A Software-Defined Network (SDN) was designed to simplify network management by allowing the control and management of the entire network from a single place. SDN is commonly used in today’s data center network infrastructures, but new forms of threats such as Distributed Denial-of-Service (DDoS), web attack, and the U2R (User to Root) attack are significant issues that might restrict the widespread adoption of SDNs. Intruders are attractive to SDN controllers because they are valuable targets. An SDN controller can be hijacked by an attacker and used to route traffic in accordance with its own needs, resulting in catastrophic consequences for the whole network. While the unified vision of SDN and deep learning methods opens new possibilities for the security of IDS deployment, the effectiveness of the detection models is dependent on the quality of the training datasets. Even though deep learning for NIDSs has lately shown promising results for a number of issues, the majority of the studies overlooked the impact of data redundancy and an unbalanced dataset. As a consequence, this may adversely affect the resilience of the anomaly detection system, resulting in suboptimal model performance. In this study, we created a hybrid Convolutional Neural Network (CNN) and bidirectional long short-term memory (BiLSTM) network to enhance network intrusion detection using binary and multiclass classification. The effectiveness of the proposed model was tested and assessed using the most frequently used datasets (UNSW-NB15 and NSL-KDD). In addition, we used the InSDN dataset, which is specifically dedicated to SDN. The outcomes demonstrate the efficiency of the proposed model in achieving high accuracy and requiring less training time.

Recognition of human actions in partially cluttered environments is an important research field of computer vision and human–computer interaction. This field has recently garnered attention from a large number of academic researchers in various fields of application. This study proposes a novel deep-learning-based architecture for the recognition and prediction of human actions based on a hybrid model. The main contribution of this study is to propose a new hybrid architecture, integrating four wide-ranging pre-trained network models in an optimized manner, using a metaheuristic algorithm. This architecture consists of four main stages: namely, the creation of the data set, the design of deep neural network (DNN) architecture, training and optimization of the proposed DNN architecture, and evaluation of the trained DNN. By adapting the aforementioned architecture, reliable features are obtained for the training procedure. In order to validate the superiority of the proposed architecture over other state-of-the-art studies, a performance evaluation between these architectures is presented using benchmark datasets. The results reveal that the proposed architecture outperforms previously developed architectures in terms of predicting human actions.

The aim of this review is the analysis of dynamical properties of Josephson junctions (JJ) with anharmonic current-phase relation (CPR). Firstly, discussion of theoretical foundation of anharmonic CPR in different Josephson structures and their experimental observation are presented. The influence of anisotropy and multiband effects on CPR of JJ are analyzed. We present recent theoretical study results of the anharmonic CPR influence on I–V curve, plasma frequency, and dynamics of long JJ. Results of study of Shapiro steps in I–V curve of anharmonic JJ are also presented. Finally, CPR anharmonicity effect on characteristics of JJ-based qubits is discussed.