Electrical and Electronic Engineering - Research Publications
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ItemDetecting selective forwarding attacks in wireless sensor networks using support vector machines(Institute of Electrical and Electronic Engineers, 2007)Wireless Sensor Networks (WSNs) are a new technology foreseen to be used increasingly in the near future due to their data acquisition and data processing abilities. Security for WSNs is an area that needs to be considered in order to protect the functionality of these networks, the data they convey and the location of their members. The security models and protocols used in wired and other networks are not suited to WSNs because of their severe resource constraints, especially concerning energy . In this article, we propose a centralized intrusion detection scheme based on Support Vector Machines (SVMs) and sliding windows. We find that our system can detect black hole attacks and selective forwarding attacks with high accuracy without depleting the nodes of their energy.
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ItemA monomial ν-SV method for regression( 2007)In the present paper we describe a new formulation for Support Vector regression (SVR), namely monomial ν-SVR. Like the standard ν-SVR, the monomial ν-SVR method automatically adjusts the radius of insensitivity (the tube width, epsilon) to suit the training data. However, by replacing Vapnik’s epsilon-insensitive cost with a more general monomial epsilon-insensitive cost (and likewise replacing the linear tube shrinking term with a monomial tube shrinking term), the performance of the monomial ν-SVR is improved for data corrupted by a wider range of noise distributions. We focus on the quadric form of monomial ν-SVR and show that the dual form of this is simpler than the standard ν-SVR. We show that, like Suykens’ Least-Squares SVR (LS-SVR) method (and unlike standard ν-SVR), the quadric ν-SVR dual has a unique global solution. Comparisons are made between the asymptotic efficiency of our method and that of standard ν-SVR and LS-SVR which demonstrate the superiority of our method for the special case of higher order polynomial noise. These theoretical predictions are validated using experimental comparisons with the alternative approaches of standard ν-SVR, LS-SVR and weighted LS-SVR.
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ItemIterative fuzzy support vector machine classification(IEEE, ELECTRON DEVICES SOC & RELIABILITY GROUP, 2007)
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ItemQuaternionic and complex-valued Support Vector Regression for Equalization and Function Approximation(IEEE - Institute of Electrical and Electronic Engineers, 2007)
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ItemMercer's theorem for quaternionic kernels( 2007)An extension of Mercer’s theorem to quaternionic valued kernel functions with applications in the field of machine learningis presented.
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ItemProtein topology classification using two-stage support vector machines.(Universal Academy Press, 2006)The determination of the first 3-D model of a protein from its sequence alone is a non-trivial problem. The first 3-D model is the key to the molecular replacement method of solving phase problem in x-ray crystallography. If the sequence identity is more than 30%, homology modelling can be used to determine the correct topology (as defined by CATH) or fold (as defined by SCOP). If the sequence identity is less than 25%, however, the task is very challenging. In this paper we address the topology classification of proteins with sequence identity of less than 25%. The input information to the system is amino acid sequence, the predicted secondary structure and the predicted real value relative solvent accessibility. A two stage support vector machine (SVM) approach is proposed for classifying the sequences to three different structural classes (alpha, beta, alpha+beta) in the first stage and 39 topologies in the second stage. The method is evaluated using a newly curated dataset from CATH with maximum pairwise sequence identity less than 25%. An impressive overall accuracy of 87.44% and 83.15% is reported for class and topology prediction, respectively. In the class prediction stage, a sensitivity of 0.77 and a specificity of 0.91 is obtained. Data file, SVM implementation (SVMHEAVY) and result files can be downloaded from http://www.ee.unimelb.edu.au/ISSNIP/downloads/.
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ItemStability Analysis of the Decomposition Method for solving Support Vector Machines( 2005)In situations where processing memory is limited, the Support Vector Machine quadratic program can be decomposed into smaller sub-problems and solved sequentially. The convergence of this method has been proven previously through the use of a counting method. In this initial investigation, we approach the convergence analysis by treating the decomposed sub-problems as subsystems of a general system. The gradients of the subproblems and the inequality constraints are explicitly modelled as system variables. The change in these variables during optimization form a dynamic system modelled by vector differential equations. We show that the change in the objective function can be written as the energy in the system. This makes it a natural Lyapunov function which has an asymptotically stable point at the origin. The asymptotic stability of the whole system then follows under certain assumptions.
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ItemDisulphide Bridge Prediction using Fuzzy Support Vector Machines( 2005)One of the major contributors to the native form of protien is cystines forming covalent bonds in oxidized state. The Prediction of such bridges from the sequence is a very challenging task given that the number of bridges will rise exponentially as the number of cystines increases. We propose a novel technique for disulphide bridge prediction based on Fuzzy Support Vector Machines. We call the system DIzzy. In our investigation, we look at disulphide bond connectivity given two Cystines with and without a priori knowledge of the bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features such as the probability of occurrence of each amino acid in different secondary structure states along with psiblast profiles. The performance is compared with normal support vector machines. We evaluate our method and compare it with the existing method using SPX dataset.
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ItemPrediction of cystine connectivity using SVM(BIOMEDICAL INFORMATICS, 2005)One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein. Here, we describe a SVM (support vector machine) model for the prediction of cystine connectivity in a protein sequence with and without a priori knowledge on their bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features (probability of occurrence of each amino acid residue in different secondary structure states along with PSI-blast profiles). We evaluate our method in SPX (an extended dataset of SP39 (swiss-prot 39) and SP41 (swiss-prot 41) with known disulphide information from PDB) dataset and compare our results with the recursive neural network model described for the same dataset.