Intrusion Detection System Using Feature Selection and classifier based Algorithm

With the enlargement of web, there has been a terrific increases in the number of attacks and therefore Intrusion Detection Systems (IDS�s) has become a main topic of information security. The purpose of IDS is to help the computer systems to deal with attacks. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks efficiently has been proposed. We have proposed an algorithm based on associan rule to detect intrusion. We have combined algorithm with feature selection to improve efficiency of IDS.The proposed feature selection and associan rule algorithms enhance the performance of the IDS in detecting the attacks

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

A machine learning-based investigation of cloud service attacks

In this thesis, the security challenges of cloud computing are investigated in the Infrastructure as a Service (IaaS) layer, as security is one of the major concerns related to Cloud services. As IaaS consists of different security terms, the research has been further narrowed down to focus on Network Layer Security. Review of existing research revealed that several types of attacks and threats can affect cloud security. Therefore, there is a need for intrusion defence implementations to protect cloud services. Intrusion Detection (ID) is one of the most effective solutions for reacting to cloud network attacks. [Continues.

Feature Selection and Classification using Flexible Neural Tree", Neurocomputing

Abstract The purpose of this research is to develop effective machine learning or data mining techniques based on Flexible Neural Tree (FNT). Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using Genetic Programming (GP) and the parameters are optimized by a Memetic Algorithm (MA). The proposed approach was applied for two real world problems involving designing Intrusion Detection System (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate

Neurocomputing 70 (2006) 305–313 Feature selection and classification using flexible neural tree

The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input feature selection and improved classification rate. r 2006 Elsevier B.V. All rights reserved