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ARM is becoming more popular in desktops and data centers. This opens a new realm in terms of security attacks against ARM, increasing the importance of having an effective and efficient defense mechanism for ARM. ARM has released Pointer Authentication, a new hardware security feature that is intended to ensure pointer integrity with cryptographic primitives. Recently, it has been found to be vulnerable. In this thesis, we utilize Pointer Authentication to build a novel scheme to completely prevent any misuse of security-sensitive pointers. We propose PACTight to tightly seal these pointers from attacks targeting Pointer Authentication itself as well as from control-flow hijacks. PACTight utilizes a strong and unique modifier that addresses the current issues with PAC and its implementations. We implement four defenses by fully integrating with the LLVM compiler toolchain. Through a robust and systemic security and performance evaluation, we show that PACTight defenses are more efficient and secure than their counterparts. We evaluated PACTight on 30 different applications, including NGINX web server and using real PAC instructions, with an average performance and memory overhead of 4.28% and 23.2% respectively even when enforcing its strongest defense. As far as we know, PACTight is the first defense mechanism to demonstrate effectiveness and efficiency with real PAC instructions. / M.S. / ARM is slowly but surely establishing itself in the market for desktops and data centers. Intel has been the dominant force for some time but ARM’s entrance into that realm opens up new avenues and possibilities for security attacks against ARM machines. Thus, it is becoming increasingly important to develop an effective and efficient defense mechanism for ARM against possible security threats, particularly against memory corruption vulnerabilities. Memory corruption vulnerabilities are still very prevalent in today’s security realm and have been for the past thirty years. Different hardware vendors have developed a variety of hardware features to combat them and ARM is no different. ARM has released Pointer Authentication, a new hardware security feature that is intended to ensure pointer integrity with cryptographic primitives. Pointer Authentication allows developers to utilize the unused bits of a pointer and add a cryptographic hash that can ensure the pointer hasn’t been tampered with. Pointer Authentication has been utilized in other solutions by security researchers. However, these solutions are either incomplete in their coverage or lack enough randomness for the cryptographic hash. In this thesis we utilize Pointer Authentication to build a novel scheme to completely prevent any misuse of security-sensitive pointers in memory corruption attacks. This thesis presents PACTight to tightly seal these pointers from attacks abusing the limited randomness of the hash as well as control-flow hijack attacks. PACTight implements four defenses by fully integrating with the LLVM compiler toolchain. Through a robust and systemic security and performance evaluation, this thesis show that PACTight defenses are more efficient and secure than their counterparts.

This dissertation includes three approaches we have been designed to tackle threats and challenges in network, software, and mobile security. The first approach demonstrates a new class of content masking attacks against the Adobe PDF standard, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. The second work protects sensitive data in binaries from being corrupted by cyber attackers. The last work proposes a mechanism which utilizes the unique walking patterns inherent to humans and differentiate our work from other walking behavior studies by using it as first-order authentication and developing matching methods fast enough to act as an actual anti-theft system.

Data represent a key resource for all organizations we may think of. Thus, it is not surprising that data are the main target of a large variety of attacks. Security vulnerabilities and phishing attacks make it possible for malicious software to steal business or privacy sensitive data and to undermine data availability such as in recent ransomware attacks.Apart from external malicious parties, insider attacks also pose serious threats to organizations with sensitive information, e.g., hospitals with patients’ sensitive information. Access control mechanisms are not always able to prevent insiders from misusing or stealing data as they often have data access permissions. Therefore, comprehensive solutions for data protection require combining access control mechanisms and other security techniques,such as encryption, with techniques for detecting anomalies in data accesses. In this the-sis, we develop fine-grained anomaly detection techniques for ensuring in depth protection of data from malicious software, specifically, ransomware, and from malicious insiders.While anomaly detection techniques are very useful, in many cases the data that is used for anomaly detection are very sensitive, e.g., health data being shared with untrusted service providers for anomaly detection. The owners of such data would not share their sensitive data in plain text with an untrusted service provider and this predicament undoubtedly hinders the desire of these individuals/organizations to become more data-driven. In this thesis, we have also built a privacy-preserving framework for real-time anomaly detection.

