Methodology

MFP: The Mobile Forensic Platform

First published May 2005

Frank Adelstein
Senior Principal Scientist, ATC-NY

Abstract
Digital forensics experts perform investigations of machines for “triage” to see if there is a problem, as well as to gather evidence and run analyses. When the machines to be examined are geographically distributed, an investigator could benefit greatly if he could conduct the investigation, or even its initial stages, remotely.

The Mobile Forensic Platform (MFP) is a tool for performing remote network forensics. With it, investigators can gather evidence on a remote running system, maintain a copy of the original evidence (protected by a cryptographic hash), and run various analyses on the data to determine the next steps in the investigation (e.g., seize the machine, run tests, look elsewhere). The MFP maintains audit logs on all tasks it performs.

ATC-NY (formerly Odyssey Research Associates) has designed the framework for, and implemented a prototype of, the MFP. We have modeled the investigative process to define the tasks investigators perform. The framework defines the interface for each modular component in the process. The prototype serves as a proof-of-concept to demonstrate how the different components of the system will function together. We implemented the MFP on a laptop computer with a platform-independent web interface serving as the GUI. The investigator runs a web browser on his desktop computer, connecting remotely to the MFP’s web server to perform the investigative tasks.

The preliminary version of the MFP supports a small set of analysis tools that illustrate the potential of the MFP. In particular, we created 3 sample logfile analysis tools. They detect if the data in a log file has been tampered, and also served to define how the analysis tools fit into the overall MFP framework. We describe each of the tools.

1 Introduction
After a crime, investigators must perform forensic investigations on any computers that might contain evidence. Investigators could be military personnel, law enforcement officials, company employees doing internal investigations, or external consultants. Because skilled investigators may be unavailable and machines may be physically far apart, there is a need for remote network-based forensics that includes gathering and analyzing evidence. It is typically impractical for an investigator to download the necessary information across the net. Firewalls, resource constraints (i.e., bandwidth), and security policies may prevent that—and even if they don’t, an investigator may simply lack the resources to store it all. The goal of the MFP is to ease the burden on the investigator who needs to examine various, geographically dispersed sites.

2 The Need for Remote Forensics
A typical forensic investigator now performs the investigation by hand-reading mail and data files, checking for system activities through different log files, and verifying the consistency of the data through the time stamps associated with files on the file system. Protections such as firewalls often force the investigator to perform these tasks on-site.

The difficulties of performing a local analysis can limit the investigation. First, forensic software must be running on the local machine, and may have to be installed. Second, running such software locally risks damaging or contaminating data. Third, if the machine has been compromised, the investigation may produce suspect results – or worse, may alert the attacker. And finally, even if the forensic software does not alter the data (e.g., boot an O/S from a floppy or CD-ROM), it still makes the machine unavailable, and may disrupt mission critical services. Additionally, this method loses transient state information, such as running processes and network connections.

Working through a remote login also poses problems. The network may not be configured to allow such remote connections (e.g., because of a firewall or because the network contains classified data). The network connection may not be fast enough to support the transfer of a large amount of data; and transmitting data across a public network (the Internet) risks divulging too much internal information.

What is needed is an approach that combines the advantages of both methods, accessing data locally on a secure platform with known tools, and keeping raw data within the network (to provide data integrity and to limit communications overhead).

3 How the MFP Works
The Mobile Forensic Platform (MFP) is a stand-alone machine, connected to the same local subnet as the machine to be investigated, allowing for high-speed transfer of data. The investigator, or an administrator, connects to the MFP through a secured connection (either through the Internet via a VPN or a phone line), encrypting all data transferred between the machines, such as via HTTPS. The particular mechanisms that provide the connection and encryption are beyond the scope of this paper.

We envision two ways in which the MFP can be used. In Option One, a large organization with multiple geographically distributed sites could keep an MFP on-site, ready to be deployed. Before it was ever needed, it would be configured for that site (e.g., dynamic vs. static IP addresses, server names, services that are run, etc.). A system administrator could then deploy the MFP at the direction of the investigator, who need not be present. This would involve essentially little more than plugging it into the network and a power outlet, and turning it on. We minimize the risk of a “rouge” MFP, since an investigator controls it remotely, and only after authenticating himself with a password.

