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This paper presents a novel method to combine k-means clustering and ID3 decision trees learning algorithms for unsupervised classification of anomalous and normal activities in computer network ARP traffic. The k-means clustering method is first applied to the normal training instances to partition it into k clusters using Euclidean distance similarity. Some anomaly criteria has been defined and...
The main drawback of traditional intrusion detection systems makes anomaly detection systems an active research area. In this paper we introduce a novel network-based anomaly detection approach using stochastic learning automata. The paper main objective is to construct a network-based statistical anomaly detection system capable of classifying the ensemble network broadcast traffic as normal or abnormal...
Network anomaly detection is an active research area. Behavior recognition of traffic is a process by which the ongoing observed behavior of a host is tracked and compared by a given model. Various methods for behavior recognition exist. But incorporation of Hidden Markov Models (HMM's) for anomaly detection (ARP anomaly detection, especially) is a novel method. This paper aims at classifying the...
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