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Ensuring Machine Learning Models Meet System and Mission Requirements

Ensuring Machine Learning Models Meet System and Mission Requirements

Created April 18, 2025

The SEI’s Machine Learning Test and Evaluation is a one-of-a-kind framework to evaluate machine learning models from inception to deployment.

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Automating Mismatch Detection in ML Systems

Automating Mismatch Detection in ML Systems

Created Oct. 1, 2022 Updated April 11, 2025

Timely deployment of Machine Learning (ML) systems is vital for critical national security missions. The SEI’s TEC tool reduces problems that cause delay in the fielding of ML systems.

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AISIRT Advances National Security with Secure AI

AISIRT Advances National Security with Secure AI

Created Nov. 10, 2023 Updated March 26, 2025

The SEI created an AISIRT to ensure that the Department of Defense and other federal agencies develop, adopt, and use AI effectively and securely to safeguard the security of the nation.

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SEI Solutions That Improve the DoD's Cyber Mission Readiness

SEI Solutions That Improve the DoD's Cyber Mission Readiness

Created March 21, 2025

The SEI develops tools that virtualize systems to deliver high-quality training and user performance validation to ensure cyber teams are ready to face ever-evolving threats and challenges.

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Improving Disaster Response with the xView2 Challenge

Improving Disaster Response with the xView2 Challenge

Created July 22, 2020 Updated March 17, 2025

The DoD’s Defense Innovation Unit, the SEI, and other organizations launched the xView2 Challenge to create accurate, efficient machine learning models that can advance disaster response. The competition resulted in xView2, a machine learning system that analyzes satellite imagery to classify damage to structures.

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Automating Container Minimization for the Edge

Automating Container Minimization for the Edge

Created Nov. 27, 2024 Updated March 12, 2025

The SEI's Container Minimization Tool prunes and deduplicates files to reduce storage waste and software vulnerabilities in the resource-limited environment of the tactical edge.

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Delivering “Virtual,” Real-World Experiences to Build Elite Cyber Teams

Delivering “Virtual,” Real-World Experiences to Build Elite Cyber Teams

Created May 28, 2019 Updated Feb. 28, 2025

The SEI CERT Division develops simulations that offer cyber operators a way to get the experience they need to perform at elite levels.

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Internet of Things (IoT) Security Platform Can Improve Warfighter Resilience, Deliver Cost Savings

Internet of Things (IoT) Security Platform Can Improve Warfighter Resilience, Deliver Cost Savings

Created Oct. 1, 2019 Updated Feb. 27, 2025

Internet of Things (IoT) devices can provide useful capabilities, but many have known security vulnerabilities that have been exploited by malicious actors. The SEI KalKi security platform leverages software-defined networking (SDN) and network function virtualization (NFV) to enable secure integration of IoT devices into Department of Defense DOD) networks, even devices that are not fully trusted or configurable. Such integration can improve warfighter resilience and deliver cost savings.

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AI Trust Lab: Trustworthy AI for a Safer Nation

AI Trust Lab: Trustworthy AI for a Safer Nation

Created Oct. 31, 2019 Updated Feb. 27, 2025

To support DoD mission success, the SEI’s Trust Lab advances the streamlined development of trustworthy and human-centered AI engineering practices by focusing on mission goals and prioritizing warfighter needs.

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Connecting Securely to IoT Devices on the Battlefield

Connecting Securely to IoT Devices on the Battlefield

Created April 5, 2022 Updated Feb. 24, 2025

The SEI developed new layers of security and functionality so that warfighters can securely access IoT devices on the battlefield.

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Untangling the Knot: Enabling Rapid Software Evolution

Untangling the Knot: Enabling Rapid Software Evolution

Created Feb. 23, 2022 Updated Oct. 3, 2024

Our automated refactoring solution recommends ways to refactor existing software, significantly increasing the efficiency of software evolution.

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Automated Repair of Static Analysis Alerts (Redemption of False Positives)

Automated Repair of Static Analysis Alerts (Redemption of False Positives)

Created Sept. 19, 2024

The SEI Redemption tool extensibly repairs code associated with static analysis alerts. Currently, it repairs uninitialized memory, null pointer, and other C/C++ weaknesses.

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The Advanced Computing Lab

The Advanced Computing Lab

Created Sept. 3, 2024

The Advanced Computing Lab has extensive expertise in software performance optimization on diverse hardware architectures, and hardware and system design for software-based systems.

