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Knowing what makes your software systems vulnerable to attacks is critical, as software vulnerabilities hurt security, reliability, and availability of the system as a whole. The Common Weakness Enumeration (CWE), a community effort that provides the foundation for such knowledge, is not sufficient, accurate and precise enough to serve as the common language measuring stick and provide a common baseline for developers and security practitioners. In this article, we introduce the relevant body of knowledge that consolidates CWE, including the Semantic Template and Software Fault Pattern efforts, and how static analysis tools add value through CWEs. We also provide future directions, present our vision on CWE formalization, and discuss the value of CWE for not only software assurance community, but also for Computer Science. [ABSTRACT FROM AUTHOR]

We propose a neural network approach for the problem of quantizer design for a distributed estimation system with communication constraints in the case where the global observation model is unknown and one must rely on a training set. Our method applies a variation of the Cyclic Generalized Lloyd Algorithm (CGLA) on every point of the training set and then uses a neural network for each quantizer to represent the training points and their associated codewords. The codeword of every training point is initialized using a previously proposed regression tree approach. Simulation results show that there is an improvement of the proposed approach over using regression trees only. [Copyright &y& Elsevier]