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In information security and network management, attacks based on vulnerabilities have grown in importance. Malicious attackers break into hosts using a variety of techniques. The most common method is to exploit known vulnerabilities. Although patches have long been available for vulnerabilities, system administrators have generally been reluctant to patch their hosts immediately because they perceive the patches to be annoying and complex. To solve these problems, we propose a security vulnerability evaluation and patch framework called PKG-VUL, which evaluates the software installed on hosts to decide whether the hosts are vulnerable and then applies patches to vulnerable hosts. All these operations are accomplished by the widely used simple network management protocol (SNMP). Therefore, system administrators can easily manage their vulnerable hosts through PKG-VUL included in the SNMP-based network management systems as a module. The evaluation results demonstrate the applicability of PKG-VUL and its performance in terms of devised criteria.

Differential white cell counts from bone marrow preparations are very useful in evaluation of various hematologic disorders. It is tedious to locate, identify, and count these classes of cells, even by skilled hands. Automation of classification and counting would be of great benefit. However, the class structure of bone marrow or peripheral blood cells is not discrete; it represents a biological continuum of maturation levels. Because of this, there is uncertainty and overlap in characteristics of adjacent cell classes such that traditional pattern recognition techniques have difficulty in arriving at accurate cell counts. In this into one of the constituent groups and then simply increment the appropriate counter. However, 11s is true in many biological processes, the maturaticn of white blood cells represents a biological continiium; the transitions between categories are "artificial boundaries". Hence, many classification errors can, and do, occur between adjacent maturation cl,mses when crisp boundaries are assumed. In this paper, we use a soft computing approach to train neural networks to effectively produce the correct total class counts instead of focusing on classification and compare these results to several traditional classifiers. Myeloid Lineage ClassificationWe describe research efforts to perform standard classification of the 6 categories of granulocytic cells from bone marrow. Before automatic classification, single cells must be extracted, or segmented, from a microscopic bone marrow image and labeled. Then, we can extract features from each single ce!U and paper, we investigate soft counting networks that are trained to produce accurate overall class counts by allowing cells to have degrees of membership in multiple cell classes. This approach is applied to a bone marrow cell library and i s compared with other standard recognition algorithms.

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