Electronic Evidence Locker: An Ontology for Electronic Evidence

With the rapid growth of crime data, overwhelming amounts of electronic evidence need to be stored and shared with the relevant agencies. Without addressing this challenge, the sharing of crime data and electronic evidence will be highly inefficient, and the resource requirements for this task will continue to increase. Relational database solutions face size limitations in storing larger amounts of crime data where each instance has unique attributes with unstructured nature. In this thesis, the Electronic Evidence Locker (EEL) was proposed and developed to address such problems. The EEL was built using a NoSQL database and a C# website for querying stored data. Baseline results were collected to measure the growth of required machine resources (in memory and time) using various test cases and larger datasets. The results showed that search time is more impacted by the search direction in the data than the addition of the query search conditions

Advanced Techniques for Improving the Efficacy of Digital Forensics Investigations

Digital forensics is the science concerned with discovering, preserving, and analyzing evidence on digital devices. The intent is to be able to determine what events have taken place, when they occurred, who performed them, and how they were performed. In order for an investigation to be effective, it must exhibit several characteristics. The results produced must be reliable, or else the theory of events based on the results will be flawed. The investigation must be comprehensive, meaning that it must analyze all targets which may contain evidence of forensic interest. Since any investigation must be performed within the constraints of available time, storage, manpower, and computation, investigative techniques must be efficient. Finally, an investigation must provide a coherent view of the events under question using the evidence gathered. Unfortunately the set of currently available tools and techniques used in digital forensic investigations does a poor job of supporting these characteristics. Many tools used contain bugs which generate inaccurate results; there are many types of devices and data for which no analysis techniques exist; most existing tools are woefully inefficient, failing to take advantage of modern hardware; and the task of aggregating data into a coherent picture of events is largely left to the investigator to perform manually. To remedy this situation, we developed a set of techniques to facilitate more effective investigations. To improve reliability, we developed the Forensic Discovery Auditing Module, a mechanism for auditing and enforcing controls on accesses to evidence. To improve comprehensiveness, we developed ramparser, a tool for deep parsing of Linux RAM images, which provides previously inaccessible data on the live state of a machine. To improve efficiency, we developed a set of performance optimizations, and applied them to the Scalpel file carver, creating order of magnitude improvements to processing speed and storage requirements. Last, to facilitate more coherent investigations, we developed the Forensic Automated Coherence Engine, which generates a high-level view of a system from the data generated by low-level forensics tools. Together, these techniques significantly improve the effectiveness of digital forensic investigations conducted using them

Extracting the Windows Clipboard from Physical Memory

When attempting to reconstruct the events leading up to a cyber security incident, one potentially important piece of information is the clipboard (Prosise et al., 2003). The clipboard has been present in Windows since Windows 3.1 and is the mechanism for transferring information from one application to another through copy and pasting actions. Being able to retrieve the last file copied or the last password used may provide investigators with invaluable information during a forensic investigation. This paper describes the Windows clipboard structure and the process of retrieving copy/paste information from Windows XP, Vista, and Windows 7 (both 32 bit and 64 bit) memory captures with data from applications including Notepad, Microsoft Word, and Microsoft Excel

Windows Operating Systems Agnostic Memory Analysis

Memory analysis is an integral part of any computer forensic investigation, providing access to volatile data not found on a drive image. While memory analysis has recently made significant progress, it is still hampered by hard-coded tools that cannot generalize beyond the specific operating system and version they were developed for. This paper proposes using the debug structures embedded in memory dumps and Microsoft’s program database (PDB) files to create a flexible tool that takes an arbitrary memory dump from any of the family of Windows NT operating systems and extract process, configuration, and network activity information. The debug structures and PDB files are incorporated into a memory analysis tool and tested against dumps from 32-bit Windows XP with physical address extensions (PAE) enabled and disabled, 32-bit Windows Vista with PAE enabled, and 64-bit Windows 7 systems. The results show the analysis tool is able to identify and parse an arbitrary memory dump and extract process, registry, and network communication information

Formalization and Detection of Host-Based Code Injection Attacks in the Context of Malware

