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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...
For applications that rely on large databases as the core data structure, the need for a fast search process is essential. Hashing algorithms have widely been adopted as the search algorithm of choice for fast lookups. Hashing algorithms involve the creation of hash values from the target database entries. A hashing algorithm that transforms the database to hash values with a distribution as uniform...
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