Abstract The study of effective methods for intrusion detection in audit trail logs is an important part of the vast effort to improve Intrusion Detection Systems that constitute one of the primary approach in the problem of computer security. Different methods have been proposed including: Neural Networks, Immune Systems, and Genetic Algorithms. Security Audit trail Analysis can be accomplished by searching... The study of effective methods for intrusion detection in audit trail logs is an important part of the vast effort to improve Intrusion Detection Systems that constitute one of the primary approach in the problem of computer security. Different methods have been proposed including: Neural Networks, Immune Systems, and Genetic Algorithms. Security Audit trail Analysis can be accomplished by searching audit trail logs of user activities for known attacks. The later problem is NPComplete. Metaheuristics offer an alternative to solve this type of problem. In this paper, we propose to use Biogeography Based Optimization algorithm (BBO) as analysis engine. BBO is a population-based evolutionary algorithm well suited for constrained optimization problems. Experimental results for simulated attacks are reported. The effectiveness of the approach is evaluated by its ability to make correct predictions. It has proven effective and capable of producing a reliable method for intrusion detection. Read More Read Less

Abstract Powerful CASE tools for Aspect Oriented System (AOS) are useful to improve the developer productivity and ensure syntactical correctness of the AOS model for effective system design. This paper has proposed a systematic approach to develop CASE tool for graph semantic based AOS model called, GAM, using meta-configurable environment called, Generic Modeling Environment (GME). Graph – semantic based Aspect Model... Powerful CASE tools for Aspect Oriented System (AOS) are useful to improve the developer productivity and ensure syntactical correctness of the AOS model for effective system design. This paper has proposed a systematic approach to develop CASE tool for graph semantic based AOS model called, GAM, using meta-configurable environment called, Generic Modeling Environment (GME). Graph – semantic based Aspect Model (GAM) is a formal aspect oriented model to conceptualize the different facets of cross cutting concerns in AOS systems like join points, advices, PointCut etc. using graph based semantics. The proposed approach in this paper is comprised of set of semantic transformation rules for systematic mapping of concepts, semantics and notation syntax of GAM into the GME modeling paradigm elements. The approach is also supported with the correctness checking of such mapping using structural correspondence method. Moreover the expressiveness of the GME based CASE tool implementation of GAM has been illustrated using case study. Read More Read Less

Abstract Automatic satellite image registration for multi-sensor images is becoming increasingly important to aid in flood damage assessment. We consider two images, the one before flood (optical image) and the other during flood (SAR image) in the registration process. The objective is to maximize the similarity metric (of these two images) using information theoretic measures such as Mutual Information (MI). The... Automatic satellite image registration for multi-sensor images is becoming increasingly important to aid in flood damage assessment. We consider two images, the one before flood (optical image) and the other during flood (SAR image) in the registration process. The objective is to maximize the similarity metric (of these two images) using information theoretic measures such as Mutual Information (MI). The maximum MI would imply that the images are registered better. The function of these metric for transformation parameters are generally non-convex and irregular and, therefore, makes it difficult to use the standard optimization methods for the global solution. In this paper, we present the nature inspired techniques – Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) to search the maximum MI. From the results obtained, we compare the performance evaluation and conclude that the nature inspired techniques are accurate and reliable in solving the automatic satellite image registration. Read More Read Less

Abstract The satisfiabiltiy problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. In this work, an evolutionary algorithm enhanced with the multilevel paradigm is introduced. The multilevel paradigm aims at dividing large and difficult problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution... The satisfiabiltiy problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. In this work, an evolutionary algorithm enhanced with the multilevel paradigm is introduced. The multilevel paradigm aims at dividing large and difficult problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using a solution from a previous level as a starting solution at the next level. Results comparing the proposed algorithm with and without the multilevel paradigm are presented. Read More Read Less

Abstract In this paper, we present a new algorithm for target detection using hyperspectral imagery. The proposed algorithm is inspired by the outstanding performance of nonlinear Kernel method and the robustness of the stochastic expectation maximization (SEM) algorithm. The traditional technique of using SEM algorithm for target detection in hyperspectral imagery is associated with dimensionality reduction of the input data... In this paper, we present a new algorithm for target detection using hyperspectral imagery. The proposed algorithm is inspired by the outstanding performance of nonlinear Kernel method and the robustness of the stochastic expectation maximization (SEM) algorithm. The traditional technique of using SEM algorithm for target detection in hyperspectral imagery is associated with dimensionality reduction of the input data using binning or principal components analysis (PCA) algorithm. Although, the data reduction of the input data is enforced to reduce the computational burden on SEM algorithm, but it affects the results of target detection, especially the challenging one, due to not using the entire information of the potential targets. To facilitate detection of the target by using the entire targets information and simultaneously reducing the computational burden on SEM algorithm, we propose a new scheme for data reduction based on using Kernels. Kernel-based input data reduction is a nonlinear filtering technique in which the input data are mapped to the feature space where most of the background data is filtered using an easily selected threshold. Then, Gaussian mixture model is generated for the reduced input-data and SEM algorithm is employed to estimate the model parameters and to classify that input data. Finally, we allocated the target’s class and isolated the target pixels. The proposed scheme for fusion the kernel with SEM algorithm has been tested using real life hyperspectral imagery and the results show superior performance compared to alternate algorithms. Read More Read Less