Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm

With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure

Analysis of Hybrid Soft Computing Techniques for Intrusion Detection on Network

Intrusion detection is an action towards security of a network when a system or network is being used inappropriately or without authorization. The use of Soft Computing Approaches in intrusion detection is an Appealing co ncept for two reasons: firstly, the Soft Computing Approaches achieve tractability, robustness, low solution cost, and better report with reality. Secondly, current techniques used in network security from intrusion are not able to cope with the dynamic and increasingly complex nature of network and their security. It is hoped that Soft Computing inspired approaches in this area will be able to meet this challenge. Here we analyze the approaches including the examination of efforts in hybrid system of SC su ch as neuro - fuzzy, fuzzy - genetic, neuro - genetic, and neuro - fuzzy - genetic used the development of the systems and outcome their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestio ns for future research

Network Attacks Detection by Hierarchical Neural Network

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not. Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifie

Supervised Intrusions Detection System Using KNN

This paper is on implementations of intrusion detection system using Knn algorithm using R language. The dataset used is the KDDcup 1999 a well know bench mark for IDS. The machine learning algorithm K nearest neighbor(Knn) is use for the detection and classification for the known attacks. The experimental results are obtained using R programming language

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