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Artificial Intelligence Engineering Body of Knowledge

Artificial Intelligence Engineering Body of Knowledge

Created Oct. 4, 2022

AI Engineering focuses on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. The body of knowledge will be a standardization of this emergent discipline and will guide practitioners in implementing AI systems.

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A Tool Set to Support Big Data Systems Acquisition

A Tool Set to Support Big Data Systems Acquisition

Created Sept. 28, 2017 Updated Aug. 2, 2022

We offer an approach that reduces risk and simplifies the selection and acquisition of big data technologies when you acquire and develop big data systems.

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DevSecOps Platform Independent Model (PIM)

DevSecOps Platform Independent Model (PIM)

Created May 26, 2022

The DevSecOps Platform Independent Model (PIM) enables organizations to implement DevSecOps in a secure, safe, and sustainable way in order to fully reap the benefits available from DevSecOps principles, practices, and tools.

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Predicting Changing Conditions in Production Machine Learning Systems

Predicting Changing Conditions in Production Machine Learning Systems

Created May 20, 2022

The inference quality of deployed machine learning (ML) models degrades over time due to differences between training and production data, typically referred to as drift. The SEI developed a process and toolset for drift behavior analysis to better understand how models will react to drift before they are deployed and detect drift at runtime due to changing conditions.

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AI Workforce Development

AI Workforce Development

Created May 3, 2022

The SEI is advancing the professional discipline of AI engineering through the latest academic advancements at Carnegie Mellon University.

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Helping the Army Deliver Secure, Cloud-Based Capabilities to the Warfighter

Helping the Army Deliver Secure, Cloud-Based Capabilities to the Warfighter

Created April 13, 2022

The SEI helped establish a roadmap for each phase of the product lifecycle so that the AEC can update its OT&E activities and support the Army’s move to the cloud.

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Bringing Computation and Actionable Data to Battlefield Edge Environments

Bringing Computation and Actionable Data to Battlefield Edge Environments

Created March 24, 2022

Making cloud computing resources available to military personnel in the field presents accessibility and security challenges. Tactical cloudlets provide secure, reliable, and timely access to cloud resources to help military personnel carry out their mission at the tactical edge despite unreliable connectivity to the cloud.

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Juneberry

Juneberry

Created March 21, 2022

Juneberry automates the training, evaluation, and comparison of multiple ML models against multiple datasets. This makes the process of verifying and validating ML models more consistent and rigorous, which reduces errors, improves reproducibility, and facilitates integration.

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Architecture Analysis and Design Language (AADL)

Architecture Analysis and Design Language (AADL)

Created July 18, 2019 Updated Feb. 2, 2022

Software for mission- and safety-critical systems, such as avionics systems in aircraft, is growing larger and more expensive. The Architecture Analysis and Design Language (AADL) addresses common problems in the development of these systems, such as mismatched assumptions about the physical system, computer hardware, software, and their interactions that can result in system problems detected too late in the development lifecycle.

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Managing Technical Debt with Data-Driven Analysis

Managing Technical Debt with Data-Driven Analysis

Created Sept. 28, 2017 Updated Feb. 2, 2022

Most software projects carry technical debt. We develop tools and techniques that identify it and provide a complete view of the debt that you need to manage.

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Architecting the Future of Software Engineering: A National Agenda for Software Engineering Research & Development

Architecting the Future of Software Engineering: A National Agenda for Software Engineering Research & Development

Created Nov. 3, 2021

This study identifies the technologies and areas of research that are most critical for enabling future software systems. The technology roadmap that resulted from this work is intended to guide the research efforts of the software engineering community toward future systems that are safe, predictable, and evolvable.

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Characterizing and Detecting Mismatch in ML-Enabled Systems

Characterizing and Detecting Mismatch in ML-Enabled Systems

Created July 7, 2021

The development of machine learning-enabled systems typically involves three separate workflows with three different perspectives—data scientists, software engineers, and operations. The mismatches that arise can result in failed systems. We developed a set of machine-readable descriptors for elements of ML-enabled systems to make stakeholder assumptions explicit and prevent mismatch.

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Applying Causal Learning to Improve Software Cost Estimation and Project Control

Applying Causal Learning to Improve Software Cost Estimation and Project Control

Created March 16, 2021

SEI researchers have applied causal learning to help the Department of Defense identify factors that increase software costs and to provide guidance to control them.