ICS security has been a topic of scrutiny and research for several years, and many security issues are well known. However, research efforts are impeded by a lack of an open virtual industrial control system testbed for security research. This thesis describes a virtual testbed framework using Python to create discrete testbed components (including virtual devices and process simulators). This testbed is designed such that the testbeds are interoperable with real ICS devices and that the virtual testbeds can provide comparable ICS network behavior to a laboratory testbed. Two testbeds based on laboratory testbeds have been developed and have been shown to be interoperable with real industrial control systemequipment and vulnerable to attacks in the samemanner as a real system. Additionally, these testbeds have been quantitatively shown to produce traffic close to laboratory systems (within 90% similarity on most metrics).

This thesis addresses the threat that personal computer faced from malware when the personal computer is connected to the Internet. Traditional host-based security approaches, such as anti-virus scanning protect the host from virus, worms, Trojans and other malwares. One of the issues of the host-based security approaches is that when the operating system is compromised by the malware, the antivirus software also becomes vulnerable. In this thesis, we present a novel approach through using an external device to enhance the host security by offloading the security solution from the host to the external device. We describe the design of the DeviceGuard framework that separate the security solution from the host and offload it to the external device, a Trusted Device. The architecture of the DeviceGuard consists of two components, the DeviceGuard application on the Trusted Device and a DeviceGuard daemon on the host. Our prototype based on Android Development Phone (ADP) shows the feasibilities and efficiency of our approach to provide security features including system file and user data integrity monitoring, secure signing and secure decryption. We use Bluetooth as the communication protocol between the host and the Trusted Device. Our experiment results indicates a practical Bluetooth throughput at about 2M Bytes per second is sufficient for short range communication between the host and the Trusted Device; Message digest with SHA-512, digital signing with 1024 bits signature and secure decryption with AES 256 bits on the Trusted device takes only the scale of 10? and 10? ms for 1K bytes and 1M bytes respectively which are also shows the feasibility and efficiency of the DeviceGuard solution. We also investigated the use of embedded system as the Trusted Device. Our solution takes advantage of the proliferation of devices, such as Smartphone, for stronger system and data security. / Master of Science

Doctor of Philosophy / Computing and Information Sciences / Scott A. DeLoach / Xinming (Simon) Ou / The Moving Target Defense (MTD) concept has been proposed as an approach to rebalance the security landscape by increasing uncertainty and apparent complexity for attackers, reducing their window of opportunity, and raising the costs of their reconnaissance and attack efforts. Intuitively, the idea of applying MTD techniques to a whole IT system should provide enhanced security; however, little research has been done to show that it is feasible or beneficial to the system’s security. This dissertation presents an MTD platform at the whole IT system level in which any component of the IT system can be automatically and reliably replaced with a fresh new one. A component is simply a virtual machine (VM) instance or a cluster of instances. There are a number of security benefits when leveraging such an MTD platform. Replacing a VM instance with a new one with the most up-to-date operating system and applications eliminates security problems caused by unpatched vulnerabilities and all the privileges the attacker has obtained on the old instance. Configuration parameters for the new instance, such as IP address, port numbers for services, and credentials, can be changed from the old ones, invalidating the knowledge the attackers already obtained and forcing them to redo the work to re-compromise the new instance. In spite of these obvious security benefits, building a system that supports live replacement with minimal to no disruption to the IT system’s normal operations is difficult. Modern enterprise IT systems have complex dependencies among services so that changing even a single instance will almost certainly disrupt the dependent services. Therefore, the replacement of instances must be carefully orchestrated with updating the settings of the dependent instances. This orchestration of changes is notoriously error-prone if done manually, however, limited tool support is available to automate this process. We designed and built a framework (ANCOR) that captures the requirements and needs of a whole IT system (in particular, dependencies among various services) and compiles them into a working IT system. ANCOR is at the core of the proposed MTD platform (ANCOR-MTD) and enables automated live instance replacements. In order to evaluate the platform’s practicality, this dissertation presents a series of experiments on multiple IT systems that show negligible (statistically non-significant) performance impacts. To evaluate the platform’s efficacy, this research analyzes costs versus security benefits by quantifying the outcome (sizes of potential attack windows) in terms of the number of adaptations, and demonstrates that an IT system deployed and managed using the proposed MTD platform will increase attack difficulty.

