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In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively.

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The development of the automobile industry and civilian infrastructures improved the lifestyles of everyone in the world. In parallel to the rise in quality of life of everyone, the number of road accidents also rose. The major reason behind road accidents is emotional factors of the drivers. The emotional imbalance will influence the drivers to abandon the traffic rules, neglect speed limits, cross the signals, cross the lane, etc. Recently automobile industries have extended their researches to the development of emotion sensing systems and embedding them inside the vehicles using affective computing technology to mitigate the road accidents. These emotion sensing systems will be decisive and act as human-like driver-assistive systems in alarming the drivers. This chapter focuses on bringing out the feasibility and existing challenges of affective computing in sensing the emotional factors of drivers for improved road safety.