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- W4285152511 abstract "Behaviour of network evolves with respect to time. With the increase in utilization of inter-networking, the need of protecting the physical systems as well as the networks from unauthorized access or any kind of intrusion has aroused. Intrusion detection system (IDS) detects network behaviour and notifies in case of any anomaly. But, there may be a lot of false positives in this event. Due to the absence of publicly accessible information indices, it becomes a very challenging task to test an intrusion detection based on anomaly, which increases the possibility of encountering false positives during the event. This paper further focuses on to train the generated data set based on deep learning features transferred from Oxford’s Visual Geometry Group (VGG-19) neural network so as to accurately detect any such future anomalies by recognizing pattern of their behaviour." @default.
- W4285152511 created "2022-07-14" @default.
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- W4285152511 date "2022-01-01" @default.
- W4285152511 modified "2023-09-30" @default.
- W4285152511 title "Building NTH: Network Threat Hunter with Deep Learning" @default.
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- W4285152511 doi "https://doi.org/10.1007/978-981-19-0475-2_50" @default.
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