The Domain Name System (DNS) is an essential component of the Internet, a virtual phone book of names and numbers, but we rarely think about it until something goes wrong. As evidenced by the recent distributed denial of service (DDoS) attack against Internet performance management company Dyn, which temporarily wiped out access to websites including Amazon, Paypal, Reddit, and the New York Times for millions of users down the Eastern Seaboard and Europe, DNS serves as the foundation for the security and operation of internal and external network applications. DNS also serves as the backbone for other services critical to organizations including email, external web access, file sharing and voice over IP (VoIP). There are steps, however, that network administrators can take to ensure the security and resilience of their DNS infrastructure and avoid security pitfalls. In this blog post, I outline six best practices to design a secure, reliable infrastructure and present an example of a resilient organizational DNS.
As part of an ongoing effort to keep you informed about our latest work, this blog post summarizes some recently published books, SEI technical reports, podcasts and webinars on insider threat, using malware analysis to identify overlooked security requirements, software architecture, scaling Agile methods, best practices for preventing and responding to DDoS attacks, and a special report documenting the technical history of the SEI.
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.
Federal agencies and other organizations face an overwhelming security landscape. The arsenal available to these organizations for securing software includes static analysis tools, which search code for flaws, including those that could lead to software vulnerabilities. The sheer effort required by auditors and coders to triage the large number of potential code flaws typically identified by static analysis can hijack a software project's budget and schedule. Auditors need a tool to classify alerts and to prioritize some of them for manual analysis. As described in my first post in this series, I am leading a team on a research project in the SEI's CERT Division to use classification models to help analysts and coders prioritize which vulnerabilities to address. In this second post, I will detail our collaboration with three U.S. Department of Defense (DoD) organizations to field test our approach. Two of these organizations each conduct static analysis of approximately 100 million lines of code (MLOC) annually.
By Will Klieber
CERT Secure Coding Team
This blog post is co-authored by Will Snavely.
Finding violations of secure coding guidelines in source code is daunting, but fixing them is an even greater challenge. We are creating automated tools for source code transformation. Experience in examining software bugs reveals that many security-relevant bugs follow common patterns (which can be automatically detected) and that there are corresponding patterns for repair (which can be performed by automatic program transformation). For example, integer overflow in calculations related to array bounds or indices is almost always a bug. While static analysis tools can help, they typically produce an enormous number of warnings. Once an issue has been identified, teams are only able to eliminate a small percentage of the vulnerabilities identified. As a result, code bases often contain an unknown number of security bug vulnerabilities. This blog post describes our research in automated code repair, which can eliminate security vulnerabilities much faster than the existing manual process and at a much lower cost. While this research focuses to the C programming language, it applies to other languages as well.
Many system and software developers and testers, especially those who have primarily worked in business information systems, assume that systems--even buggy systems--behave in a deterministic manner. In other words, they assume that a system or software application will always behave in exactly the same way when given identical inputs under identical conditions. This assumption, however, is not always true. While this assumption is most often false when dealing with cyber-physical systems, new and even older technologies have brought various sources of non-determinism, and this has significant ramifications on testing. This blog post, the first in a series, explores the challenges of testing in a non-deterministic world.
This blog post is coauthored by Dionisio de Niz.
Software with timers and clocks (STACs) exchange clock values to set timers and perform computation. STACs are key elements of safety-critical systems that make up the infrastructure of our daily lives. They are particularly used to control systems that interact (and must be synchronized) with the physical world. Examples include avionics systems, medical devices, cars, cell phones, and other devices that rely on software not only to produce the right output, but also to produce it at the correct time. An airbag, for example, must deploy as intended, but just as importantly, it must deploy at the right time. Thus, when STACs fail to operate as intended in the safety-critical systems that rely on them, the result can be significant harm or loss of life. Within the Department of Defense (DoD), STACs are used widely, ranging from real-time thread schedulers to controllers for missiles, fighter planes, and aircraft carriers. This blog post presents exploratory research to formally verify safety properties of sequential and concurrent STACs at the source-code level.
The growth and change in the field of robotics in the last 15 years is tremendous, due in large part to improvements in sensors and computational power. These sensors give robots an awareness of their environment, including various conditions such as light, touch, navigation, location, distance, proximity, sound, temperature, and humidity. The increasing ability of robots to sense their environments makes them an invaluable resource in a growing number of situations, from underwater explorations to hospital and airport assistants to space walks. One challenge, however, is that uncertainty persists among users about what the robot senses; what it predicts about its state and the states of other objects and people in the environment; and what it believes its outcomes will be from the actions it takes. In this blog post, I describe research that aims to help robots explain their behaviors in plain English and offer greater insights into their decision making.