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Smart textiles are fabrics that have been designed and manufactured to include technologies that provide the wearer with increased functionality. These textiles have numerous potential applications, such as the ability to communicate with other devices, conduct energy, transform into other materials and protect the wearer from environmental hazards. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection and safety, and healthy lifestyle gained strong interest during the last few years. Smart fabrics and interactive textiles' activities include personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/ actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. This writing includes the origin and introduction of smart textile and integrated wearable electronics for sport wear, industrial purpose, automotive and entertainment applications, healthcare & safety, military, public sectors and new developments in smart textiles.

Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest data set available at online, formatted with packet based, flow based data and some additional metadata. The data set is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multi-class classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.