Adopting genetic algorithm to develop a neural network for recognition of network intrusion - K, Shashank; Balachandra, Mamatha (International Journal of Engineering and Technology, 2019)
Protecting the network from unapproved access and exposure is called network security. Machine learning has been significantly useful in detecting the attack patterns. Deep learning being one among them is the standard tool for feature extraction and transformation. Building a deep learning model becomes tedious due to the substantial computation involved. Ultimately it can be optimized by suitably selecting the hyper parameters to evolve a neural network that gives the best results. The genetic algorithm is one such algorithm that can be employed to optimize the training phase of a deep learning model by selection of the best parameters. In the paper, a genetic algorithm based deep learning model is proposed to build a network intrusion detection system. NSL KDD dataset with 41 feature attributes is used to train and test the model.Â Â