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CHIPS Articles: Cybersecurity Analysis through Binary Forensics and Distributed Computing to Secure Naval Networks

Cybersecurity Analysis through Binary Forensics and Distributed Computing to Secure Naval Networks
By Arijit Das, Abhinav Arya and Jim Zhou - January-March 2018
Overview

Cybersecurity is of utmost importance to the U.S. Navy. With thousands of operational computers and networks, the Navy requires a reliable method of detecting cybersecurity and network vulnerabilities and attacks. Fortunately, there is a reliable and accurate method of unearthing these threats to military machines by analyzing system and network logs.

The Windows Event Log

Many naval computers run on the Microsoft Windows operating system. Windows includes software that monitors network and security alerts and records them in the Windows Event Log. Event logs contain information critical to investigating the history and validating the security of computers.

Microsoft updated the event log file with the release of Windows 7 by obfuscating the file structure and encoding the data in a proprietary binary format, exhibited as: .evtx. These event logs can only be viewed through the Windows Event Viewer, a graphical user interface that cannot be parsed by automated systems. In addition, since the event logs are encoded in a binary format, traditional text processing methods cannot be applied, compounding the difficulty of automating event log processing.

Objective

To streamline the process of updating naval computers and servers to the latest Windows operating system, the Navy desires a software tool to decode the complex binary structure of Windows event logs and extract relevant event information corresponding to critical security messages and alerts.

Binary Forensics

Windows event logs are encoded in a proprietary binary structure that prevents someone or some program from easily accessing the valuable information stored within them. Fortunately, research conducted by the Digital Forensics Research Conference (DFRWS) began to decrypt the binary structure of event logs.

According to Andreas Shuster’s research paper presented at DFRWS, “Introducing the Microsoft Vista Log File Format,” the Windows event log is comprised of three sections: the file header, chunk and event record. The chunk is merely a container for all the event records, which house the vital information regarding specific security alerts and events. Navigating to the individual event records is a relatively simple task. The start of every event record is marked by a sequence of two bytes: 0x2a 0x2a, which translates to: ** in ASCII. From here, it becomes much more difficult to locate critical information.

The remainder of the event record is encapsulated in a binary XML structure. It is similar to a regular XML document, except all the opening and closing tags have been converted to binary tokens. Further, the content of the XML is not stored inside the tags, meaning that Windows separates the content (the data regularly stored within XML tags) from the structure (opening/closing tags). Microsoft elected to do this to reduce disk space usage, as the structure of the XML does not have to be replicated for every event in the event record. Therefore, all the information necessary for determining the characteristics of an event is stored separately from the binary XML, in a structure known as the substitution array.

Figure 1 depicts the binary sequences that form an individual event record. The sequence: 0x2a 0x2a, indicates the beginning of an event record. For the purpose of extracting information, the binary XML section can be skipped because the substitution array holds the relevant data to analyze cybersecurity.

Although Shuster mentions that the sequence: 0x14 0x00 0x00 0x00, is the sole indicator of the start of the substitution array, examination of several event records proves that the sequence: 0x12 0x00 0x00 0x00, can also indicate the start of a substitution array, as demonstrated by Figure 1. In cases where 0x14 specifies the start of the substitution array, the event identification (ID) is located at the 83rd and 84th byte after the header.

For 0x12 substitution arrays, the event ID comprises the 75th and 76th byte after the header, 0x10 0x12, in Figure 1. To convert this sequence to an identifiable decimal event ID, reverse the order of the bytes (because Windows stores data in a Little-Endian format) and perform a hexadecimal to decimal conversion. 0x10 0x12 therefore converts to the event: ID 4624.

Table 1 maps select event IDs to their corresponding security message or alert. The event record, depicted in Figure 1, is therefore an alert that an account successfully logged on to the computer, given by its 4624 event ID. For a full description of event IDs relating to security recorded in Windows Event Logs, please visit the Microsoft website.

Obtaining the time that the security alert was raised is a slightly more challenging task since the 8 bytes that correspond to the “time created” are not always stored at the same location. Additionally, the time is stored as a 64-bit value representing the number of 100-nanosecond intervals since Jan. 1, 1601, requiring a conversion to UNIX time for proper display. The bytes correlating to the time that the event record portrayed by Figure 1 was created are 0x48 0x01 0x58 0x5c 0x33 0x0d 0xd3 0x01, which represent 08/04/2017 08:07:16 AM PDT, meaning that a user logged on to the computer from which this event log was obtained at 8:07 AM on August 4, 2017.

