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Mobile ad hoc network (MANET) is a self-created and self organized network of wireless mobile nodes. Due to special characteristics of these networks, security issue is a difficult task to achieve. Hence, applying current intrusion detection techniques developed for fixed networks is not sufficient for MANETs. In this paper, we proposed an approach based on(More)
Network topology in mobile ad hoc networks (MANETs) changes rapidly due to the mobility of nodes. Hence, an important challenge for these networks is to develop an approach which can detect anomalies in network traffic with high accuracy despite the dynamic changing of network topology. In this paper, we present a hybrid approach based on the artificial bee(More)
The main goal of one-class classification is to classify one class from remaining feature space. One-class SVM is a kernel based approach which is very fast and precise and therefore is used in different fields such as image processing, protein classification and anomaly detection for statistical learning. There are some approaches suggested for anomaly(More)
Mobile ad hoc networks (MANETs) are multi-hop wireless networks of mobile nodes constructed dynamically without the use of any fixed network infrastructure. Due to inherent characteristics of these networks, malicious nodes can easily disrupt the routing process. A traditional approach to detect such malicious network activities is to build a profile of the(More)
Feature selection is an important task to improve prediction accuracy of classifiers and to decrease the problem size. Several approaches have been presented to perform feature selection using metaheuristic algorithms. In this paper, we employ the binary quantum-inspired gravitational search algorithm (BQIGSA) combined with the k-nearest neighbor classifier(More)
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