Abdu Gumaei

TL;DR: A hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach and the experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches.

TL;DR: A reinforcement-learning-based state-action-reward-state-action (RL-SARSA) algorithm to resolve the resource management problem in the edge server, and make the optimal offloading decision for minimizing system cost, including energy consumption and computing time delay is proposed.

TL;DR: A hybrid deep learning model to efficiently detect network intrusions based on a convolutional neural network (CNN) and a weight-dropped, long short-term memory (WDLSTM) network is proposed, which uses the deep CNN to extract meaningful features from IDS big data and W DLSTM to retain long-term dependencies among extracted features to prevent overfitting on recurrent connections.

TL;DR: A novel two-stage deep learning model based on a stacked auto-encoder with a soft-max classifier for efficient network intrusion detection that has the potential to serve as a future benchmark for deep learning and network security research communities.

TL;DR: This paper proposes a security architecture that integrates the Blockchain and the Software-defined network technologies to defend against forged commands and misrouting of commands in industrial IoT systems and test the effectiveness and efficiency of the proposed security solution.