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Über dieses Buch

Digital forensics deals with the acquisition, preservation, examination, analysis and presentation of electronic evidence. Networked computing, wireless communications and portable electronic devices have expanded the role of digital forensics beyond traditional computer crime investigations. Practically every crime now involves some aspect of digital evidence; digital forensics provides the techniques and tools to articulate this evidence. Digital forensics also has myriad intelligence applications. Furthermore, it has a vital role in information assurance -- investigations of security breaches yield valuable information that can be used to design more secure systems. Advances in Digital Forensics VII describes original research results and innovative applications in the discipline of digital forensics. In addition, it highlights some of the major technical and legal issues related to digital evidence and electronic crime investigations. The areas of coverage include: Themes and Issues, Forensic Techniques, Fraud and Malware Investigations, Network Forensics, and Advanced Forensic Techniques. This book is the 7th volume in the annual series produced by the International Federation for Information Processing (IFIP) Working Group 11.9 on Digital Forensics, an international community of scientists, engineers and practitioners dedicated to advancing the state of the art of research and practice in digital forensics. The book contains a selection of 21 edited papers from the 7th Annual IFIP WG 11.9 International Conference on Digital Forensics, held at the National Center for Forensic Science, Orlando, Florida, USA in the spring of 2011. Advances in Digital Forensics VII is an important resource for researchers, faculty members and graduate students, as well as for practitioners and individuals engaged in research and development efforts for the law enforcement and intelligence communities. Gilbert Peterson is an Associate Professor of Computer Engineering at the Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA. Sujeet Shenoi is the F.P. Walter Professor of Computer Science at the University of Tulsa, Tulsa, Oklahoma, USA.





1. The State of the Science of Digital Evidence Examination

This paper examines the state of the science and the level of consensus in the digital forensics community regarding digital evidence examination. The results of this study indicate that elements of science and consensus are lacking in some areas and are present in others. However, the study is small and of limited scientific value. Much more work is required to evaluate the state of the science of digital evidence examination.
Fred Cohen, Julie Lowrie, Charles Preston

2. An Investigative Framework for Incident Analysis

A computer incident occurs in a larger context than just a computer network. Because of this, investigators need a holistic forensic framework to analyze incidents in their entire context. This paper presents a framework that organizes incidents into social, logical and physical levels in order to analyze them in their entirety (including the human and physical factors) rather than from a purely technical viewpoint. The framework applies the six investigative questions – who, what, why, when, where and how – to the individual stages of an incident as well as to the entire incident. The utility of the framework is demonstrated using an insider threat case study, which shows where the evidence may be found in order to conduct a successful investigation.
Clive Blackwell

3. Cloud Forensics

Cloud computing may well become one of the most transformative technologies in the history of computing. Cloud service providers and customers have yet to establish adequate forensic capabilities that could support investigations of criminal activities in the cloud. This paper discusses the emerging area of cloud forensics, and highlights its challenges and opportunities.
Keyun Ruan, Joe Carthy, Tahar Kechadi, Mark Crosbie



4. Searching Massive Data Streams Using Multipattern Regular Expressions

This paper describes the design and implementation of lightgrep, a multipattern regular expression search tool that efficiently searches massive data streams. lightgrep addresses several shortcomings of existing digital forensic tools by taking advantage of recent developments in automata theory. The tool directly simulates a nondeterministic finite automaton, and incorporates a number of practical optimizations related to searching with large pattern sets.
Jon Stewart, Joel Uckelman

5. Fast Content-Based File Type Identification

Digital forensic examiners often need to identify the type of a file or file fragment based on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds up classification by randomly sampling file blocks. Experimental results demonstrate that up to a fifteen-fold reduction in computational time can be achieved with limited impact on accuracy.
Irfan Ahmed, Kyung-Suk Lhee, Hyun-Jung Shin, Man-Pyo Hong

6. Case-Based Reasoning in Live Forensics

The traditional forensic search and seizure process employed by law enforcement is not always appropriate given large data volumes and the potential of hard drive encryption. This paper proposes a framework built on case-based reasoning to support a live forensic response during the search and seizure process. The framework assists a first responder by identifying the risks and the procedures to ensure the optimal collection of evidence based on prior cases. Test results demonstrate that the framework provides valuable assistance to first responders, reducing the time taken to complete a response and increasing the likelihood of a successful conclusion.
Bruno Hoelz, Celia Ralha, Frederico Mesquita

