Survey on Ai-Enhanced Network Security with E...

Survey on Ai-Enhanced Network Security with Efficient NETB0

Author Name : Nithya P, Sofiya P, Anandkumar R

ABSTRACT In recent years, machine learning and data mining techniques have greatly improved network intrusion detection. These methods allow automation of anomaly detection in network traffic and thus improve general security measures. One of the biggest problems for researchers is the lack of published data for research purposes. In this project, we propose that an artificial intelligence (AI) intrusion detection system using a convolutional neural network with EfficientNetB0 be investigated and tested on the CTU13 dataset in response to ever-evolving network attacks. First, the data were pre-processed by data transformation and normalization for input into the CNN EfficientNetB0 model. CNN's EfficientNetB0 algorithm was applied to data that was preprocessed to create a learning model and validated using the full CTU-13 dataset. Finally, the accuracy, detection rate and false alarm rate were calculated to ensure the detection performance of the CNN EfficientNetB0 model. The model was found to perform well in intrusion detection.