By acquiring the event ID and the time the security alert was raised, the Navy can begin to pinpoint any potential cybersecurity threats harboring within naval computers and servers. The Navy can monitor event logs for any dangerous events, such as those with event IDs of 4649 (replay attack detected) or 4724 (password reset attempt) and so forth.

Distributed Computing

The Navy desires a cross-platform software tool that can analyze terabytes of event log data to help pinpoint potential cybersecurity threats to military machines. Following the decryption of the event log binary file format, a preliminary event log parser in Java was developed. This program, although capable of accurately extracting the security event data, processed the event logs sequentially and was therefore inefficient. Because the Navy possesses at least 1 gigabyte of event log data, a more efficient algorithm or programming design was needed.

Figure 2 depicts the output of a sequential Java program that parsed a 20 MB security event log with more than 30,000 events. Although this method satisfies the objective of extracting data from event logs, a more efficient solution can be implemented by leveraging distributed computing.

Hadoop is an open-source programming framework that supports distributed computing. The Hadoop Distributed File System (HDFS) enables computers in a cluster to interact with one another and share the load of storing and processing files.

Figure 3 portrays the software architecture of HDFS. HDFS consists of a talker, or communicator server (NameNode), and several storage servers (DataNodes). The NameNode communicates with all the DataNodes, and directs the organization and replication of data within HDFS. When a file is uploaded to HDFS it immediately becomes immutable, that is it cannot be further edited. Since HDFS is equipped to handle so called “Big Data,” it typically divides large files into 64 MB chunks. Each chunk is replicated and distributed among the DataNodes, as directed by the NameNode.

Figure 3 depicts three different 20 MB event log files (orange, yellow, green). Since each file is less than 64 MB, it is not divided into blocks, but is rather directly replicated once, with each copy stored on different servers. This organization offers two distinct advantages: (1) If a server crashes or loses memory, a copy of the file is always saved since every file is replicated multiple times and is stored on separate nodes; and (2) The design enables distributed computing because multiple nodes can process the same file at the same time. Instead of a single computer parsing an event log file, Hadoop enables several commodity servers to engage in high performance computing by parallel processing a single file.

HDFS is compatible with MapReduce, a programming paradigm developed by Google designed to manage big data. MapReduce efficiently operates over a distributed file system by utilizing multiple nodes to compute, operating on the simple principle of divide and conquer. As its name suggests, there are two primary phases in a MapReduce algorithm: the Map phase and the Reduce phase. In the Map phase, the algorithm generates key-value pairs from the input. In the Reduce phase, the key-value pairs are aggregated into an output. The Map phase typically does all the preprocessing of the data; including filtering and sorting while the Reduce phase performs all the counting or summation operations.

Figure 4 illustrates a MapReduce algorithm performing a word count job. It reads in the input text file and breaks it apart into key-value pairs, where each key is a word in the input file with a value of 1. Subsequently, the key-value pairs are assigned to different computer nodes by their key in the shuffle phase. Finally, the algorithm aggregates the key-value pairs that have the same key into one pair, summing up the values to get a word count for each key.

The same principles of HDFS and MapReduce can be leveraged to parallel process event logs. Each event log is uploaded to HDFS, where the event logs are divided into separate servers and replicated. Then a MapReduce job is run on the event logs files. In the Map phase, the servers parse the event logs and generate key-value pairs of every event ID and its corresponding time. The Reducer aggregates the pairs by matching event IDs and discards the times while replacing the value with a word count of the event IDs.

The advantage of leveraging Hadoop MapReduce to process event log data is twofold: (1) HDFS distributes the event logs to different servers, enabling parallel parsing of the binary data and; (2) MapReduce can efficiently perform a word count operation on the event IDs, providing insight into the frequency of certain events.

As shown in Figure 5, the event ID 4624 has a corresponding value of 6422, signifying that the computer from which the event log was obtained was logged into 6,422 times (reference Table 1). Similarly, 4688 has a value of 253, meaning that 253 programs or processes have been executed on this computer.

Conclusion

By utilizing binary forensics and distributed computing, a software application was created that decoded the complex binary structure of Windows event logs and extracts relevant event information corresponding to critical security messages and alerts. Its output of event IDs and the corresponding number of incidents gives the Navy insight into the frequency of benign and malignant events on military computers. Since the software is compatible with .evtx files, it can process event logs generated by computers operating on Windows 7 or later versions, assisting in the upgrade of naval computers to the latest operating systems. It will be made open-source, to help other individuals and corporations efficiently monitor their computers for cybersecurity threats.

