Memory-corruption vulnerability is one of the most common attack vectors used to compromise computer systems. Such vulnerabilities could lead to serious security problems and would remain an unsolved problem for a long time. Existing memory corruption attacks can be broadly classified into two categories: i) control-flow attacks and ii) data-oriented attacks. Though data-oriented attacks are known for a long time, the threats have not been adequately addressed due to the fact that most previous defense mechanisms focus on preventing control-flow exploits. As launching a control-flow attack becomes increasingly difficult due to many deployed defenses against control-flow hijacking, data-oriented attacks are considered an appealing attack technique for system compromise, including the emerging embedded control systems. To counter data-oriented attacks, mitigation techniques such as memory safety enforcement and data randomization can be applied in different stages over the course of an attack. However, attacks are still possible because currently deployed defenses can be bypassed. This dissertation explores the possibility of defeating data-oriented attacks through external monitoring using program anomaly detection techniques. I start with a systematization of current knowledge about exploitation techniques of data-oriented attacks and the applicable defense mechanisms. Then, I address three research problems in program anomaly detection against data-oriented attacks. First, I address the problem of securing control programs in Cyber-Physical Systems (CPS) against data-oriented attacks. I describe a new security methodology that leverages the event-driven nature in characterizing CPS control program behaviors. By enforcing runtime cyber-physical execution semantics, our method detects data-oriented exploits when physical events are inconsistent with the runtime program behaviors. Second, I present a statistical program behavior modeling framework for frequency anomaly detection, where frequency anomaly is the direct consequence of many non-control-data attacks. Specifically, I describe two statistical program behavior models, sFSA and sCFT, at different granularities. Our method combines the local and long-range models to improve the robustness against data-oriented attacks and significantly increase the difficulties that an attack bypasses the anomaly detection system. Third, I focus on defending against data-oriented programming (DOP) attacks using Intel Processor Trace (PT). DOP is a recently proposed advanced technique to construct expressive non-control data exploits. I first demystify the DOP exploitation technique and show its complexity and rich expressiveness. Then, I design and implement the DeDOP anomaly detection system, and demonstrate its detection capability against the real-world ProFTPd DOP attack. / Ph. D. / Memory-corruption vulnerability is one of the most common attack vectors used to compromise computer systems. Such vulnerabilities could lead to serious security problems and would remain an unsolved problem for a long time. This is because low-level memory-unsafe languages (e.g., C/C++) are still in use today for interoperability and speed performance purposes, and remain common sources of security vulnerabilities. Existing memory corruption attacks can be broadly classified into two categories: i) control-flow attacks that corrupt control data (e.g., return address or code pointer) in the memory space to divert the program’s control-flow; and ii) data-oriented attacks that target at manipulating non-control data to alter a program’s benign behaviors without violating its control-flow integrity. Though data-oriented attacks are known for a long time, the threats have not been adequately addressed due to the fact that most previous defense mechanisms focus on preventing control-flow exploits. As launching a control-flow attack becomes increasingly difficult due to many deployed defenses against control-flow hijacking, data-oriented attacks are considered an appealing attack technique for system compromise, including the emerging embedded control systems. To counter data-oriented attacks, mitigation techniques such as memory safety enforcement and data randomization can be applied in different stages over the course of an attack. However, attacks are still possible because currently deployed defenses can be bypassed. This dissertation explores the possibility of defeating data-oriented attacks through external monitoring using program anomaly detection techniques. I start with a systematization of current knowledge about exploitation techniques of data-oriented attacks and the applicable defense mechanisms. Then, I address three research problems in program anomaly detection against data-oriented attacks. First, I address the problem of securing control programs in Cyber-Physical Systems (CPS) against data-oriented attacks. The key idea is to detect subtle data-oriented exploits in CPS when physical events are inconsistent with the runtime program behaviors. Second, I present a statistical program behavior modeling framework for frequency anomaly detection, where frequency anomaly is often consequences of many non-control-data attacks. Our method combines the local and long-range models to improve the robustness against data-oriented attacks and significantly increase the difficulties that an attack bypasses the anomaly detection system. Third, I focus on defending against data-oriented programming (DOP) attacks using Intel Processor Trace (PT). I design and implement the DEDOP anomaly detection system, and demonstrate its detection capability against the real-world DOP attack.