Assembling Metadata for Database Forensics

Since information is often a primary target in a computer crime, organizations that store their information in database management systems (DBMSs) must develop a capability to perform database forensics. This paper describes a database forensic method that transforms a DBMS into the required state for a database forensic investigation. The method segments a DBMS into four abstract layers that separate the various levels of DBMS metadata and data. A forensic investigator can then analyze each layer for evidence of malicious activity. Tests performed on a compromised PostgreSQL DBMS demonstrate that the segmentation method provides a means for extracting the compromised DBMS components.
Hector Beyers, Martin Olivier, Gerhard Hancke

Forensic Leak Detection for Business Process Models

This paper presents a formal forensic technique based on information flow analysis to detect data and information leaks in business process models. The approach can be uniformly applied to the analysis of process specifications and the log files generated during process execution. The Petri net dialect IF net is used to provide a common basis for the formalization of isolation properties, the representation of business process specifications and their analysis. The utility of the approach is illustrated using an eHealth case study.
Rafael Accorsi, Claus Wonnemann

Analyzing Stylometric Approaches to Author Obfuscation

Authorship attribution is an important and emerging security tool. However, just as criminals may wear gloves to hide their fingerprints, so too may criminal authors mask their writing styles to escape detection. Most authorship studies have focused on cooperative and/or unaware authors who do not take such precautions. This paper analyzes the methods implemented in the Java Graphical Authorship Attribution Program (JGAAP) against essays in the Brennan-Greenstadt obfuscation corpus that were written in deliberate attempts to mask style. The results demonstrate that many of the more robust and accurate methods implemented in JGAAP are effective in the presence of active deception.
Patrick Juola, Darren Vescovi



Detecting Fraud Using Modified Benford Analysis

Large enterprises frequently enforce accounting limits to reduce the impact of fraud. As a complement to accounting limits, auditors use Benford analysis to detect traces of undesirable or illegal activities in accounting data. Unfortunately, the two fraud fighting measures often do not work well together. Accounting limits may significantly disturb the digit distribution examined by Benford analysis, leading to high false alarm rates, additional investigations and, ultimately, higher costs. To better handle accounting limits, this paper describes a modified Benford analysis technique where a cut-off log-normal distribution derived from the accounting limits and other properties of the data replaces the distribution used in Benford analysis. Experiments with simulated and real-world data demonstrate that the modified Benford analysis technique significantly reduces false positive errors.
Christian Winter, Markus Schneider, York Yannikos

Detecting Collusive Fraud in Enterprise Resource Planning Systems

As technology advances, fraud is becoming increasingly complicated and difficult to detect, especially when individuals collude. Surveys show that the median loss from collusive fraud is much greater than fraud perpetrated by individuals. Despite its prevalence and potentially devastating effects, internal auditors often fail to consider collusion in their fraud assessment and detection efforts. This paper describes a system designed to detect collusive fraud in enterprise resource planning (ERP) systems. The fraud detection system aggregates ERP, phone and email logs to detect collusive fraud enabled via phone and email communications. The performance of the system is evaluated by applying it to the detection of six fraudulent scenarios involving collusion.
Asadul Islam, Malcolm Corney, George Mohay, Andrew Clark, Shane Bracher, Tobias Raub, Ulrich Flegel

Analysis of Back-Doored Phishing Kits

This paper analyzes the “back-doored” phishing kits distributed by the infamous Mr-Brain hacking group of Morocco. These phishing kits allow an additional tier of cyber criminals to access the credentials of Internet victims. Several drop email obfuscation methods used by the hacking group are also discussed.
Heather McCalley, Brad Wardman, Gary Warner

Identifying Malware Using Cross-Evidence Correlation

This paper proposes a new correlation method for the automatic identification of malware traces across multiple computers. The method supports forensic investigations by efficiently identifying patterns in large, complex datasets using link mining techniques. Digital forensic processes are followed to ensure evidence integrity and chain of custody.
Anders Flaglien, Katrin Franke, Andre Arnes

Detecting Mobile Spam Botnets Using Artificial immune Systems

Malicious software infects large numbers of computers around the world. Once compromised, the computers become part of a botnet and take part in many forms of criminal activity, including the sending of unsolicited commercial email or spam. As mobile devices become tightly integrated with the Internet, associated threats such as botnets have begun to migrate onto the devices. This paper describes a technique based on artificial immune systems to detect botnet spamming programs on Android phones. Experimental results demonstrate that the botnet detection technique accurately identifies spam. The implementation of this technique could reduce the attractiveness of mobile phones as a platform for spammers.
Ickin Vural, Hein Venter



