Digital Forensic Science - Vassil Roussev Synthesis Lectures on Information Security, Privacy, and Trust

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the introduction of digital computing and communication, the same general assumptions were taken to the cyber world, largely unchallenged. Although a detailed conceptual discussion is outside the intent of this text, we should note that the presence of persistent “digital traces” (broadly defined) is neither inevitable nor is it a “natural” consequence of the processing and communication of digital information. Such records of cyber interactions are the result of concious engineering decisions, ones not usually taken specifically for forensic purposes. This is a point we will return to shortly, as we work toward a definition that is more directly applicable to digital forensics.

      Any discussion on forensic evidence must inevitably begin with the Daubert standard—a reference to three landmark decisions by the Supreme Court of the United States: Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993); General Electric Co. v. Joiner, 522 U.S. 136 (1997); and Kumho Tire Co. v. Carmichael, 526 U.S. 137 (1999).

      In the words of Goodstein [78]: “The presentation of scientific evidence in a court of law is a kind of shotgun marriage between the two disciplines.… The Daubert decision is an attempt (not the first, of course) to regulate that encounter.”

      These cases set a new standard for expert testimony [11], overhauling the previous Frye standard of 1923 (Frye v. United States, 293 F. 1013, D.C. Cir. 1923). In brief, the Supreme Court instructed trial judges to become gatekeepers of expert testimony, and gave four basic criteria to evaluate the admissability of forensic evidence:

      1. The theoretical underpinnings of the methods must yield testable predictions by means of which the theory could be falsified.

      2. The methods should preferably be published in a peer-reviewed journal.

      3. There should be a known rate of error that can be used in evaluating the results.

      4. The methods should be generally accepted within the relevant scientific community.

      The court also emphasized that these standards are flexible and that the trial judge has a lot of leeway in determining admissability of forensic evidence and expert witness testimony. During legal proceedings, special Daubert hearings are often held in which the judge rules on the admissibility of expert witness testimony requested by the two sides.

      In other words, scientific evidence becomes forensic only if the court deems it admissible. It is a somewhat paradoxic situation that an evaluation of the scientific merits of a specific method is rendered by a judge, not scientists. There is no guarantee that the legal decision, especially in the short term, will be in agreement with the ultimate scientific consensus on the subject. The courts have a tendency to be conservative and skeptical with respect to new types of forensic evidence. The admissability decision also depends on the specific case, the skill of the lawyers on both sides, the communication skills of the expert witnesses, and a host of other factors that have nothing to do with scientific merit.

      The focus of this book is on the scientific aspect of the analytical methods and, therefore, we develop a more technical definition of digital forensic science.

      Early applications of digital forensic science emerged out of law enforcement agencies, and were initiated by investigators with some technical background, but no formal training as computer scientists. Through the 1990s, with the introduction and mass adoption of the Internet, the amount of data and the complexity of the systems investigated grew quickly. In response, digital forensic methods developed in an ad hoc, on-demand fashion, with no overarching methodology, or peer-reviewed venues. By the late 1990s, coordinated efforts emerged to formally define and organize the discipline, and to spell out best field practices in search, seizure, storage, and processing of digital evidence [126].

      In 2001, the first Digital Research Forensic Workshop was organized with the recognition that the ad hoc approach to digital evidence needed to be replaced by a systematic, multi-disciplinary effort to firmly establish digital forensic science as a rigorous discipline. The workshop produced an in-depth report outlining a research agenda and provided one of the most frequently cited definitions of digital forensic science [136]:

      Digital forensics: The use of scientifically derived and proven methods toward the preservation, collection, validation, identification, analysis, interpretation, documentation, and presentation of digital evidence derived from digital sources for the purpose of facilitating or furthering the reconstruction of events found to be criminal, or helping to anticipate unauthorized actions shown to be disruptive to planned operations.

      This definition, although primarily stressing the investigation of criminal actions, also includes an anticipatory element, which is typical of the notion of forensics in operational environments. The analysis there is performed primarily to identify the vector of attack and scope of a security incident; identifying adversary with any level of certainty is rare, and prosecution is not the typical outcome.

      In contrast, the reference definition provided by NIST a few years later [100] is focused entirely on the legal aspects of forensics, and emphasizes the importance of strict chain of custody:

      Digital forensics is considered the application of science to the identification, collection, examination, and analysis of data while preserving the integrity of the information and maintaining a strict chain of custody for the data. Data refers to distinct pieces of digital information that have been formatted in a specific way.

      Another way to describe these law-centric definitions is that they provide a litmus test for determining whether specific investigative tools and techniques qualify as being forensic. From a legal perspective, this open-ended definition is normal and works well as the admissability of all evidence gets decided during the legal proceedings.

      From the point of view of a technical discussion, however, such definitions are too generic to provide a meaningful starting point. Further, the chain of custody issues are primarily of procedural nature and do not bring up any notable technical problems. Since the goal of this book is to consider the technical aspects of digital forensics, it would be prudent to start with a working definition that is more directly related to our subject.

      We adopt the working definition first introduced in [154], which directly relates to the formal definition of computing in terms of Turing machines, and is in the spirit of Carrier’s computer history model (Section 3.3.2):

      Digital forensics is the process of reconstructing the relevant sequence of events that have led to the currently observable state of a target IT system or (digital) artifacts.

       Notes

      1. The notion of relevance is inherently case-specific, and a big part of a forensic analyst’s expertise is the ability to identify case-relevant evidence.

      2. Frequently, a critical component of the forensic analysis is the causal attribution of event sequence to specific human actors of the system (such as users and administrators).

      3. The provenance, reliability, and integrity of the data used as evidence are of primary importance.

      We view all efforts to perform system, or artifact, analysis after the fact as a form of forensics. This includes common activities, such as incident response and internal investigations, which almost never result in any legal actions. On balance, only a tiny fraction of forensic analyses make it to the courtroom as formal evidence; this should not constrain us from exploring the full spectrum of techniques for reconstructing the past of digital artifacts.

      The benefit of employing a broader view of forensic computing is that it helps us to identify closely related tools and

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