Manas Ranjan Patra

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Intrusion detection is an emerging area of research in the computer security and networks 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 behaviour or anomalous. However, these single classifier systems fail to provide the(More)
Cloud computing is an architecture for providing computing service via the internet on demand and pay per use access to a pool of shared resources namely networks, storage, servers, services and applications, without physically acquiring them. So it saves managing cost and time for organizations. Many industries, such as banking, healthcare and education(More)
This paper applies discriminative multinomial Naïve Bayes with various filtering analysis in order to build a network intrusion detection system. For our experimental analysis, we used the new NSL-KDD dataset, which is considered as a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. We perform 2 class classifications with 10-fold(More)
An intrusion is defined as a violation of the security policy of the system, and hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Recently, data mining techniques have gained importance in providing the valuable information which in turn can help to enhance the decision on(More)
The growing dependence of modern society on telecommunication and information networks has become inevitable. Therefore, the security aspects of such networks play a strategic role in ensuring protection of data against misuse. Intrusion Detection systems (IDS) are meant to detect intruders who elude the “first line” protection. Data mining techniques are(More)
Intrusion detection technology is an effective approach to deal with problems of malicious attacks on computer networks. In this paper, we present an intrusion detection model based on Ensemble of classifiers such as AdaBoost, MultiBoosting and Bagging to gain more opportunity of training misclassified samples and reduce the error rate by the majority(More)