Machine learning and deep learning techniques are widely used to assess intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. However, no previous research has examined the performance of the proposed model on a variety of publicly accessible datasets in detail. Due to the dynamic nature of the attack and its rapidly changing attack techniques, the publicly accessible intrusion datasets must be updated and benchmarked regularly. The deep neural network (DNN) and convolutional neural network (CNN) are examined in this article as types of deep learning models for developing a flexible and effective IDS capable of detecting and comparing them with the proposed model in detecting cyber-attacks. The constant development of network behavior and the fast growth of attacks need the development of IDS and the evaluation of many datasets produced over time through static and dynamic methods. This kind of research enables the identification of the most efficient algorithm for identifying future cyber-attacks. We proposed a novel two-stage deep learning technique hybridizing Long-Short Term Memory (LSTM) and Auto-Encoders (AE) for detecting attacks. The CICIDS2017 and CSE-CICDIS2018 datasets are used to determine the optimum network parameters for the proposed LSTM-AE. The experimental results show that the proposed hybrid model works well and is applicable for detecting attacks in modern scenarios.

We evaluate a downlink non-orthogonal multiple access (NOMA)-enable UAV-aided communication system to address the demand of spectrum usage of unmanned aerial vehicles (UAVs). In this paper, multiple NOMA users are served by an UAV to improve the effective spectrum usage. Over Nakagami-m fading model, the performance of the system was investigated based on the analysis of outage probabilities (OPs) of the NOMA users, the ergodic rate, and symbol error rate of the system under two scenarios, i.e., perfect successive interference cancellation (pSIC) and imperfect successive interference cancellation (ipSIC). Additionally, the effects of the system parameters such as the transmit power and altitude of the UAV, the coefficients of channel model on the system performance were studied. The results demonstrate that the performance of the NOMA-based system is better compared with that of the conventional orthogonal multiple access (OMA)-based system in terms of OP, throughput and ergodic rate. Considering outage probabilities at the users, the system in the ipSIC case achieves the same performance as the pSIC case at a low transmit power of the UAV. In addition, an increase in the height of the UAV decreases the ergodic capacity of each user.INDEX TERMS unmanned aerial vehicle, NOMA, outage probability, ergodic capacity.

Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and promptly send warnings to him/her for avoiding incidents related to traffic accidents. In this paper, to meet the requirement, based on radar sensors applications, the authors first use a small‐sized millimetre‐wave radar installed at the steering wheel of the vehicle to collect signals from different head movements of the driver. The received signals consist of the reflection patterns that change in response to the head movements of the driver. Then, in order to distinguish these different movements, a classifier based on the measured signal of the radar sensor is designed. However, since the collected data set is not large, in this paper, the authors propose One‐shot learning to classify four cases of driver's head movements. The experimental results indicate that the proposed method can classify the four types of cases according to the various head movements of the driver with a high accuracy reaching up to 100%. In addition, the classification performance of the proposed method is significantly better than that of the convolutional neural network (CNN) model.

Covid-19 is an infectious disease associated with acute respiratory infections caused by the SARS-CoV-2 virus. Its entrance and infection mechanisms have been identified as S protein, main protease (Mpro or 3CLpro), angiotensin-converting enzyme 2 (ACE2), and RNA-dependent RNA polymerase (RdRp). As a result, they are critical targets in the prevention of SARS-CoV-2 viral infection. Andrographis paniculata has been shown to be potentially effective in the treatment of several viruses. This study evaluated the inhibitory effects of compounds in Andrographis paniculata against the S protein, 3CLpro, ACE2, and RdRp targets by molecular docking method. The 3D structure of the SARS-CoV-2 RdRp, 3CLpro, S protein and ACE2 were obtained from the RCSB Protein Data Bank. Compounds were collected from the publication of Andrographis paniculataand these structures were obtained from the PubChem database. Molecular docking was done by AutoDock Vina software. Lipinski rule of five is used to compare compounds with drug-like and non-drug-like properties. Pharmacokinetic parameters of potential compounds were evaluated using the pkCSM tool. Based on previous publications of Andrographis paniculata, 22 main compounds were collected. The results showed that the compound 3-O-beta-D-Glucopyranosyl-14,19-dideoxyandrographolide had a strong inhibitory effect on both the RdRp and 3CLpro targets of SARS-CoV-2 virus. Analysis of Lipinski 5’s rule exhibited that 3-O-beta-D-Glucopyranosyl-14,19-dideoxyandrographolide has drug-likeness properties. In addition, the results of predicting pharmacokinetic parameters indicated that this compound had good intestinal absorption and low toxicity. The compound 3-O-beta-D-Glucopyranosyl-14,19-dideoxyandrographolide is a potential compound to become the therapeutic drug Covid-19 from Andrographis paniculata.