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AI Engineering: A National Initiative

AI Engineering: A National Initiative

Created Feb. 25, 2021

The SEI is taking the initiative to develop an AI engineering discipline that will lay the groundwork for establishing the practices, processes, and knowledge to build new generations of AI solutions.

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Knowing When You Don’t Know: Engineering AI Systems in an Uncertain World

Knowing When You Don’t Know: Engineering AI Systems in an Uncertain World

Created Feb. 17, 2021

This project is benchmarking methods for quantifying uncertainty in machine learning (ML) models. It is also developing techniques to identify the causes of uncertainty, rectify them, and efficiently update ML models to reduce uncertainty in their predictions.

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Train, But Verify

Train, But Verify

Created Oct. 27, 2020

Attacks on machine learning (ML) systems can make them learn the wrong thing, do the wrong thing, or reveal sensitive information. Train, But Verify protects ML systems by training them to act against two of these threats at the same time and verifying them against realistic threat models.

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Learning Patterns by Observing Behavior with Inverse Reinforcement Learning

Learning Patterns by Observing Behavior with Inverse Reinforcement Learning

Created Feb. 11, 2020

The Software Engineering Institute (SEI) uses Inverse Reinforcement Learning (IRL) techniques—an area of machine learning—to more efficiently and effectively teach novices how to perform expert tasks, achieve robotic control, and perform activity-based intelligence.

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Community Guidance to Prevent Common Coding Errors

Community Guidance to Prevent Common Coding Errors

Created Dec. 1, 2017 Updated Dec. 3, 2019

The SEI leads a community initiative to establish secure coding practices that prevent coding errors and that are reliable, usable, and effective.

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Cyber Intelligence Study

Cyber Intelligence Study

Created May 14, 2019

The practice of cyber intelligence helps organizations protect their assets, know their risks, and recognize opportunities. In 2018, the SEI conducted a cyber intelligence study on behalf of the United States Office of the Director of National Intelligence (ODNI). Our task was to understand how organizations perform the work of cyber intelligence throughout the United States.

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Training Army Analysts to Use the Big Data Platform

Training Army Analysts to Use the Big Data Platform

Created Jan. 4, 2019

ARCYBER is teaming with the SEI CERT Division to create training capabilities that help Army analysts develop the necessary skills for using its Big Data Platform.

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Smart Grid Maturity Model (SGMM)

Smart Grid Maturity Model (SGMM)

Created Sept. 11, 2018

The smart grid is a constantly evolving infrastructure of digital technology and power industry practices for improving the management of electricity generation, transmission, and distribution. The Smart Grid Maturity Model (SGMM) helps utilities plan their smart grid journeys.

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Runtime Assurance for Big Data Systems

Runtime Assurance for Big Data Systems

Created June 22, 2018

To help assure runtime performance in big data systems, we designed a reference architecture to automatically generate and insert monitors and aggregate metric streams.

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SEI Hosts Crisis Simulation Exercise for Cyber Intelligence Research Consortium

SEI Hosts Crisis Simulation Exercise for Cyber Intelligence Research Consortium

Created Jan. 30, 2018

In SEI crisis simulation exercises, participants use scenarios that present fictitious malicious actors and environmental factors based on real-world events.

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Cyber Lightning Case Study

Cyber Lightning Case Study

Created Jan. 29, 2018

The SEI hosted Cyber Lightning, a three-day joint training exercise involving Air National Guard and Air Force Reserve units from western Pennsylvania and eastern Ohio.

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USPS Case Study

USPS Case Study

Created Jan. 24, 2018

The SEI teamed with the U.S. Postal Service to help it improve its cybersecurity and resilience and collaborated on a program to develop a strong cybersecurity workforce.

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Acquiring Systems, Not Just Software

Acquiring Systems, Not Just Software

Created Dec. 19, 2017

The U.S. Department of Defense (DoD) and federal agencies are increasingly acquiring software-intensive systems instead of building them with internal resources. However, acquisition programs frequently have difficulty identifying the critical software acquisition activities, deliverables, risks, and opportunities.

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Digital Forensics: Advancing Solutions for Today's Escalating Cybercrime

Digital Forensics: Advancing Solutions for Today's Escalating Cybercrime

Created Dec. 15, 2017

As cybercrime proliferates, CERT researchers help law enforcement investigators process digital evidence with courses, methodologies and tools, skills, and experience.

