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Analysing and predicting the traffic of network will improve security. Network traffic analysis is implemented in different areas of applications such as banking, e commerce, etc. Different traffic analysis techniques are proposed like algorithms-based prediction, time series-based prediction model, Data mining-based analysis and ML based analysis. However, detecting intrusions with better accuracy is a nightmare while analysing vast congested traffic. In this paper to overcome the shortcomings of earlier proposed approaches, Gated Recurrent Neural Network is employed. Gated RNN provides better performance in detection, prediction and classification of intrusions in the real time network traffic. Proposed method is compared with earlier methods and validated with security metrics like accuracy and complexity
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