Abstract. Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate.

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Amine libraries and their derivatives are important targets for high throughput synthesis because of their versatility as medicinal agents and agrochemicals. As a part of our efforts towards automated chemical library synthesis, a titanium(IV) isopropoxide mediated solution phase reductive amination protocol was successfully translated to automation on the Trident(TM) library synthesizer of Argonaut Technologies. An array of 24 secondary amines was prepared in high yield and purity from 4 primary amines and 6 carbonyl compounds. These secondary amines were further utilized in a split synthesis to generate libraries of ureas, amides and sulfonamides in solution phase on the Trident(TM). The automated runs included 192 reactions to synthesize 96 ureas in duplicate and 96 reactions to synthesize 48 amides and 48 sulfonamides. A number of polymer-assisted solution phase protocols were employed for parallel work-up and purification of the products in each step.

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