. To improve the performance of such applications, application programmers need effective mechanisms that can fully leverage multi and many core architectures. These challenges stand in the way of realizing the true potential of emerging many core platforms. The techniques described in this dissertation address these challenges. Specifically, this dissertation contributes techniques to transparently detect and eliminate several concurrency bugs, including deadlocks, asymmetric write-write data races, priority inversion, live-locks, order violations, and bugs that stem from the presence of asynchronous signaling and locks. A second major contribution of this dissertation is a programming framework that exploits coarse-grain speculative parallelism to improve the performance of otherwise hard-to-parallelize applications. / Ph. D.

No description available.

This research was a qualitative program evaluation of students' perceptions of Latinos in Action (LIA), a peer-mentoring program that seeks to improve high school Latino graduation rates and college admittance. The study was conducted with college students who participated in the program in high school. LIA graduates were interviewed to determine what major factors influenced and supported them in their academic decisions. Additional data included an interview with the program director, results from the High School Survey of Student Engagement (HSSSE), and a video of one of the interviewees. The researcher explored students' experiences in the LIA program with the goal of determining how the program impacted their goals to graduate from high school and attend college. While all of the students planned to attend college before participation in the program, they did not know how they would be able to get there. LIA provided the motivational support and the direction to help students get to college. In addition, all of the students mentioned parent support as a major influence. Other influences included positive peer support, a supportive high school teacher, school involvement, and being a role model for younger students.