UAV-Based Secure Data Communication: Multilevel Authentication Perspective.

An Internet of Things (IoT) system for managing and coordinating unmanned aerial vehicles (UAVs) has revolutionized the industrial sector. The largest issue with the design of the Internet of UAVs (IoUAV) is security. Conspicuously, the novel contribution of the proposed work is to develop a layered authentication approach to facilitate safe IoUAV communication. Specifically, four modules, including the pre-deployment module, user registration module, login module, and authentication module, form the basis of security analysis. In the proposed technique, UAVs are added to the IoUAV registry. The next step is the user registration module, where people are registered with the UAV so they may access the information in real time. In the login module, the user connects with the server for data transmission. Finally, in the authentication module, all entities, including users, servers, and UAVs, are authenticated to ensure secure data communication. The proposed method achieves peak performance as compared to the state-of-the-art techniques in terms of statistical parameters of latency (3.255s), throughput (90.15%), and packet loss (8.854%).

IoT-based intrusion detection system using convolution neural networks.

In the Information and Communication Technology age, connected objects generate massive amounts of data traffic, which enables data analysis to uncover previously hidden trends and detect unusual network-load. We identify five core design principles to consider when designing a deep learning-empowered intrusion detection system (IDS). We proposed the Temporal Convolution Neural Network (TCNN), an intelligent model for IoT-IDS that aggregates convolution neural network (CNN) and generic convolution, based on these concepts. To handle unbalanced datasets, TCNN is accumulated with synthetic minority oversampling technique with nominal continuity. It is also used in conjunction with effective feature engineering techniques like attribute transformation and reduction. The presented model is compared to two traditional machine learning algorithms, random forest (RF) and logistic regression (LR), as well as LSTM and CNN deep learning techniques, using the Bot-IoT data repository. The outcomes of the experiments depicts that TCNN maintains a strong balance of efficacy and performance. It is better as compared to other deep learning IDSs, with a multi-class traffic detection accuracy of 99.9986 percent and a training period that is very close to CNN.

Domestic Violence Against Married Women During the COVID-19 Quarantine in Saudi Arabia.

Background  Before the coronavirus disease 2019 (COVID-19) pandemic, cases of domestic abuse and aggressive behaviour between Saudi married couples were increasing annually, a topic of growing concern both socially and medically. With the forced indoor confinement enacted as a containment measure, international studies regarding domestic abuse indicated an almost unanimous increase in prevalence. This cross-sectional national study aimed to assess the change between the pre-and intra-pandemic prevalence of abuse in Saudi Arabia.  Material and methods  Anonymous data were gathered using a web-based Arabic version of the World Health Organization (WHO) multi-country instrument measuring violence against women residing in Saudi Arabia. The previously validated questionnaire included a series of multiple-choice questions related to demographic information, family infrastructure, experienced situations of abuse, and the severity and form of abuse during the quarantine period, from March 23, 2020, to June 21, 2020. Associations were tested using a two-tailed Pearson's Chi-square test and odds ratios. A binary multivariate logistic regression was used to identify the independent factors associated with domestic violence.  Results  In total, 2254 participants were included in the present study. The majority (n=2129, 94.7%) were Saudi nationals. The highest proportion (n=1022, 45.3%) was in the 30 to 40 years age group. The self-reported prevalence of domestic violence before COVID-19 pandemic and quarantine was 25.4% and 16.6% during the confinement, indicating an overall decrease of 8.8% in the reported cases. Regarding the type of violence, of the 315 (16.6%) women who endured violence since the confinement, the majority (n=301, 95.6%) experienced multiple forms of violent abuse, 264 (87.7%) suffered from psychological/emotional violence, 114 (37.9%) from physical violence, and 50 (16.6%) from sexual violence. Of the group who experienced multiple forms of violence, 120 (39.9%) reported an increase in the frequency and perceived intensity of the violence since the confinement. The only variable that directly increased the likelihood of suffering domestic violence had more than three children [OR = 1.59, P = 0.018]. Conclusions  Contrary to trends observed in other countries, the national prevalence of abusive conduct towards married women showed a marked decrease during the quarantine period-more children directly correlated with a higher reported frequency of being abused. Further studies in neighbouring countries with comparable societies and structures must be conducted to assess the validity of our findings in the context of the global trends of violence in the marital home.

Sunscreen Use among a Population of Saudi University Students.

RESULTS: A total of 1,011 students were enrolled. Approximately half were males (n = 510). Half of the students used sunscreen (n = 515, 51%). Female gender, high family income, previous history of sunburn, tanning bed use, and use of other sun protection methods were factors independently associated with sunscreen use. The main reasons for using sunscreen were prevention of sunburns, dark spots, skin cancer, and overall skin darkening. Eighty percent of participants used other methods of sun protection. Sunscreen with a sun protection factor (SPF) > 30 was used in 59% of students. However, the majority did not know if the sunscreen they use provided broad-spectrum coverage or not. Only 35% of students apply sunscreen in both sunny and cloudy days. Most students apply sunscreen less than 10 minutes before going out and do not repeat the application throughout the day. More than 90% of students seem to apply insufficient amount of sunscreen. CONCLUSION: Almost half of the population in the study use sunscreen. We have identified several areas of improper use of sunscreen. Increasing the awareness of effective sunscreen use in our community might be needed.