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 virtual integration, blockchain programming, Agile DevOps, software innovations, cybersecurity engineering and software assurance, threat modeling, and blacklist ecosystem analysis. 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.
Well-known asymmetries pit cyber criminals with access to cheap, easy-to-use tools against government and industry organizations that must spend more and more to keep information and assets safe. To help reverse this imbalance, the SEI is conducting a study sponsored by the U.S. Office of the Director of National Intelligence to understand cyber intelligence best practices, common challenges, and future technologies that we will publish at the conclusion of the project. Through interviews with U.S.-based organizations from a variety of sectors, we are identifying tools, practices, and resources that help those organizations make informed decisions that protect their information and assets. This blog post describes preliminary findings from the interviews we have conducted so far. Our final report, which will include an anonymized look at the cyber intelligence practices of all the organizations we interviewed, will be released after the conclusion of the study in 2019.
For many DoD missions, our ability to collect information has outpaced our ability to analyze that information. Graph algorithms and large-scale machine learning algorithms are a key to analyzing the information agencies collect. They are also an increasingly important component of intelligence analysis, autonomous systems, cyber intelligence and security, logistics optimization, and more. In this blog post, we describe research to develop automated code generation for future-compatible graph libraries: building blocks of high-performance code that can be automatically generated for any future platform.
The size of aerospace software, as measured in source lines of code (SLOC), has grown rapidly. Airbus and Boeing data show that SLOC have doubled every four years. The current generation of aircraft software exceeds 25 million SLOC (MSLOC). These systems must satisfy safety-critical, embedded, real-time, and security requirements. Consequently, they cost significantly more than general-purpose systems. Their design is more complex, due to quality attribute requirements, high connectivity among subsystems, and sensor dependencies--each of which affects all system development phases but especially design, integration, and verification and validation.
Numerous tools exists to help detect flaws in code. Some of these are called flaw-finding static analysis (FFSA) tools because they identify flaws by analyzing code without running it. Typical output of an FFSA tool includes a list of alerts for specific lines of code with suspected flaws. This blog post presents our initial work on applying static analysis test suites in a novel way by automatically generating a large amount of labeled data for a wide variety of code flaws to jump-start static analysis alert classifiers (SAACs). SAACs are designed to automatically estimate the likelihood that any given alert indicates a genuine flaw.
This blog post is also authored by Forrest Shull.
Modern software systems are constantly exposed to attacks from adversaries that, if successful, could prevent a system from functioning as intended or could result in exposure of confidential information. Accounts of credit card theft and other types of security breaches concerning a broad range of cyber-physical systems, transportation systems, self-driving cars, and so on, appear almost daily in the news. Building any public-facing system clearly demands a systematic approach for analyzing security needs and documenting mitigating requirements. In this blog post, which was excerpted from a recently published technical report, we present the Hybrid Threat Modeling Method that our team of researchers developed after examining popular methods.
Cost estimation was cited by the Government Accountability Office (GAO) as one of the top two reasons why DoD programs continue to have cost overruns. How can we better estimate and manage the cost of systems that are increasingly software intensive? To contain costs, it is essential to understand the factors that drive costs and which ones can be controlled. Although we understand the relationships between certain factors, we do not yet separate the causal influences from non-causal statistical correlations. In this blog post, we explore how the use of an approach known as causal learning can help the DoD identify factors that actually cause software costs to soar and therefore provide more reliable guidance as to how to intervene to better control costs.
When considering best practices in egress filtering, it is important to remember that egress filtering is not focused on protecting your network, but rather on protecting other organizations' networks. For example, the May 2017 Wannacry Ransomware attack is believed to have exploited an exposed vulnerability in the server message block (SMB) protocol and was rapidly spread via communications over port 445. Egress and ingress filtering of port 445 would have helped limit the spread of Wannacry. In this post--a companion piece to Best Practices for Network Border Protection, which highlighted best practices for filtering inbound traffic--I explore best practices and considerations for egress filtering.