Through our work in cyber security, we have amassed millions of pieces of malicious software in a large malware database called the CERT Artifact Catalog. Analyzing this code manually for potential similarities and to identify malware provenance is a painstaking process. This blog post follows up our earlier post to explore how to create effective and efficient tools that analysis can use to identify malware.
After 47 weeks and 50 blog postings, the sands of time are quickly running out in 2011. Last week's blog posting summarized key 2011 SEI R&D accomplishments in our four major areas of software engineering and cyber security: innovating software for competitive advantage, securing the cyber infrastructure, accelerating assured software delivery and sustainment for the mission, and advancing disciplined methods for engineering software.This week's blog posting presents a preview of some upcoming blog postings you'll read about in these areas during 2012.
A key mission of the SEI is to advance the practice of software engineering and cyber security through research and technology transition to ensure the development and operation of software-reliant Department of Defense (DoD) systems with predictable and improved quality, schedule, and cost. To achieve this mission, the SEI conducts research and development (R&D) activities involving the DoD, federal agencies, industry, and academia. One of my initial blog postings summarized the new and upcoming R&D activitieswe had planned for 2011. Now that the year is nearly over, this blog posting presents some of the many R&D accomplishments we completed in 2011.
As with any new initiative or tool requiring significant investment, the business value of statistically-based predictive models must be demonstrated before they will see widespread adoption. The SEI Software Engineering Measurement and Analysis (SEMA)initiative has been leading research to better understand how existing analytical and statistical methods can be used successfully and how to determine the value of these methods once they have been applied to the engineering of large-scale software-reliant systems.
The DoD relies heavily on mission- and safety-critical real-time embedded software systems (RTESs), which play a crucial role in controlling systems ranging from airplanes and cars to infusion pumps and microwaves. Since RTESs are often safety-critical, they must undergo an extensive (and often expensive) certification process before deployment. This costly certification process must be repeated after any significant change to the RTES, such as migrating a single-core RTES to a multi-core platform, significant code refactoring, or performance optimizations, to name a few.
As part of an ongoing effort to keep you informed about our latest work, I'd like to let you know about some recently published SEI technical reports and notes. These reports highlight the latest work of SEI technologists in Agile methods, insider threat,the SMART Grid Maturity Model, acquisition, and CMMI. This post includes a listing of each report, author/s, and links where the published reports can be accessed on the SEI website.
Whether soldiers are on the battlefield or providing humanitarian relief effort, they need to capture and process a wide range of text, image, and map-based information. To support soldiers in this effort, the Department of Defense (DoD) is beginning to equip soldiers with smartphones to allow them to manage that vast array and amount of information they encounter while in the field. Whether the information gets correctly conveyed up the chain of command depends, in part, on the soldier's ability to capture accurate data while in the field. This blog posting, a follow-up to our initial post, describes our work on creating a software application for smartphones that allows soldier end-users to program their smartphones to provide an interface tailored to the information they need for a specific mission.
Cloudlets, which are lightweight servers running one or more virtual machines (VMs), allow soldiers in the field to offload resource-consumptive and battery-draining computations from their handheld devices to nearby cloudlets. This architecture decreases latency by using a single-hop network and potentially lowers battery consumption by using WiFi instead of broadband wireless. This posting extends our original postby describing how we are using cloudlets to help soldiers perform various mission capabilities more effectively, including facial, speech, and imaging recognition, as well as decision making and mission planning.
In the first post in this two-part series, we covered five unique challenges that impact insider threat programs and hub analysts. The challenges included lack of adequate training, competing interests, acquiring data, analyzing data, and handling false positives.
As you read the new challenges introduced in this post, ask yourself the same questions: 1) How many of these challenges are ones you are facing today? 2) Are there challenges in this list that lead to an "aha" moment? 3) Are there challenges you are facing that did not make the list? 4) Do you need assistance with combating any of these challenges? Let us know your answers and thoughts via email at firstname.lastname@example.org.