According to the National Institute of Standards and Technology (NIST), Information Security Continuous Monitoring (ISCM) is a process for continuously analyzing, reporting, and responding to risks to operational resilience (in an automated manner, whenever possible). Compared to the traditional method of collecting and assessing risks at longer intervals--for instance, monthly or annually--ISCM promises to provide near-real-time situational awareness of an organization's risk profile. ISCM creates challenges as well as benefits, however, because the velocity of information gathered using ISCM is drastically increased. Development, operation, and maintenance processes built for a more leisurely pace can thus be overwhelmed. This blog post explores how organizations can leverage Agile methods to keep pace with the increased velocity of ISCM risk information, while ensuring that changes to systems are conducted in a controlled manner.
In my preceding blog posts, I promised to provide more examples highlighting the importance of software sustainment in the U.S. Department of Defense (DoD). My focus is on sustaining legacyweapons systems that are no longer in production, but are expected to remain a key component of our defense capability for decades to come. Despite the fact that these legacy systems are no longer in the acquisition phase, software upgrade cycles are needed to refresh their capabilities every 18 to 24 months. In addition, significant modernization can often be made by more extensive, focused software upgrades with relatively modest hardware changes. This third blog post describes effective sustainment engineering efforts in the Army, using examples from across its software engineering centers. These examples are tied to SEI research on capability maturity models, agility, and the Architecture Analysis and Design Language (AADL) modeling notation.
Software and acquisition professionals often have questions about recommended practices related to modern software development methods, techniques, and tools, such as how to apply agile methods in government acquisition frameworks, systematic verification and validation of safety-critical systems, and operational risk management. In the Department of Defense (DoD), these techniques are just a few of the options available to face the myriad challenges in producing large, secure software-reliant systems on schedule and within budget.
Ultimately, SEI curated recommended practices on five software topics: Agile at Scale, Safety-Critical Systems, Monitoring Software-Intensive System Acquisition Programs, Managing Intellectual Property in the Acquisition of Software-Intensive Systems, and Managing Operational Resilience. In addition to a recently published paper on SEI efforts and individual posts on the SPRUCE site, these recommended practices will be published in a series of posts on the SEI blog. This post, the first in a three-part series by Robert Ferguson, first explores the challenges to Monitoring Software-Intensive System Acquisition (SISA) programs and presents the first two recommended best practices as detailed in the SPRUCE post. The second post in this series will present the next three best practices. The final post will present the final two recommendations as well as conditions that will allow organizations to derive the most benefit from these practices.
Due to advances in hardware and software technologies, Department of Defense (DoD) systems today are highly capable and complex. However, they also face increasing scale, computation, and security challenges. Compounding these challenges, DoD systems were historically designed using stove-piped architectures that lock the Government into a small number of system integrators, each devising proprietary point solutions that are expensive to develop and sustain over the lifecycle. Although these stove-piped solutions have been problematic (and unsustainable) for years, the budget cuts occurring under sequestration are motivating the DoD to reinvigorate its focus on identifying alternative means to drive down costs, create more affordable acquisition choices, and improve acquisition program performance. A promising approach to meet these goals is Open Systems Architecture (OSA), which combines
modular open development practices designed to reduce the cycle time needed to acquire new systems and insert new technology into legacy systems, and
This blog posting expands on earlier coverage of how acquisition professionals and system integrators can apply OSA practices to effectively decompose large monolithic business and technical architectures into manageable and modular solutions that can integrate innovation more rapidly and lower total ownership costs.
By David Svoboda Senior Member of the Technical Staff CERT Division
Whether Java is more secure than C is a simple question to ask, but a hard question to answer well. When we began writing the SEI CERT Oracle Coding Standard for Java, we thought that Java would require fewer secure coding rules than the SEI CERT C Coding Standard because Java was designed with security in mind. We naively assumed that a more secure language would need fewer rules than a less secure one. However, Java has 168 coding rules compared to just 116 for C. Why? Was our (admittedly simplistic) assumption completely spurious? Or, are there problems with our C or Java rules? Or, are Java programs, on average, just as susceptible to vulnerabilities as C programs? In this post, I attempt to analyze our CERT rules for both C and Java to determine if they indeed refute the conventional wisdom that Java is more secure than C.
There are more than 200 different types of testing, and many stakeholders in testing--including the testers themselves and test managers--are often largely unaware of them or do not know how to perform them. Similarly, test planning frequently overlooks important types of testing. The primary goal of this series of blog posts is to raise awareness of the large number of test types, to verify adequate completeness of test planning, and to better guide the testing process. In the previous blog entry in this series, I introduced a taxonomy of testing in which 15 subtypes of testing were organized around how they addressed the classic 5W+2H questions: what, when, why, who, where, how, and how well. This and future postings in this series will cover each of these seven categories of testing, thereby providing structure to roughly 200 types of testing currently used to test software-reliant systems and software applications.
This blog entry covers the four, top-level subtypes of testing that answer the following questions:
What-based testing: What is being tested?
Object-Under-Test-based (OUT) testing
When-based testing: When is the testing being performed?
Built-in-Test (BIT) testing
After exploring the what-based and when-based categories of testing, this post presents a section on using the taxonomy, as well as opportunities for accessing it.
CertStream is a free service for getting information from the Certificate Transparency Log Network. I decided to investigate the presence of domains generated by Domain Generation Algorithms (DGA) in this stream and I found some intersting phenomena.