Demystifying Neural Network: A Case Study on Intrusion Detection System

Considering the historical context of Deep Neural Networks (DNNs), it can be inferred that DNN has had a promising history that has now evolved with various philosophical viewpoints. Modeling DNN has now become effortless with the generation and availability of large amount of data. Moreover, with the development of improved hardware and software infrastructure requirements, DNN models have now grown in size. DNN models have the capability of addressing complex application problems with improved accuracy over time. The art and science of DNN is based on the foundation of neural network. Thus, this paperr aims at discussing the fundamentals of neural networks and how they work. The paper includes a brief discussion on functionality of neural network, role of activation functions, backpropagation algorithm, loss function calculations, and optimizers for neural networks. Further, the paperr also discusses generalization in neural network and parameters in neural network architecture. The paper also includes a case study on intrusion detection model building using neural network. The demystifying neural network is the first stride towards understanding DNN.