A Novel Deep Learning Model to Enhance Network Traffic Monitoring for Cybersecurity


  • Abhijit Das Research Scholar, VTU, PESITM, Shimoga-577205, Department of CSE, Karnataka, INDIA
  • Pramod Associate Professor, PESITM, Shimoga-577205, VTU, Department of ISE, Karnataka, INDIA


Cyber Security, Deep Learning, Monitoring, Networks, Traffic


The data amount flowing through computer networks at any specified period is referred to as network traffic. Data packets are broken down into networks traffic and transmitted over a network before being reconstructed by the receiving computers or devices. In the present time, network traffic has become a major problem of rapid increment in network devices which creates complexity in networking data. So, to overcome this problem, the author gives the method which is linked with deep learning methods and will also help to monitor the network traffic for cybersecurity. This paper gives the deep learning method which helps to find the network traffic as well as providing the cybersecurity to that network. The suggested method contains the deep learning method (DLM), convolutional neural network (CNN), recurrent neural network (RNN) and Long Short-Term Memory (LSTM). The method provides better accuracy results for observing the network traffic and also provides cybersecurity by using deep learning methods. This research shows that deep learning techniques can be used to build massive deep learning techniques for networks classification that can be applied in the real world where they can preserve classifying accuracy and increase classification speed considering restricted resource availability.


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In this diagram shows the convolutional neural network with the tensor reshape fully connected and gives outputs of given dimensions




How to Cite

Das, A. ., & Pramod. (2022). A Novel Deep Learning Model to Enhance Network Traffic Monitoring for Cybersecurity. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 335 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2299



Research Article