2021 Year in Review
AI and Open Source Software Contribution
The SEI has a long history of developing tools and platforms and releasing them as open source software to further research and practice. As of late 2021, the SEI had more than 100 open source software project repositories on its GitHub site and main website.
In fiscal year 2021, the SEI released several new tools and updated a number of existing open source projects.
Kaiju Malware Analysis Tool Suite
Building on the CERT Division’s Pharos advanced binary code analysis framework, Kaiju extends the U.S. National Security Agency’s Ghidra reverse engineering platform with several powerful new analysis tools. Kaiju brings a variety of improvements to Ghidra’s disassembler and decompiler, including powerful code comparison tools, an advanced capability for reasoning about program behavior, and improved support for decompiling C++ programs.
In partnership with the Cybersecurity and Infrastructure Security Agency (CISA), the SEI’s CERT Division developed the Foundry Appliance, which seamlessly integrates numerous SEI open source applications used to put on the annual President's Cup Cybersecurity Competition. Users can leverage this virtual appliance to build cyber laboratories, challenges, and competitions.
NetSA Tool Suite
The NetSA tool suite includes YAF (Yet Another Flow Sensor), the Mothra security analysis platform, and Super Mediator, among other tools. In 2021, the CERT Division updated the YAF and Super Mediator products.
The CERT Division’s Crucible is an open source, cyber-simulation application framework enabling everything from small-scale virtual-environment labs and cyber challenges to large-scale multi-team exercises sponsored by the U.S. Marine Corps, Army, Air Force, Cyber Command, Indo-Pacific Command, Special Operations Command, Department of the Treasury, and others.
The AI Division’s Juneberry tool improves the experience of machine learning experimentation by providing a framework for automating the training, evaluation, and comparison of multiple models against multiple datasets, reducing errors and improving reproducibility.