Abdul Rehman Javed

TL;DR: This systematic literature review offers a wide range of information on Industry 4.0 from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

TL;DR: This paper proposes a novel approach named CANintelliIDS, based on a combination of convolutional neural network (CNN) and attention-based gated recurrent unit (GRU) model to detect single intrusion attacks as well as mixed intrusion attacks on a CAN bus.

TL;DR: A literature review of Artificial Intelligence techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection for each AI technique is presented and the qualities and shortcomings of these methodologies are examined.

TL;DR: Cognitive Assessment of Smart Home Resident (CA-SHR) is proposed to measure the ability of smart home residents in executing simple to complex activities of daily living using pre-defined scores assigned by a neuropsychologist and improves the reliability of the CA- SHR through the correct assignment of labels to the smart home resident compared with existing techniques.

TL;DR: An anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN is proposed and achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.