In Option Two, the MFP could be configured at the time it is deployed. The configuration should be straightforward and again could be done under the supervision of the investigator, via a phone call. In both situations, the investigator can begin gathering and analyzing evidence without having to go on-site.

The MFP tries to limit the amount of trust an organization must put into the investigator. In reality, he will have virtually unrestricted access to the machines that are under investigation. However, the MFP maintains its own audit log of every transaction performed, from every data file downloaded to every analysis or browsing of the data files.

4 The Investigative Process
We talked to a number of investigators and created a five-step description of the process they use, shown in Figure 1.

Figure 1 Investigative Process

The investigative process consists of:
1. acquiring evidence,
2. preserving evidence,
3. normalizing evidence,
4. running analyses, and
5. examining the results.

The only non-obvious steps are preserving and normalizing the evidence. Digital evidence is preserved by taking a cryptographically secure hash, such as MD5, of the raw data. An investigator can show that the data has not been changed at a later date by taking a hash of the data and comparing it to the original hash. Note that this implies that the original hash must be preserved, but this is easily done by having the investigator print the web page that displays the hash.

Data is normalized by converting it to a common format. For example, all timestamps and machine names must be in the same format (local time vs. UTC vs. Epoch for timestamps, and IP numbers vs. FQDN for machine names).

The entire process repeats as needed. We built the MFP to follow this process.

5 MFP Support for the Investigative Process
The MFP has a modular design, to simplify coding and testing as well as to allow flexibility and extensibility. Conceptually, the MFP has six layers, as shown in Figure 2. The input comes from a lower layer, is processed, and then is passed up to the next higher layer. The top layer represents the user interface, and the remaining five map into the steps of the investigative process shown in Figure 1.

Figure 2 MFP Architecture

5.1 Acquiring
The lowest layer of the MFP handles the gathering data from the various sources. The details of communicating with the device under investigation and obtaining its data are confined to this layer. By limiting the “device dependent” code, the upper levels of the MFP can work independent of how the data was obtained, and new drivers can be added transparently, to support new platforms.

The initial plan is for the MFP to use FTP, NFS, SMB, and SCP as methods to transfer files from a running system. To do so, of course, the MFP must have the proper credentials (e.g., user name and password). Note that this level obtains the data, but does not care what the data represents; at this level, all files are treated equally.

5.2 Preserving
Layer 2 preserves the data via a cryptographically secure hash function. Initially, we are supporting MD5, but support for other functions, such as SHA-1, can be added as desired. The MFP automatically preserves all evidence it acquires by taking an MD5 hash of the file. The hash can be viewed at any time. This process is the same for all data, since during this step all data is represented as a file on the MFP.

If the data is output resulting from running a script on the target machine, the MFP computes a hash immediately after the output is generated. If the data is a file residing on the target machine, the MFP computes a hash of the data on the machine and then computes a hash of the data once it has been acquired and verifies that the two hashes match. Regardless of the normalizations or later analyses performed, the investigator can always go back to and examine the original raw data.

5.3 Normalizing
Layer 3 normalizes the data. In order to maximize the usefulness of our analysis tools, we want them to work on multiple kinds of files (e.g., from different operating systems or running in different time zones). So the data in these files must be converted into some standard format. The normalizing process depends heavily upon the nature of the data. Data files that contain timestamps must represent time in the same way. Similarly, files that contain machine names or IP addresses must use the same representation for all data. Normalization will change the data, which is why we preserve the original in the previous layer. It is always possible to start from the original data file and rerun all subsequent steps to verify the procedure (as might be required in a court of law).

Currently, very little normalization is performed. File MAC times for Windows is stored as a 64 bit number, and is changed into a 32-bit Unix-style Epoch. So, millisecond precision is degraded into second precision. File times are presented in the time zone of the investigator (which can be changed on the fly). We currently support only one operating system; we expect we will need to normalize more data as we add support for additional OSs.

