Design, Implementation and Experiments for Moving Target Defense Framework

The traditional defensive security strategy for distributed systems employs well-established defensive techniques such as; redundancy/replications, firewalls, and encryption to prevent attackers from taking control of the system. However, given sufficient time and resources, all these methods can be defeated, especially when dealing with sophisticated attacks from advanced adversaries that leverage zero-day exploits

Improving Memory Forensics Through Emulation and Program Analysis

Memory forensics is an important tool in the hands of investigators. However, determining if a computer is infected with malicious software is time consuming, even for experts. Tasks that require manual reverse engineering of code or data structures create a significant bottleneck in the investigative workflow. Through the application of emulation software and symbolic execution, these strains have been greatly lessened, allowing for faster and more thorough investigation. Furthermore, these efforts have reduced the barrier for forensic investigation, so that reasonable conclusions can be drawn even by non-expert investigators. While previously Volatility had allowed for the detection of malicious hooks and injected code with an insurmountably high false positive rate, the techniques presented in the work have allowed for a much lower false positive rate automatically, and yield more detailed information when manual analysis is required. The second contribution of this work is to improve the reliability of memory forensic tools. As it currently stands, if some component of the operating system or language runtime has been updated, the task of verifying that these changes do not affect the correctness of investigative tools involves a large reverse engineering effort, and significant domain knowledge, on the part of whoever maintains the tool. Through modifications of the techniques used in the hook analysis, this burden can be lessened or eliminated by comparing the last known functionality to the new functionality. This allows the tool to be updated quickly and effectively, so that investigations can proceed without issue

Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field

Measuring the Semantic Integrity of a Process Self

The focus of the thesis is the definition of a framework to protect a process from attacks against the process self, i.e. attacks that alter the expected behavior of the process, by integrating static analysis and run-time monitoring. The static analysis of the program returns a description of the process self that consists of a context-free grammar, which defines the legal system call traces, and a set of invariants on process variables that hold when a system call is issued. Run-time monitoring assures the semantic integrity of the process by checking that its behavior is coherent with the process self returned by the static analysis. The proposed framework can also cover kernel integrity to protect the process from attacks from the kernel-level. The implementation of the run-time monitoring is based upon introspection, a technique that analyzes the state of a computer to rebuild and check the consistency of kernel or user-level data structures. The ability of observing the run-time values of variables reduces the complexity of the static analysis and increases the amount of information that can be extracted on the run-time behavior of the process. To achieve transparency of the controls for the process while avoiding the introduction of special purpose hardware units that access the memory, the architecture of the run-time monitoring adopts virtualization technology and introduces two virtual machines, the monitored and the introspection virtual machines. This approach increases the overall robustness because a distinct virtual machine, the introspection virtual machine, applies introspection in a transparent way both to verify the kernel integrity and to retrieve the status of the process to check the process self. After presenting the framework and its implementation, the thesis discusses some of its applications to increase the security of a computer network. The first application of the proposed framework is the remote attestation of the semantic integrity of a process. Then, the thesis describes a set of extensions to the framework to protect a process from physical attacks by running an obfuscated version of the process code. Finally, the thesis generalizes the framework to support the efficient sharing of an information infrastructure among users and applications with distinct security and reliability requirements by introducing highly parallel overlays

Doctor of Philosophy

dissertationA modern software system is a composition of parts that are themselves highly complex: operating systems, middleware, libraries, servers, and so on. In principle, compositionality of interfaces means that we can understand any given module independently of the internal workings of other parts. In practice, however, abstractions are leaky, and with every generation, modern software systems grow in complexity. Traditional ways of understanding failures, explaining anomalous executions, and analyzing performance are reaching their limits in the face of emergent behavior, unrepeatability, cross-component execution, software aging, and adversarial changes to the system at run time. Deterministic systems analysis has a potential to change the way we analyze and debug software systems. Recorded once, the execution of the system becomes an independent artifact, which can be analyzed offline. The availability of the complete system state, the guaranteed behavior of re-execution, and the absence of limitations on the run-time complexity of analysis collectively enable the deep, iterative, and automatic exploration of the dynamic properties of the system. This work creates a foundation for making deterministic replay a ubiquitous system analysis tool. It defines design and engineering principles for building fast and practical replay machines capable of capturing complete execution of the entire operating system with an overhead of several percents, on a realistic workload, and with minimal installation costs. To enable an intuitive interface of constructing replay analysis tools, this work implements a powerful virtual machine introspection layer that enables an analysis algorithm to be programmed against the state of the recorded system through familiar terms of source-level variable and type names. To support performance analysis, the replay engine provides a faithful performance model of the original execution during replay

A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis

Now a day the threat of malware is increasing rapidly. A software that sneaks to your computer system without your knowledge with a harmful intent to disrupt your computer operations. Due to the vast number of malware, it is impossible to handle malware by human engineers. Therefore, security researchers are taking great efforts to develop accurate and effective techniques to detect malware. This paper presents a semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based. The Signature-based technique is largely used today by anti-virus software to detect malware, is fast and capable to detect known malware. However, it is not effective in detecting zero-day malware and it is easily defeated by malware that use obfuscation techniques. Likewise, a considerable false positive rate and high amount of scanning time are the main limitations of heuristic-based techniques. Alternatively, memory analysis is a promising technique that gives a comprehensive view of malware and it is expected to become more popular in malware analysis. The main contributions of this paper are: (1) providing an overview of malware types and malware detection approaches, (2) discussing the current malware analysis techniques, their findings and limitations, (3) studying the malware obfuscation, attacking and anti-analysis techniques, and (4) exploring the structure of memory-based analysis in malware detection. The detection approaches have been compared with each other according to their techniques, selected features, accuracy rates, and their advantages and disadvantages. This paper aims to help the researchers to have a general view of malware detection field and to discuss the importance of memory-based analysis in malware detection

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Android Memory Capture and Applications for Security and Privacy

The Android operating system is quickly becoming the most popular platform for mobiledevices. As Android’s use increases, so does the need for both forensic and privacy toolsdesigned for the platform. This thesis presents the first methodology and toolset for acquiringfull physical memory images from Android devices, a proposed methodology for forensicallysecuring both volatile and non-volatile storage, and details of a vulnerability discovered by theauthor that allows the bypass of the Android security model and enables applications to acquirearbitrary permissions