To date, the potential of on-line Dynamic Security Assessment (DSA) to monitor, alert, and enhance system security is constrained by the longer computational cycle time. Traditional techniques requiring extensive numerical computations make it challenging to complete the assessment within an acceptable time. Longer computational cycles produce obsolete security assessment results as the system operating point evolves continuously. This thesis presents a DSA algorithm, based on Transient Energy Function (TEF) method and machine learning, to enable frequent computational cycles in on-line DSA of power systems. The use of selected terms of the TEF as pre-processed input features for machine learning demonstrated the ability to successfully train a contingency-independent classifier that is capable of classifying stable and unstable operating points. The network is trained for current system topology and loading conditions. The classifier can be trained using a small dataset when the TEF terms are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. The test results demonstrate the potential of using the proposed technique for power system on-line DSA. Power system devices such as HVDC and FACTS can be included in the algorithm by incorporating the effective terms of a corresponding TEF. An on-line DSA system requires the integration of several functional components. The practicality of the proposed technique in terms of a) critical data communications aspects b) computational hardware requirements; and c) capabilities and limitations of the tools in use was tested using an implementation of an on-line DSA system. The test power system model was simulated using a real-time digital simulator. The other functional units were distributed over the Local Area Network (LAN). The implementation indicated that an acceptable computational cycle time can be achieved using the proposed method. In addition, the work carried out during this thesis has produced two tools that can be used for a) web-based automated data generation for power system studies; and b) testing of on-line DSA algorithms. / February 2017

<div><div><div><p>Provenance collection and analysis is one of the most important techniques used in analyzing computation system behaviors. For forensic analysis in enterprise environment, existing provenance systems are limited. On one hand, they tend to log many redundant and irrelevant events causing high runtime and space overhead as well as long investigation time. On the other hand, they lack the application specific provenance data, leading to ineffective investigation process. Moreover, emerging machine learning especially deep learning based artificial intelligence systems are hard to interpret and vulnerable to adversarial attacks. Using provenance information to analyze such systems and defend adversarial attacks is potentially very promising but not well-studied yet.</p><p><br></p><div><div><div><p>In this dissertation, I try to address the aforementioned challenges. I present an effective and efficient operating system level provenance data collector, ProTracer. It features the idea of alternating between logging and tainting to perform on-the-fly log filtering and reduction to achieve low runtime and storage overhead. Tainting is used to track the dependence relationships between system call events, and logging is performed only when useful dependencies are detected. I also develop MPI, an LLVM based analysis and instrumentation framework which automatically transfers existing applications to be provenance-aware. It requires the programmers to annotate the desired data structures used for partitioning, and then instruments the program to actively emit application specific semantics to provenance collectors which can be used for multiple perspective attack investigation. In the end, I propose a new technique named NIC, a provenance collection and analysis technique for deep learning systems. It analyzes deep learning system internal variables to generate system invariants as provenance for such systems, which can be then used to as a general way to detect adversarial attacks.</p></div></div></div></div></div></div>

<p>Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into the data, leading to concept drift that causes the anomaly detection to misclassify the actual attack as benign. Our goal is to estimate the vulnerability of an anomaly detection method to an unknown attack, in particular the expected</p> <p>minimum number of poison samples the adversary would need to succeed. Such an estimate is a necessary step in risk analysis: do we expect the anomaly detection to be sufficiently robust to be useful in the face of attacks? We analyze DBSCAN, LOF,</p> <p>one-class SVM as an anomaly detection method, and derive estimates for robustness to poisoning attacks. The analytical estimates are validated against the number of poison samples needed for the actual anomalies in standard anomaly detection test</p> <p>datasets. We then develop defense mechanism, based on the concept drift caused by the poisonous points, to identify that an attack is underway. We show that while it is possible to detect the attacks, it leads to a degradation in the performance of the</p> <p>anomaly detection method. Finally, we investigate whether the generated adversarial samples for one anomaly detection method transfer to another anomaly detection method.</p>