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The Latest Research in Software Engineering and Cybersecurity

As part of an ongoing effort to keep you informed about our latest work, this blog post summarizes some recently published SEI reports, podcasts, and presentations highlighting our work in cyber risk and resilience management, Agile/DevOps and risk management, best practices in insider threat, and dynamic design analysis. This post also includes a link to our recently published 2017 SEI Year in Review. These publications highlight the latest work of SEI technologists in these areas. This post includes a listing of each publication, author(s), and links where they can be accessed on the SEI website.

As U.S. Department of Defense (DoD) mission-critical and safety-critical systems become increasingly connected, exposure from security infractions is likewise increasing. In the past, system developers had worked on the assumption that, because their systems were not connected and did not interact with other systems, they did not have to worry about security. "Closed" system assumptions, however, are no longer valid, and security threats affect the safe operation of systems.

To address exponential growth in the cost of system development due to the increased complexity of interactions and mismatched assumptions in embedded software systems, the safety-critical system community has embraced virtual system integration and analysis of embedded systems. In this blog post, I describe our efforts to demonstrate how virtual system integration can be extended to address security concerns at the architecture level and complement code-level security analysis.

In a previous blog post, we addressed how machine learning is becoming ever more useful in cybersecurity and introduced some basic terms, techniques, and workflows that are essential for those who work in machine learning. Although traditional machine learning methods are already successful for many problems, their success often depends on choosing and extracting the right features from a dataset, which can be hard for complex data. For instance, what kinds of features might be useful, or possible to extract, in all the photographs on Google Images, all the tweets on Twitter, all the sounds of a spoken language, or all the positions in the board game Go? This post introduces deep learning, a popular and quickly-growing subfield of machine learning that has had great success on problems about these datasets, and on many other problems where picking the right features for the job is hard or impossible.

DevOps is a set of development practices that emphasizes collaboration, communication, and automation throughout the application lifecycle. In DevOps, all stakeholders--including IT operations staff, testers, developers, customers, and security personnel--are embedded from the inception of the project to its end. This blog post describes SEI research and customer engagements aimed at applying DevOps practices that are typically used at the end of the lifecycle to automate governance at the beginning of the development timeline.

In the SEI's examination of the software sustainment phase of the Department of Defense (DoD) acquisition lifecycle, we have noted that the best descriptor for sustainment efforts for software is "continuous engineering." Typically, during this phase, the hardware elements are repaired or have some structural modifications to carry new weapons or sensors. Software, on the other hand, continues to evolve in response to new security threats, new safety approaches, or new functionality provided within the system of systems. In this blog post, I will examine the intersection of three themes--product line practices, software sustainment, and public-private partnerships--that emerged during our work with one government program. I will also highlight some issues we have uncovered that deserve further discussion and research.

As the use of unmanned aircraft systems (UASs) increases, the volume of potentially useful video data that UASs capture on their missions is straining the resources of the U.S. military that are needed to process and use this data. This publicly released video is an example of footage captured by a UAS in Iraq. The video shows ISIS fighters herding civilians into a building. U.S. forces did not fire on the building because of the presence of civilians. Note that this video footage was likely processed by U.S. Central Command (CENTCOM) prior to release to the public to highlight important activities within the video, such as ISIS fighters carrying weapons, civilians being herded into the building to serve as human shields, and muzzle flashes emanating from the building.

Micro-expressions--involuntary, fleeting facial movements that reveal true emotions--hold valuable information for scenarios ranging from security interviews and interrogations to media analysis. They occur on various regions of the face, last only a fraction of a second, and are universal across cultures. In contrast to macro-expressions like big smiles and frowns, micro-expressions are extremely subtle and nearly impossible to suppress or fake. Because micro-expressions can reveal emotions people may be trying to hide, recognizing micro-expressions can aid DoD forensics and intelligence mission capabilities by providing clues to predict and intercept dangerous situations. This blog post, the latest highlighting research from the SEI Emerging Technology Center in machine emotional intelligence, describes our work on developing a prototype software tool to recognize micro-expressions in near real-time.