5.4 Running
The analysis tools reside in layers 4 and 5. Layer 4 contains the generic tools that can be used on all types of data. Currently, the only element in this layer is a data viewing tool. It allows the investigator to view the raw data and search for particular strings. Layer 5 contains analysis tools that are specifically designed to work with a certain type of data, for example a web server log file or a syslog log file.

5.5 Analyzing
While Figure 1 distinguishes running analyses and viewing the results as separate steps, the MFP combines these two activities. So layers 4 and 5 are also responsible for viewing the data. The analysis tools are designed to make it easier to view the data, and to see patterns and connections that would not be obvious by just looking at the raw data. We can provide some automated checking of common situations and problems, however we do assume that the MFP is guided by an investigator with expertise in the domain. The tools will aid in the investigation, allowing an efficient use of the investigator’s time; they will not and cannot replace the investigator.

5.6 User Interface
Layer 6, the user interface, allows the investigator to perform all tasks in the process by mediating all interactions between the MFP and other layers of the MFP. (The user does not interact with layers 2 and 3, which operate automatically, layer 6 mediates all interaction between the investigator and the MFP. The MFP uses a web-based interface to allow the maximum compatibility with different systems.

6 Initial Set of Analysis Tools
For our “proof of concept” we created three sample analysis tools, representative examples that help define the interface between the different tools in the MFP (specifically, the form of the input data and the type of data produced as output). We concentrated on performing analyses on Unix style syslog log files. Note that the tools operate only on a copy of the data files residing on the MFP. The tools are designed to only read the files and produce output displayed on the investigator’s web browser, never modifying the data. The tools check for log file consistency by looking for out of sequence entries, the size of gaps between the entries, and missing periodic entries. We describe the three tools below.

6.1 Monotonic
The first tool, the monotonicity check, verifies that entries in the logfile are in (ascending) chronological order. This is a simple, straightforward check. We developed this script primarily to define how to manage input and output flows and to define the format for the I/O data. It also served as a simple test of the functions that read and parse log files, which are used in the other tools. This script served as a first step towards the development of the other two more complex scripts.

6.2 Max-Gap
The purpose of max-gap analysis is to quickly spot anomalous gaps in the log files. It creates a histogram showing the distribution of “gap sizes” in the log file. For example, a typical server might be generating log file entries between 5-10 seconds, and once per second during busy periods. If an intruder were to delete, say, a 5 minute section of the log max-gap analysis would make that deletion stand out. The histogram divides the data into 10 “buckets” equal-sized on a logarithmic scale that is dynamically determined, depending on the ranges of data values. We use the following equation to compute the range of values that go into each bucket.

Given:

min = minimum gap size (dynamically determined),

max = maximum value of largest bin (dynamically determined by largest gap size),

binsmax = maximum number of histogram bins (set to 10),

k = minimum value of smallest bin (set to 1.0), and

gapi = the gap size for the ith data value,

the index of the bin, bin#, in which the data value gapi belongs (1 – 10) is computed as:

The tool then produces HTML-based output indicating, for each of the 10 buckets, the minimum and maximum values, and the number of items in each bucket. We may allow additional configuration parameters (such as choice of the number of buckets) in a later version of the MFP.

Figure 3 shows a screen shot from a sample max-gap analysis. The histogram shows a clear bimodal behavior in which part of the time the machine was active and the gaps were spaced together closely, within 10-20 seconds, and the rest of the time the machine was idle with large gaps (over one hour). If an intruder were active for a few minutes and then erased his activity, the anomalous gap would likely stand out. This example is meant to demonstrate how the tool works, not to assert that the results of this analysis are typical. The log file had very few entries and the machine was not a server. Nonetheless, the investigator can get a visual “fingerprint” of the structure of the log files and may be able to detect anomalies.

This tool can be effective when used in conjunction with other data points, for example comparing web, mail, ftp, and syslog activity. In addition, it could be used to establish a fingerprint of the system’s normal behavior, as a control.

