Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.Comment: 9 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis

Spam Recognition using Linear Regression and Radial Basis Function Neural Network

Spamming is the abuse of electronic messaging systems to send unsolicited bulk messages. It is becoming a serious problem for organizations and individual email users due to the growing popularity and low cost of electronic mails. Unlike other web threats such as hacking and Internet worms which directly damage our information assets, spam could harm the computer networks in an indirect way ranging from network problems like increased server load, decreased network performance and viruses to personnel issues like lost employee time, phishing scams, and offensive content. Though a large amount of research has been conducted in this area to prevent spamming from undermining the usability of email, currently existing filtering methods\u27 performance still suffers from extensive computation (with large volume of emails received) and unreliable predictive capability (due to highly dynamic nature of emails). In this chapter, we discuss the challenging problems of Spam Recognition and then propose an anti-spam filtering framework; in which appropriate dimension reduction schemes and powerful classification models are employed. In particular, Principal Component Analysis transforms data to a lower dimensional space which is subsequently used to train an Artificial Neural Network based classifier. A cost-sensitive empirical analysis with a publicly available email corpus, namely Ling-Spam, suggests that our spam recognition framework outperforms other state- of-the-art learning methods in terms of spam detection capability. In the case of extremely high misclassification cost, while other methods\u27 performance deteriorates significantly as the cost factor increases, our model still remains stable accuracy with low computation cost

Optimal Efficiency Control of Synchronous Reluctance Motors-based ANN Considering Cross Magnetic Saturation and Iron Loss

This paper presents a new idea by using the Artificial Neural Networks (ANNs) for estimating the parameters of the machine which achieving the maximum efficiency of the Synchronous Reluctance Motor (SynRM). This model take into consideration the magnetic saturation, cross-coupling and iron loss. With Finite Element Analysis (FEA), the characteristics of the SynRM including inductances and iron loss resistance are determined. Because of the non-linear characteristics, an ANN trained off-line, is then proposed to obtain the d-q inductances and iron loss resistance from Id,Iq currents and the speed. After learning process, an analytical expression of the optimal currents is given thanks to Lagrange optimization. Therefore, the optimal currents will be obtained online in real time. This method can be achieved with maximum efficiency and high-precision torque control. Simulation and experimental results are presented to confirm the validity of the proposed method

Catalytic Promiscuity and the Evolutionary Mechanism of NSAR Reaction Specificity in the NSAR/OSBS Subfamily

Catalytic promiscuity is the coincidental ability for an enzyme to catalyze nonbiological reactions in the same active site as the native biological reaction. Several lines of evidence show that catalytic promiscuity plays an important role in the evolution of new enzyme functions. Studying catalytic promiscuity can help identify structural features that predispose an enzyme to evolve new functions. This dissertation studies the mechanistic basis of catalytic promiscuity in the evolution of N-succinylamino acid racemase (NSAR) activity in the NSAR/o-succinylbenzoate synthase (NSAR/OSBS) subfamily. The NSAR/OSBS subfamily is a branch of the OSBS family, which belongs to the mechanistically diverse enolase superfamily. We identified a conserved, second-shell residue R266 in all the NSAR/OSBS enzymes while the homologous position is usually hydrophobic in other nonpromiscuous OSBS subfamilies. We found that the R266 residue is an important NSAR reaction specificity determinant because the R266Q mutation in Amycolatopsis NSAR/OSBS enzyme profoundly reduces NSAR activity while having a moderate effect on OSBS activity. Mechanistic investigation by hydrogen-deuterium exchange showed that R266 modulates the reactivity of the catalytic base K263, but not the other catalytic base K163. The crystal structure of Amycolatopsis NSAR/OSBS R266Q mutant shows that K263 adopts a different conformation, so it is not positioned correctly to act as a general acid/base for catalysis. Further kinetic and mechanistic studies of the R266Q mutation in other NSAR/OSBS members showed that R266 is also important for NSAR activity. However, the specific phenotypic effects of the R266Q mutation are masked by the sequence and structural contexts in which the mutation occurs (that is, epistatic constraints), making it harder to fully understand the roles of R266 in the NSAR/OSBS subfamily. Up to date, R266 is the first residue that was identified in the NSAR/OSBS enzymes to be pre-adaptive and vital for NSAR activity, and such identification is essential to understand the evolution of NSAR activity in the NSAR/OSBS subfamily. This finding is significant because it can help us understand the reaction specificity determinants in other enzymes. For example, mutating the homologous lysine to a glutamine in members of the dipeptide epimerase family also has a deleterious effect on the activity, further supporting the universal importance of a positively charged amino acid at position 266 for the epimerase/racemase activity in the enolase superfamily. This dissertation provides the mechanistic basis of determining epimerase/racemase reaction specificity in enzymes and can be ultimately used as a predictive tool for functional annotations, the development of protein engineering, and the improvement of protein design methods

Catalytic Promiscuity and the Evolutionary Mechanism of NSAR Reaction Specificity in the NSAR/OSBS Subfamily

Catalytic promiscuity is the coincidental ability for an enzyme to catalyze nonbiological reactions in the same active site as the native biological reaction. Several lines of evidence show that catalytic promiscuity plays an important role in the evolution of new enzyme functions. Studying catalytic promiscuity can help identify structural features that predispose an enzyme to evolve new functions. This dissertation studies the mechanistic basis of catalytic promiscuity in the evolution of N-succinylamino acid racemase (NSAR) activity in the NSAR/o-succinylbenzoate synthase (NSAR/OSBS) subfamily. The NSAR/OSBS subfamily is a branch of the OSBS family, which belongs to the mechanistically diverse enolase superfamily. We identified a conserved, second-shell residue R266 in all the NSAR/OSBS enzymes while the homologous position is usually hydrophobic in other nonpromiscuous OSBS subfamilies. We found that the R266 residue is an important NSAR reaction specificity determinant because the R266Q mutation in Amycolatopsis NSAR/OSBS enzyme profoundly reduces NSAR activity while having a moderate effect on OSBS activity. Mechanistic investigation by hydrogen-deuterium exchange showed that R266 modulates the reactivity of the catalytic base K263, but not the other catalytic base K163. The crystal structure of Amycolatopsis NSAR/OSBS R266Q mutant shows that K263 adopts a different conformation, so it is not positioned correctly to act as a general acid/base for catalysis. Further kinetic and mechanistic studies of the R266Q mutation in other NSAR/OSBS members showed that R266 is also important for NSAR activity. However, the specific phenotypic effects of the R266Q mutation are masked by the sequence and structural contexts in which the mutation occurs (that is, epistatic constraints), making it harder to fully understand the roles of R266 in the NSAR/OSBS subfamily. Up to date, R266 is the first residue that was identified in the NSAR/OSBS enzymes to be pre-adaptive and vital for NSAR activity, and such identification is essential to understand the evolution of NSAR activity in the NSAR/OSBS subfamily. This finding is significant because it can help us understand the reaction specificity determinants in other enzymes. For example, mutating the homologous lysine to a glutamine in members of the dipeptide epimerase family also has a deleterious effect on the activity, further supporting the universal importance of a positively charged amino acid at position 266 for the epimerase/racemase activity in the enolase superfamily. This dissertation provides the mechanistic basis of determining epimerase/racemase reaction specificity in enzymes and can be ultimately used as a predictive tool for functional annotations, the development of protein engineering, and the improvement of protein design methods