The industrial sector has been making use of machines and sensors having the capability to communicate with each other. This forms a network of Industrial devices which is called Industrial Internet of Things (IIoT). IIoT is an emerging trend that can generate a huge amount of data that is vulnerable to cyber-attacks. In IIoT network, data and control instructions flow through Supervisory Control and Data Acquisition (SCADA) system. A Network Intrusion Detection System (NIDS) which can monitor realtime network traffic could be deployed between SCADA and the IIoT devices to detect cyber-attacks. NIDS with Deep Learning (DL) algorithms require large dataset for which CSE-CIC-IDS2018 and UNSW-NB15 dataset is used. The paper compares a Multi-Layer Perceptron(MLP), a Fully Connected Deep Neural Network (FCNN), and Convolutional Neural Network (CNN) on CSE-CIC-IDS2018 and UNSW-NB15 with XGBoost for feature selection.

This paper implements the automated classification of patient discharge notes into standard disease labels which includes the name of the diagnostic procedure required. In this approach, we use Convolutional Neural Networks to classify and represent complex features from the medical discharge summaries using the MT sample dataset. We make use of GloVE to have a pretrained model learn from it.