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Helping Government Realize the Agile Advantage

Helping Government Realize the Agile Advantage

Created Dec. 15, 2017

We develop a wealth of resources to help the U.S. Department of Defense (DoD) and federal agencies make informed decisions about using Agile and lean approaches in achieving their goals.

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Security-Aware Acquisition

Security-Aware Acquisition

Created Dec. 15, 2017

The techniques developed by CERT researchers help you evaluate and manage cyber risk in today’s complex software supply chains.

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Empirical Research Office

Empirical Research Office

Created Dec. 15, 2017

We improve the capability delivered for every dollar of U.S. Department of Defense (DoD) investment made in software systems by improving the use of data in decision making.

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System and Platform Evaluation

System and Platform Evaluation

Created Dec. 15, 2017

CERT researchers develop and perform advanced penetration testing and cyber vulnerability assessments of organizations' systems and platforms.

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Using Automation to Prioritize Alerts from Static Analysis Tools

Using Automation to Prioritize Alerts from Static Analysis Tools

Created Sept. 28, 2017

The new CERT method for validating and repairing defects found by static analysis tools helps auditors and coders address more alerts with less effort.

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Supporting the U.S. Army's Joint Multi-Role Technology Demonstrator Effort

Supporting the U.S. Army's Joint Multi-Role Technology Demonstrator Effort

Created Sept. 28, 2017

We build and analyze virtual software systems to find problems early in development, before a system is built. Early discovery reduces cost and certification time.

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Converting a Navy Weapon System from a 32- to a 64-Bit Architecture

Converting a Navy Weapon System from a 32- to a 64-Bit Architecture

Created Sept. 28, 2017

The SEI provided an independent assessment of the risks of migrating a weapons control system deployed by the U.S. Navy from one architecture to another.

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GraphBLAS: A Programming Specification for Graph Analysis

GraphBLAS: A Programming Specification for Graph Analysis

Created Sept. 28, 2017

The GraphBLAS Forum is a world-wide consortium of researchers working to develop a programming specification for graph analysis that will simplify development.

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Positive Incentives for Reducing Insider Threat

Positive Incentives for Reducing Insider Threat

Created Sept. 28, 2017

Insiders present unique challenges to cybersecurity. We research insider threats and develop tools to analyze threat indicators in sociotechnical networks.

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Improving Verification with Parallel Software Model Checking

Improving Verification with Parallel Software Model Checking

Created Sept. 28, 2017

Current methods for software model checking can take too much time. We develop algorithms for SMC that execute many operations in parallel to improve scalability.

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Design Pattern Recovery from Malware Binaries

Design Pattern Recovery from Malware Binaries

Created Sept. 28, 2017

The U.S. Department of Defense (DoD) and industry face many malware problems. CERT researchers automate malware analysis capabilities, including those focused on malware family evolution and similarity.

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Automating Vulnerability Discovery in Critical Applications

Automating Vulnerability Discovery in Critical Applications

Created Sept. 28, 2017

CERT researchers develop automated tools that discover and mitigate software vulnerabilities and transfer them to researchers, procurement specialists, and software vendors.

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QUELCE: Quantifying Uncertainty in Early Lifecycle Cost Estimation

QUELCE: Quantifying Uncertainty in Early Lifecycle Cost Estimation

Created Sept. 19, 2017

Costs for large new systems are hard to estimate. We developed a method to quantify uncertainty and increase confidence in a program's cost estimate.

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Automated Code Repair

Automated Code Repair

Created Sept. 19, 2017

Finding security flaws in source code is daunting; fixing them is an even greater challenge. Our researchers are creating automated tools that can repair bugs automatically or by prompting developers for more information to make effective repairs.

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Verifying Distributed, Adaptive Real-Time (DART) Systems

Verifying Distributed, Adaptive Real-Time (DART) Systems

Created Sept. 15, 2017

Distributed, adaptive real-time (DART) systems must satisfy safety-critical requirements. We developed a method to verify DART systems and generate assured code.

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Explainable AI: Why Did the Robot Do That?

Explainable AI: Why Did the Robot Do That?

Created Sept. 15, 2017

To help human users trust their robot team members in critical situations, we develop tools that allow autonomous systems to explain their behavior.

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Multi-Agent Decentralized Planning for Adversarial Robotic Teams

Multi-Agent Decentralized Planning for Adversarial Robotic Teams

Created Sept. 15, 2017

We created multi-agent planning techniques, middleware, and algorithms that enable single users to manage fleets of UASs in real-world environments with changing adversaries.

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