Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis.

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.

Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis.

In today's digitalized era, the usage of Android devices is being extensively witnessed in various sectors. Cybercriminals inevitably adapt to new security technologies and utilize these platforms to exploit vulnerabilities for nefarious purposes, such as stealing users' sensitive and personal data. This may result in financial losses, discredit, ransomware, or the spreading of infectious malware and other catastrophic cyber-attacks. Due to the fact that ransomware encrypts user data and requests a ransom payment in exchange for the decryption key, it is one of the most devastating types of malicious software. The implications of ransomware attacks can range from a loss of essential data to a disruption of business operations and significant monetary damage. Artificial intelligence (AI)-based techniques, namely machine learning (ML), have proven to be notable in the detection of Android ransomware attacks. However, ensemble models and deep learning (DL) models have not been sufficiently explored. Therefore, in this study, we utilized ML- and DL-based techniques to build efficient, precise, and robust models for binary classification. A publicly available dataset from Kaggle consisting of 392,035 records with benign traffic and 10 different types of Android ransomware attacks was used to train and test the models. Two experiments were carried out. In experiment 1, all the features of the dataset were used. In experiment 2, only the best 19 features were used. The deployed models included a decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), ensemble of (DT, SVM, and KNN), feedforward neural network (FNN), and tabular attention network (TabNet). Overall, the experiments yielded excellent results. DT outperformed the others, with an accuracy of 97.24%, precision of 98.50%, and F1-score of 98.45%. Whereas, in terms of the highest recall, SVM achieved 100%. The acquired results were thoroughly discussed, in addition to addressing limitations and exploring potential directions for future work.

Increasing Uptake of COVID-19 Vaccination and Reducing Health Inequalities in Patients on Renal Replacement Therapy-Experience from a Single Tertiary Centre.

BACKGROUND: COVID-19 vaccination has changed the landscape of the COVID-19 pandemic; however, decreased uptake due to vaccine hesitancy has been observed, particularly in patients from minority ethnic backgrounds and socially deprived areas. These patient characteristics are common in patients on Renal Replacement Therapy (RRT), a population at extremely high risk of developing serious illness from COVID-19 and who would thus benefit the most from the vaccination programme. We designed a bespoke COVID-19 vaccination programme for our RRT population with the aim of decreasing health inequalities and increasing vaccination uptake. METHODS: Key interventions included addressing vaccine hesitancy by deploying the respective clinical teams as trusted messengers, prompt eligible patient identification and notification, the deployment of resources to optimise vaccine administration in a manner convenient to patients, and the timely collection and analysis of local safety and efficacy data. First, COVID-19 vaccination data in relation to ethnicity and social deprivation in our RRT population, measured by the multiple deprivation index, were analysed and compared to uptake data in the total regional adult clinically extremely vulnerable (CEV) population in Greater Manchester (GM). Univariate logistic regression analysis was used to explore the factors associated with not receiving a vaccine. RESULTS: Out of 1156 RRT patients included in this analysis, 96.7% received the first dose of the vaccination compared to 93% in the cohort of CEV patients in the GM. Age, gender, ethnicity, and a lower index of multiple deprivation were not identified as significant risk factors for poor first dose vaccine uptake in our cohort. Vaccine uptake in Asian and Black RRT patients was 94.9% and 92.3%, respectively, compared to 93% and 76.2% for the same ethnic groups in the reference CEV GM. Vaccine uptake was 96.1% for RRT patients in the lowest quartile of the multiple deprivation index, compared to 90.5% in the GM reference population. CONCLUSION: Bespoke COVID-19 vaccination programmes based on local clinical teams as trusted messengers can improve negative attitudes towards vaccination and reduce health inequalities.