Proactive approaches for preventing attacks through security measurements are crucial for preventing sophisticated attacks. However, proactive measures must employ qualitative security metrics and systemic measurement methodologies to assess security guarantees, as some metrics (e.g., entropy) used for evaluating security guarantees may not capture the capabilities of advanced attackers. Also, many proactive measures (e.g., data pointer protection or data flow integrity) suffer performance bottlenecks. This dissertation identifies and represents attack vectors as metrics using the knowledge from advanced exploits and demonstrates the effectiveness of the metrics by quantifying attack surface and enabling ways to tune performance vs. security of existing defenses by identifying and prioritizing key attack vectors for protection. We measure attack surface by quantifying the impact of fine-grained Address Space Layout Randomization (ASLR) on code reuse attacks under the Just-In-Time Return-Oriented Programming (JITROP) threat model. We conduct a comprehensive measurement study with five fine-grained ASLR tools, 20 applications including six browsers, one browser engine, and 25 dynamic libraries. Experiments show that attackers only need several seconds (1.5-3.5) to find various code reuse gadgets such as the Turing Complete gadget set. Experiments also suggest that some code pointer leaks allow attackers to find gadgets more quickly than others. Besides, the instruction-level single-round randomization can restrict Turing Complete operations by preventing up to 90% of gadgets. This dissertation also identifies and prioritizes critical data pointers for protection to enable the capability to tune between performance vs. security. We apply seven rule-based heuristics to prioritize externally manipulatable sensitive data objects/pointers. Our evaluations using 33 ground truths vulnerable data objects/pointers show the successful detection of 32 ground truths with a 42% performance overhead reduction compared to AddressSanitizer. Our results also suggest that sensitive data objects are as low as 3%, and on average, 82% of data objects do not need protection for real-world applications. / Doctor of Philosophy / Proactive approaches for preventing attacks through security measurements are crucial to prevent advanced attacks because reactive measures can become challenging, especially when attackers enter sophisticated attack phases. A key challenge for the proactive measures is the identification of representative metrics and measurement methodologies to assess security guarantees, as some metrics used for evaluating security guarantees may not capture the capabilities of advanced attackers. Also, many proactive measures suffer performance bottlenecks. This dissertation identifies and represents attack elements as metrics using the knowledge from advanced exploits and demonstrates the effectiveness of the metrics by quantifying attack surface and enabling the capability to tune performance vs. security of existing defenses by identifying and prioritizing key attack elements. We measure the attack surface of various software applications by quantifying the available attack elements of code reuse attacks in the presence of fine-grained Address Space Layout Randomization (ASLR), a defense in modern operating systems. ASLR makes code reuse attacks difficult by making the attack components unavailable. We perform a comprehensive measurement study with five fine-grained ASLR tools, real-world applications, and libraries under an influential code reuse attack model. Experiments show that attackers only need several seconds (1.5-3.5) to find various code reuse elements. Results also show the influence of one attack element over another and one defense strategy over another strategy. This dissertation also applies seven rule-based heuristics to prioritize externally manipulatable sensitive data objects/pointers – a type of attack element – to enable the capability to tune between performance vs. security. Our evaluations using 33 ground truths vulnerable data objects/pointers show the successful identification of 32 ground truths with a 42% performance overhead reduction compared to AddressSanitizer, a memory error detector. Our results also suggest that sensitive data objects are as low as 3% of all objects, and on average, 82% of objects do not need protection for real-world applications.

<p>Program anomaly detection — modeling normal program executions to detect deviations at runtime as cues for possible exploits — has become a popular approach for software security. To leverage high performance modeling and complete tracing, existing techniques however focus on subsets of applications, e.g., on system calls or calls to predefined libraries. Due to limited scope, it is insufficient to detect subtle control-oriented and data-oriented attacks that introduces new illegal call relationships at the application level. Also such techniques are hard to apply on devices that lack a clear separation between OS and the application layer. This dissertation advances the design and implementation of program anomaly detection techniques by providing application context for library and system calls making it powerful for detecting advanced attacks targeted at manipulating intra- and inter-procedural control-flow and decision variables. </p> <p><br></p> <p>This dissertation has two main parts. The first part describes a statically initialized generic calling context program anomaly detection technique LANCET based on Hidden Markov Modeling to provide security against control-oriented attacks at program runtime. It also establishes an efficient execution tracing mechanism facilitated through source code instrumentation of applications. The second part describes a program anomaly detection framework EDISON to provide security against data-oriented attacks using graph representation learning and language models for intra and inter-procedural behavioral modeling respectively.</p> <p><br> This dissertation makes three high-level contributions. First, the concise descriptions demonstrates the design, implementation and extensive evaluation of an aggregation-based anomaly detection technique using fine-grained generic calling context-sensitive modeling that allows for scaling the detection over entire applications. Second, the precise descriptions show the design, implementation, and extensive evaluation of a detection technique that maps runtime traces to the program’s control-flow graph and leverages graphical feature representation to learn dynamic program behavior. Finally, this dissertation provides details and experience for designing program anomaly detection frameworks from high-level concepts, design, to low-level implementation techniques.</p>