The United States Army Research Laboratory (ARL) has released the source code for Dshell, a framework that can be used to analyze cyberattacks.
ARL, which is part of the U.S. Army Research, Development and Engineering Command, has been using the tool for close to five years when reviewing incidents on the Department of Defense’s networks.
Dshell is an extensible forensic analysis framework that enables users to quickly develop plugins for analyzing network packet captures. The Army hopes that by publishing the source code on GitHub, members of the digital forensic and incident response community will contribute to the project by creating new custom modules.
According to William Glodek, network security branch chief at ARL, the Army has been trying to find ways to interact and engage with the community through a collaborative platform. Dshell is the first tool released by the research laboratory because its core is similar to the one of existing solutions, but it provides a simpler way to add new functionality.
The list of Dshell’s features includes IPv4 and IPv6 support, chainable decoders, robust stream reassembly, and custom output handlers. The tool has been developed in Python and it requires the installation of several Python modules.
“I want to give back to the cyber community, while increasing collaboration between Army, the Department of Defense and external partners to improve our ability to detect and understand cyber attacks,” Glodek stated.
“The success of Dshell so far has been dependant on a limited group of motivated individuals within government. By next year it should be representative of a much larger group with much more diverse backgrounds to analyze cyber attacks that are common to us all,” he added.
It’s not uncommon for organizations to release some of the tools they have been using internally as open source. Last year, Cisco released the OpenSOC big data security analytics framework. In January, Twitter announced the availability of AnomalyDetection, an R package used to detect anomalies such as the ones caused by spam and bots.