Figure 3 Sample Max Gap Analysis

6.3 Periodic
The periodic tool detects the absence of log entries that should appear at periodic intervals. The input to the tool must include a list of which periodic events to examine, the periods of those events and a string-pattern (or regular expression) which can be used to identify each event in the log file.

7 Related Work
Some automated tools are available that aid in the investigation, but they are limited. The typical model used is that the machine is seized, the disk is imaged (i.e., duplicated), and the forensic analyses are performed on the duplicate disk. Tools such as Encase[1] and FIAT[2] are designed to work on a seized disk (or, more commonly, a duplicate of such a disk). The Encase Enterprise Edition now supports some remote forensic capability. It focuses mostly on files, rather than transient information relating to the running state of the machine and requires software to be installed on the target machine. The Coroner’s Toolkit (TCT) [3] must run on the system it is analyzing. The @stake Sleuth Kit (TASK) [4] is a tool kit built using tools from The Coroner’s Toolkit to perform analysis of Windows and Unix systems. The Autopsy Forensic Browser [5] is an HTML-based graphical interface to TASK. Like TCT, it requires local disk access in order to perform its diagnostics.

8 Current status/Future Plans
Phase I was completed in January 2002. We demonstrated the feasibility of the concept, using discussions with various investigators as a “sanity check.” We designed the plan for the full prototype. We created the structure and framework for the MFP and implemented selected components of it as a “proof of concept.” These components include pieces of the user interface as well as analysis tools. In the interim between Phase I and II, we have developed the initial demo MFP (presented at the Digital Forensics Research Workshop [6]).

In Phase II, which began on July 29, 2002, we will create a working prototype based on the work done in Phase I. We intend to work with investigators (as testers) to gather feedback and refine the MFP based on their comments. We will be adding new analysis tools and functionality (e.g., support for Windows event logs). Our goal is to work towards a Phase III, which ends with a dual-use, commercializable product.

9 Conclusion
Because expertise in forensic investigation is limited, it is vital to use investigators’ time as efficiently as possible. Our new technology, the Mobile Forensic Platform, will help investigators work more efficiently by performing routine forensic tasks via a remote connection.

Our initial work has defined the basic structure of the MFP and the types of tasks it will perform. We created a flexible, extensible system, a forensic toolbox to which new tools can easily be added as the need arises. For the next phase of the project, we will create a fully functional prototype that can be used in the field and will incorporate feedback from testers.

References

[1] Encase, distributed by Guidance Software, http://www.guidancesoftware.com/html/forensic_software.html

[2] Forensic Intrusion Analysis Tool (FIAT) was initially developed by MITRE and is being enhanced by the DoD Computer Forensics Lab (DCFL).

[3] The Coroner’s Toolkit, created by Dan Farmer and Wietse Venema, http://www.porcupine.org/forensics/tct.html

[4] The @stake Sleuth Kit (TASK), http://www.atstake.com/research/tools/task/.

[5] The Autopsy Forensic Browser, http://www.atstake.com/research/tools/autopsy/index.html

[6] Digital Forensics Research Workshop, Syracuse, NY, August 2002, http://www.dfrws.org/.

© 2003 International Journal of Digital Evidence

About the Author
Frank Adelstein is a senior principal scientist at ATC-NY (formerly Odyssey Research Associates). He is the PI on efforts in digital forensics, intrusion detection, and wireless security. He received his MS and PhD in Computer Science from The Ohio State University in 1991 and 1995, and his BS in Computer Engineering from The University of Michigan in 1988. He was a postdoc in the Computer Science Department at Cornell working for the Xerox Design Research Center before joining Odyssey.

His interests include a wide range of topics, including security, networks, multimedia, the web, and more. He also is a commercially rated private pilot. Currently, he is co-authoring a book on Mobile Computing, with his section focusing on security in mobile computing.

This article has been reproduced with the kind permission of Frank Adelstein and the International Journal of Digital Evidence

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