Two-class support vector data description
Pattern Recognition
Survey and taxonomy of feature selection algorithms in intrusion detection system
Inscrypt'06 Proceedings of the Second SKLOIS conference on Information Security and Cryptology
Learning intrusion detection: supervised or unsupervised?
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Network intrusion detection by combining one-class classifiers
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Hadoop: The Definitive Guide
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
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Classification of network traffic for intrusion detection is a Big Data classification problem. It requires an efficient Machine Learning technique to learn the characteristics of the rapidly changing varieties of traffic in large volume and high velocity so that this knowledge can be applied to a classification task. This paper proposes a supervised-learning technique called the Unit Ring Machine which utilizes the geometric patterns of the network traffic variables to learn the traffic characteristics. It provides a single-domain, representation-learning technique with a class-separate objective for the network intrusion detection. It assigns a large volume of network traffic data to a single unit-ring and categorizes them based on the varieties of network traffic, making it a highly suitable technique for the Big Data classification of network intrusion traffic.