Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

An Architecture Supporting Intelligent Mobile Healthcare Using Human-Computer Interaction HCI Principles

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several devices.Moreover,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters.This raises potential challenges for intelligent monitoring of patient's health.In this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles.The HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare models.Furthermore,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving lives.The intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related information.Health care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent assistant.The HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.