In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.
Facial identification has been recognized as most simple and non-intrusive technology that can be applied in many places. However, there are still many unsolved facial identification problems due to different intra-personal variations. In particular, when images of the databases appear at different facial expressions, most currently available facial recognition approaches encounter the expression-invariant problem in which neutral faces are difficult to be recognized. In this paper, a new approach is proposed to transform facial expressions to neutral-face like images; hence enabling image retrieval systems to robustly identify a person's face for which its learning and testing face images differ in facial expression.
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