The Host-Based Code Injection Attack (HBCIAs) is a technique that malicious software utilizes in order to avoid detection or steal sensitive information. In a nutshell, this is a local attack where code is injected across process boundaries and executed in the context of a victim process. Malware employs HBCIAs on several operating systems including Windows, Linux, and macOS. This thesis investigates the topic of HBCIAs in the context of malware. First, we conduct basic research on this topic. We formalize HBCIAs in the context of malware and show in several measurements, amongst others, the high prevelance of HBCIA-utilizing malware. Second, we present Bee Master, a platform-independent approach to dynamically detect HBCIAs. This approach applies the honeypot paradigm to operating system processes. Bee Master deploys fake processes as honeypots, which are attacked by malicious software. We show that Bee Master reliably detects HBCIAs on Windows and Linux. Third, we present Quincy, a machine learning-based system to detect HBCIAs in post-mortem memory dumps. It utilizes up to 38 features including memory region sparseness, memory region protection, and the occurence of HBCIA-related strings. We evaluate Quincy with two contemporary detection systems called Malfind and Hollowfind. This evaluation shows that Quincy outperforms them both. It is able to increase the detection performance by more than eight percent

Acquisition and Forensic Analysis of Volatile Data Stores

The advent of more witted threats against typical computer systems demonstrates a need for forensic analysis of memory-resident data in addition to the conventional static analysis common today. Some tools are starting to become available to duplicate various types of volatile data stores. Once the data store has been duplicated, current forensic procedures have no vector for extrapolating further information from the duplicate. This thesis is focused on providing the groundwork for performing forensic investigations on the data that is typically stored in a volatile data store, such as system RAM, while creating as small an impact as possible to the state of a system. It is intended that this thesis will give insight to obtaining more post incident response information along with a smaller impact to potential evidence when compared to typical incident response procedures

On the Identification of Information Extracted from Windows Physical Memory

Forensic investigation of the physical memory of computer systems is gaining the attention of experts in the digital forensics community. Forensic investigators find it helpful to seize and capture data from the physical memory and perform post-incident analysis when identifying potential evidence. However, there have been few investigations which have identified the quantity and quality of information that can be recovered from only the computer system memory (RAM) while the application is still running. In this paper, we present the results of investigations carried out to identify relevant information that has been extracted from the physical memory of computer systems running Windows XP. We found fragments of partial evidence from allocated memory segments. This evidence was dispersed in the physical memory that had been allocated to the application. The identification of this information is useful to forensic investigators as this approach can uncover what a user is doing on the application which can be used as evidence

Windows Driver Memory Analysis: A Reverse Engineering Methodology

In a digital forensics examination, the capture and analysis of volatile data provides significant information on the state of the computer at the time of seizure. Memory analysis is a premier method of discovering volatile digital forensic information. While much work has been done in extracting forensic artifacts from Windows kernel structures, less focus has been paid to extracting information from Windows drivers. There are two reasons for this: (1) source code for one version of the Windows kernel (but not associated drivers) is available for educational use and (2) drivers are generally called asynchronously and contain no exported functions. Therefore, finding the handful of driver functions of interest out of the thousands of candidates makes reverse code engineering problematic at best. Developing a methodology to minimize the effort of analyzing these drivers, finding the functions of interest, and extracting the data structures of interest is highly desirable. This paper provides two contributions. First, it describes a general methodology for reverse code engineering of Windows drivers memory structures. Second it applies the methodology to tcpip.sys, a Windows driver that controls network connectivity. The result is the extraction from tcpip.sys of the data structures needed to determine current network connections and listeners from the 32 and 64 bit versions of Windows Vista and Windows 7. Manipulation (DKOM), tcpip.sys, Windows 7, Windows Vista. 2000 MSC: 60, 490

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we present new detection methods, which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA enabled GPU hardware to speed-up memory forensics. All three ideas are currently a work in progress

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we will present new detection methods which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows’ memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf–Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA-enabled GPU hardware, to speed-up memory forensics. All three ideas are currently a work in progress. Keywords: rootkit detection, anti-forensics, memory analysis, scattered fragments, anticipatory enhancement, CUDA