Organizational improvement efforts should be driven by business needs, not by the content of improvement models. While improvement models, such as the Capability Maturity Model Integration (CMMI) or the Baldrige Criteria for Performance Excellence, provide excellent guidance and best practice standards, the way in which those models are implemented must be guided by the same drivers that influence any other business decision. Business drivers are the collection of people, information, and conditions that initiate and support activities that help an organization accomplish its mission.
In previous blog posts, I have written about applying similarity measures to malicious code to identify related files and reduce analysis expense. Another way to observe similarity in malicious code is to leverage analyst insights by identifying files that possess some property in common with a particular file of interest. One way to do this is by using YARA, an open-source project that helps researchers identify and classify malware. YARA has gained enormous popularity in recent years as a way for malware researchers and network defenders to communicate their knowledge about malicious files, from identifiers for specific families to signatures capturing common tools, techniques, and procedures (TTPs). This blog post provides guidelines for using YARA effectively, focusing on selection of objective criteria derived from malware, the type of criteria most useful in identifying related malware (including strings, resources, and functions), and guidelines for creating YARA signatures using these criteria.
Last week, we presented the first posting in a series from a panel at SATURN 2012 titled "Reflections on 20 Years of Software Architecture." In her remarks on the panel summarizing the evolution of software architecture work at the SEI, Linda Northrop, director of the SEI's Research, Technology, and System Solutions (RTSS) Program, referred to the steady growth in system scale and complexity over the past two decades and the increased awareness of architecture as a primary means for achieving desired quality attributes, such as performance, reliability, evolvability, and security.
A search on the term "software architecture" on the web as it existed in 1992 yielded 88,700 results. In May, during a panel providing a 20-year retrospective on software architecture hosted at the SEI Architecture Technology User Network (SATURN) conference, moderator Rick Kazman noted that on the day of the panel discussion--May 9, 2012-- that same search yielded 2,380,000 results. This 30-fold increase stems from various factors, including the steady growth in system complexity, the increased awareness of the importance of software architecture on system quality attributes, and the quality and impact of efforts by the SEI and other groups conducting research and transition activities on software architecture. This blog posting--the first in a series--provides a lightly edited transcription of the presentation of the first panelist, Linda Northrop, director of the SEI's Research, Technology, & System Solutions (RTSS) Programat the SEI, who provided an overview of the evolution of software architecture work at the SEI during the past twenty years.
For more than 10 years, scientists, researchers, and engineers used the TeraGrid supercomputer network funded by the National Science Foundation (NSF) to conduct advanced computational science. The SEI has joined a partnership of 17 organizations and helped develop the successor to the TeraGrid called the Extreme Science and Engineering Discovery Environment (XSEDE). This posting, which is the first in a multi-part series, describes our work on XSEDE that allows researchers open access--directly from their desktops--to the suite of advanced computational tools and digital resources and services provided via XSEDE. This series is not so much concerned with supercomputers and supercomputing middleware, but rather with the nature of software engineering practice at the scale of the socio-technical ecosystem.
All software engineering and management practices are based on cultural and social assumptions. When adopting new practices, leaders often find mismatches between those assumptions and the realities within their organizations. The SEI has an analysis method called Readiness and Fit Analysis (RFA) that allows the profiling of a set of practices to understand their cultural assumptions and then to use the profile to support an organization in understanding its fit with the practices' cultural assumptions. RFA has been used for multiple technologies and sets of practices, most notably for adoption of CMMI practices.
Blockchain technology was conceived a little over ten years ago. In that short time, it went from being the foundation for a relatively unknown alternative currency to being the "next big thing" in computing, with industries from banking to insurance...