COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

Social media platforms such as Facebook, Instagram, and Twitter are an inevitable part of our daily lives. These social media platforms are effective tools for disseminating news, photos, and other types of information. In addition to the positives of the convenience of these platforms, they are often used for propagating malicious data or information. This misinformation may misguide users and even have dangerous impact on society’s culture, economics, and healthcare. The propagation of this enormous amount of misinformation is difficult to counter. Hence, the spread of misinformation related to the COVID-19 pandemic, and its treatment and vaccination may lead to severe challenges for each country’s frontline workers. Therefore, it is essential to build an effective machine-learning (ML) misinformation-detection model for identifying the misinformation regarding COVID-19. In this paper, we propose three effective misinformation detection models. The proposed models are long short-term memory (LSTM) networks, which is a special type of RNN; a multichannel convolutional neural network (MC-CNN); and k-nearest neighbors (KNN). Simulations were conducted to evaluate the performance of the proposed models in terms of various evaluation metrics. The proposed models obtained superior results to those from the literature.

With the advancement of the Internet, many individuals and organizations have started to use it to store and send personal or business information. Some of this information is highly confidential and that, in turn, raises an issue of data privacy and confidentiality. Major advances in the Internet have also aided intruders in getting unauthorized access to confidential information. Information Security is the art of securing data from illegitimate access while ensuring integrity, availability, and confidentiality of data. Information Security is achieved through cryptographic encryption and decryption algorithms. Encryption hides confidential information by converting it to an unreadable form while the reverse process of retrieving data from the unreadable or encrypted form is known as decryption. Many cryptographic algorithms exist today and selecting which one to use depends on several factors and measures. This paper conducts a comparison of the encryption speed for five different cryptographic symmetric block-cipher algorithms: DES, 3DES, Blowfish, Twofish, and Threefish. The simulation is done using Python with various text file sizes. The results show that Blowfish outperforms the rest of the algorithms that were tested.

Today, the world is fighting against a dangerous epidemic caused by the novel coronavirus, also known as COVID-19. All have been impacted and countries are trying to recover from the social, economic, and health devastations of COVID-19. Recent epidemiology research has concentrated on using different prediction models to estimate the numbers of infected, recovered, and deceased cases around the world. This study is primarily focused on evaluating two common prediction models: Susceptible – Infected – Recovered (SIR) and Susceptible – Exposed – Infected – Recovered (SEIR). The SIR and SEIR models were compared in estimating the outbreak and identifying the better fitting model for forecasting future spread in Kuwait. Based on the results of the comparison, the SEIR model was selected for predicting COVID-19 infected, recovered, and cumulative cases. The data needed for estimation was collected from official sites of the Kuwait Government between 24 February and 1 December 2020. This study presents estimated values for peak dates and expected eradication of COVID-19 in Kuwait. The proposed estimation model is simulated using the Python Programming language on the collected data. The simulation was performed with various basic reproduction numbers (between 5.2 and 3), the initial exposed population, and the incubation rate. The results show that the SEIR model was better suited than the SIR model for predicting both infection and recovery cases with $R_0$ values ranging from 3 to 4, $E_0=80$ and $\alpha =0.2$.

The aim of this study was to retrospectively characterize E. coli and K. pneumoniae isolates obtained from neonates during a suspected NICU outbreak of infection in Ha’il, Saudi Arabia during a period of one month (April 2014). Antibiotic susceptibility patterns, molecular characterization for antibiotic-resistant genes (blaTEM, blaSHV, and blaCTX-M), and genotyping by PFGE and MLST were performed. A total of 24 E. coli and 48 K. pneumoniae isolates were cultured from neonates that had been admitted to the NICU. Among E. coli, the majority of isolates (19/24) were ESBL-positive and all of these nineteen (100%) harbored the CTX-M-15 gene. A total of 15% (3/19) were co-producers of CTX-M-15 and SHV-12, and 68.4% (13/19) were co-producers of CTX-M-15 and TEM-1. Among K. pneumoniae isolates, 87.5% (42/48) were ESBL positive with 92.85% (39/42) of these isolates containing the CTX-M-15 gene. A total of 97% (38/39) of K. pneumoniae were co-producers of CTX-M-15 and SHV-12, and 88% (37/42) were positive for TEM-1. Furthermore, 85.7% (36/42) K. pneumoniae were co-producers of CTX-M-15 and TEM-1. The majority of E. coli isolates (18/19 isolates) were grouped into two genetic clusters by pulsed field gel electrophoresis (PFGE) and all the isolates were found to be ST-131 type. In contrast, K. pneumoniae (31/42) isolates belonged to a single genotypic lineage, and all (100%) isolates belonged to the ST-14 type. This is the first report of CTX-M-15-positive, ESBL E. coli, and K. pneumoniae isolates recovered from an outbreak in an NICU in Ha’il, Saudi Arabia. It is alarming to note the high rate of outbreak isolates with simultaneous production of CTX-M-15 and SHV-12 conferring high-level resistance to oxyimino-cephalosporins.

Denial of service is a common attack in the Internet which causes significant problems for both users and service providers. Distributed attack sources can be used to enlarge the attack in case of distributed denial of service (DDoS). Defending against DoS/DDoS attacks generally involves 3 different phases: prevention, detection and response. Detection, the subject of this paper, is one of the key steps in defending against DoS/DDoS attacks as the proper response will be linked to the detection alarm. A good detection technique provides short detection time, low false positive rate, and low computational overhead. This paper presents a novel technique which detects TCP based flooding attacks by using the TCP congestion window which is analysed using the cumulative sum (CUSUM). Network Simulator (NS2) is used to validate the proposed technique.