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A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion
A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion
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.
- University of North Carolina at Chapel Hill United States
Microsoft Academic Graph classification: Feature learning Data mining computer.software_genre computer Intrusion Intrusion detection system Supervised learning Task (computing) Traffic classification Volume (computing) Computer science Big data business.industry business Machine learning Artificial intelligence
Microsoft Academic Graph classification: Feature learning Data mining computer.software_genre computer Intrusion Intrusion detection system Supervised learning Task (computing) Traffic classification Volume (computing) Computer science Big data business.industry business Machine learning Artificial intelligence
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).5 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).5 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
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- University of North Carolina at Chapel Hill United States
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.