An FPGA System for Detecting Malicious DNS Network Traffic

Billions of legitimate packets traverse computer networks every day. Unfortunately, malicious traffic also traverses these same networks. An example is traffic that abuses the Domain Name System (DNS) protocol to exfiltrate sensitive data, establish backdoor tunnels or control botnets. This paper describes the TRAPP-2 system, an extended version of the Tracking and Analysis for Peer-to-Peer (TRAPP) system, which detects BitTorrent and Voice over Internet Protocol (VoIP) traffic. TRAPP-2 is designed to detect a DNS packet, extract the packet payload, compare the data against a hash list and, if the packet is suspicious, log it for future analysis. Results show that the TRAPP-2 system captures 91.89% of DNS packets of interest under a 93.7% network load (937 Mbps). Also, as the hash list size is increased from 1,000 to 131,072,000 unique items, each doubling of the hash list size results in a mean increase of approximately 16 CPU cycles. These results demonstrate the ability of TRAPP-2 to detect traffic of interest under a saturated network load while maintaining large hash lists.
Brennon Thomas, Barry Mullins, Gilbert Peterson, Robert Mills

Router and Interface Marking for Network Forensics

The primary aim of network forensics is to trace attackers and obtain evidence for possible prosecution. Many traceback techniques exist, but most of them focus on distributed denial of service (DDoS) attacks. This paper presents a novel traceback technique that deterministically marks the interface number and the address of the router from which each outgoing packet entered the network. An analysis against various traceback metrics demonstrates that the technique enhances network attack attribution.
Emmanuel Pilli, Ramesh Joshi, Rajdeep Niyogi

Extracting Evidence Related to VoIP Calls

The Voice over Internet Protocol (VoIP) is designed for voice communications over IP networks. To use a VoIP service, an individual only needs a user name for identification. In comparison, the public switched telephone network requires detailed information from a user before creating an account. The limited identity information requirement makes VoIP calls appealing to criminals. In addition, due to VoIP call encryption, conventional eavesdropping and wiretapping methods are ineffective. Forensic investigators thus require alternative methods for recovering evidence related to VoIP calls. This paper describes a digital forensic tool that extracts and analyzes VoIP packets from computers used to make VoIP calls.
David Irwin, Jill Slay



Sensitivity Analysis of Bayesian Networks Used in Forensic Investigations

Research on using Bayesian networks to enhance digital forensic investigations has yet to evaluate the quality of the output of a Bayesian network. The evaluation can be performed by assessing the sensitivity of the posterior output of a forensic hypothesis to the input likelihood values of the digital evidence. This paper applies Bayesian sensitivity analysis techniques to a Bayesian network model for the well-known Yahoo! case. The analysis demonstrates that the conclusions drawn from Bayesian network models are statistically reliable and stable for small changes in evidence likelihood values.
Michael Kwan, Richard Overill, Kam-Pui Chow, Hayson Tse, Frank Law, Pierre Lai

Steganographic Techniques for Hiding Data in SWF Files

Small Web Format (SWF) or Flash files are widely used on the Internet to provide Rich Internet Applications (RIAs). This makes SWF files an excellent candidate for disseminating hidden data. However, digital forensic investigators are unable to detect and extract the hidden data because limited information is available about the techniques used to hide data in SWF files. This paper investigates several data insertion techniques for hiding data in SWF files. The techniques include appending data to an SWF file, adding an extra Metadata tag, creating a custom Definition tag, and replacing fill bits with hidden data. Experimental results obtained with a simple SWF (version 10) file are used to evaluate the effectiveness of the data hiding techniques and identify the artifacts that remain.
Mark-Anthony Fouche, Martin Olivier

Evaluating Digital Forensic Options for the Apple iPad

The iPod Touch, iPhone and iPad from Apple are among the most popular mobile computing platforms in use today. These devices are of forensic interest because of their high adoption rate and potential for containing digital evidence. The uniformity in their design and underlying operating system (iOS) also allows forensic tools and methods to be shared across product types. This paper analyzes the tools and methods available for conducting forensic examinations of the Apple iPad. These include commercial software products, updated methodologies based on existing jailbreaking processes and the analysis of the device backup contents provided by iTunes. While many of the available commercial tools offer promise, the results of our analysis indicate that most comprehensive examination of the iPad requires jailbreaking to perform forensic duplication and manual analysis of its media content.
Andrew Hay, Dennis Krill, Benjamin Kuhar, Gilbert Peterson

Forensic Analysis of Plug Computers

A plug computer is essentially a cross between an embedded computer and a traditional computer, and with many of the same capabilities. However, the architecture of a plug computer makes it difficult to apply commonly used digital forensic methods. This paper describes methods for extracting and analyzing digital evidence from plug computers. Two popular plug computer models are examined, the SheevaPlug and the Pogoplug.
Scott Conrad, Greg Dorn, Philip Craiger
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