This systematic literature review explores the digital transformation (DT) and cybersecurity implications for achieving business resilience. DT involves transitioning organizational processes to IT solutions, which can result in significant changes across various aspects of an organization. However, emerging technologies such as artificial intelligence, big data and analytics, blockchain, and cloud computing drive digital transformation worldwide while increasing cybersecurity risks for businesses undergoing this process. This literature survey article highlights the importance of comprehensive knowledge of cybersecurity threats during DT implementation to prevent interruptions due to malicious activities or unauthorized access by attackers aiming at sensitive information alteration, destruction, or extortion from users. Cybersecurity is essential to DT as it protects digital assets from cyber threats. We conducted a systematic literature review using the PRISMA methodology in this research. Our literature review found that DT has increased efficiency and productivity but poses new challenges related to cybersecurity risks, such as data breaches and cyber-attacks. We conclude by discussing future vulnerabilities associated with DT implementation and provide recommendations on how organizations can mitigate these risks through effective cybersecurity measures. The paper recommends a staged cybersecurity readiness framework for business organizations to be prepared to pursue digital transformation.

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox).

Smart cities assure the masses a higher quality of life through digital interconnectivity, leading to increased efficiency and accessibility in cities. In addition, a huge amount of data is being exchanged through smart devices, networks, cloud infrastructure, big data analysis and Internet of Things (IoT) applications in the various private and public sectors, such as critical infrastructures, financial sectors, healthcare, and Small and Medium Enterprises (SMEs). However, these sectors require maintaining certain security mechanisms to ensure the confidentiality and integrity of personal and critical information. However, unfortunately, organizations fail to maintain their security posture in terms of security mechanisms and controls, which leads to data breach incidents either intentionally or inadvertently due to the vulnerabilities in their information management systems that either malicious insiders or attackers exploit. In this paper, we highlight the importance of data breaches and issues related to information leakage incidents. In particular, the impact of data breaching incidents and the reasons contributing to such incidents affect the citizens’ well-being. In addition, this paper also discusses various preventive measures such as security mechanisms, laws, standards, procedures, and best practices, including follow-up mitigation strategies.

Preeclampsia is one of the illnesses associated with placental dysfunction and pregnancy-induced hypertension, which appears after the first 20 weeks of pregnancy and is marked by proteinuria and hypertension. It can affect pregnant women and limit fetal growth, resulting in low birth weights, a risk factor for neonatal mortality. Approximately 10% of pregnancies worldwide are affected by hypertensive disorders during pregnancy. In this review, we discuss the machine learning and deep learning methods for preeclampsia prediction that were published between 2018 and 2022. Many models have been created using a variety of data types, including demographic and clinical data. We determined the techniques that successfully predicted preeclampsia. The methods that were used the most are random forest, support vector machine, and artificial neural network (ANN). In addition, the prospects and challenges in preeclampsia prediction are discussed to boost the research on artificial intelligence systems, allowing academics and practitioners to improve their methods and advance automated prediction.

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients’ need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.