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Abstract Existing machine learning solutions for ne Existing machine learning solutions for networkbased intrusion detection cannot maintain their reliability over time when facing high-speed networks and evolving attacks. In this paper, we propose BigFlow, an approach capable of processing evolving network traffic while being scalable to large packet rates. BigFlow employs a verification method that checks if the classifier outcome is valid in order to provide reliability. If a suspicious packet is found, an expert may help BigFlow to incrementally change the classification model. Experiments with BigFlow, over a network traffic dataset spanning a full year, demonstrate that it can maintain high accuracy over time. It requires as little as 4% of storage and between 0.05% and 4% of training time, compared with other approaches. BigFlow is scalable, coping with a 10-Gbps network bandwidth in a 40-core cluster commodity hardware. h in a 40-core cluster commodity hardware.
Author Eduardo Viegas + , Altair Santin + , Alysson Bessani + , Nuno Ferreira Neves +
Journal Future Generation Computer Systems  +
Key FGCS19  +
Month apr  +
NumPubDate 2,019.04  +
Pages 473–485  +
ResearchLine Fault and Intrusion Tolerance in Open Distributed Systems (FIT) +
Title BigFlow: Real-time and Reliable Anomaly-based Intrusion Detection for High-Speed Networks  +
Type article  +
Volume 93  +
Year 2019  +
Has improper value forThis property is a special property in this wiki. Url  +
Categories Publication  +
Modification dateThis property is a special property in this wiki. 19 July 2019 08:57:56  +
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