As affirmed by Graph 1 and Graph 2, employing distributed computing and MapReduce has resulted in software that is scalable, able to process gigabytes to terabytes of event log data. A roughly 6,000 percent increase in rate of bytes processed corresponded to an only 3.12 percent increase in cluster CPU usage, exemplifying the scalability of this software.

This software will help pinpoint potential cybersecurity threats on military machines and safeguard against future attacks to the naval network. Future research and software development can be conducted to extract more information from the event logs, such as the application-specific event message, and to further optimize the algorithm.

Acknowledgements
The authors would like to thank the Office of Naval Research summer internship program at the Naval Postgraduate School which enabled them to collaborate on this effort. The authors also thank Capt. Benjamin Brida for insight into the Hadoop framework and the Java codebase/libraries for processing binary data. Jim Zhou set up the Cloudera Hadoop cluster on Redhat Linux running on Dell 1U rack-mounted servers connected via an infiniband switch.

Arijit Das has been part of the research faculty at the Naval Postgraduate school since 2002, teaching computer science courses such as Java, databases, C, Android programming and HTML5, and working on DoD projects in the areas of databases, big data, mobile development, e-Learning and virtualization. He is a frequent speaker at developer tech events for Oracle technologies applicable to DoD information technology.

Abhinav Arya is a high school senior at Bellarmine College Preparatory. He has developed software for tech companies and nonprofits and is proficient in advanced Java programming. From January to June 2017, Abhinav worked as a software development intern for Qolsys to develop a software tool that analyzes radio signals emitted from the company's flagship product, the IQ Panel 2. During the summer of 2017, Abhinav researched at the Naval Postgraduate School under the guidance of Prof. Arijit Das, where he created a Java MapReduce algorithm that parallel processes binary event logs across a cluster of Hadoop DataNodes and extracts critical security information.

Jim Zhou is a technology support staff member at the Naval Postgraduate School working on his Master of Science in Computer Science in the area of big data, Hadoop and Spark. He is well experienced in the areas of network security, virtualization and rack setups for military systems.

References
Apache Software Foundation. “MapReduce Tutorial.” Apache Hadoop 2.9.0 – MapReduce Tutorial, Apache, https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html.
Hortonworks Inc. “Apache Hadoop HDFS.” Apache Hadoop HDFS, https://hortonworks.com/apache/hdfs/.
Hortonworks. “MapReduce.” Apache Hadoop MapReduce, https://hortonworks.com/apache/mapreduce/.
InfoQ. “Understanding HDFS Using Legos.” YouTube, YouTube, 18 Feb. 2015, www.youtube.com/watch?v=4Gfl0WuONMY.
Microsoft Corporation. “Description of Security Events in Windows 7 and in Windows Server 2008 R2.” Support.microsoft.com, 16 Feb. 2011, https://support.microsoft.com/en-us/help/977519/description-of-security-even ts-in-windows-7-and-in-windows-server-2008.
Schuster, Andreas. “Introducing the Microsoft Vista Event Log File Format.” Digital Investigation, vol. 4, 2007, pp. 65–72., doi:10.1016/j.diin.2007.06.015.

Figure 1. Hexadecimal representation of the binary data in .evtx event logs.
Figure 1. Hexadecimal representation of the binary data in .evtx event logs.

Table 1. Certain Event IDs correspond to critical security threats/alerts.
Table 1. Certain Event IDs correspond to critical security threats/alerts.

Figure 2. Output of the sequential EVTX parser.
Figure 2. Output of the sequential EVTX parser.

Figure 3. Graphical representation of the Hadoop Distributed File System (HDFS).
Figure 3. Graphical representation of the Hadoop Distributed File System (HDFS).

Figure 4. Graphical representation of a MapReduce algorithm.
Figure 4. Graphical representation of a MapReduce algorithm.

Figure 5. MapReduce output: Event IDs and their corresponding frequency.
Figure 5. MapReduce output: Event IDs and their corresponding frequency.

Graph 1. Rate of bytes processed of two MapReduce EVTX parse jobs.
Graph 1. Rate of bytes processed of two MapReduce EVTX parse jobs.

Graph 2. Cluster CPU usage of two MapReduce EVTX parse jobs.
Graph 2. Cluster CPU usage of two MapReduce EVTX parse jobs.
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