Analytical and Predictive Model for the impact of social distancing on COVID-19 pandemic

The coronavirus (COVID-19) as in the study of which had a starting point in China in 2019, has spread rapidly in every single country and has spread in millions of cases. The pandemic attracts lots of attentions due to major impacts not only on human health but on many other aspects including, social and political ones. This paper presents a robust data-driven machine learning analysis of COVID19 starting from data collection to the final step of knowledge extraction based on the selected research topics. The proposed approach evaluates the impact of social distancing on COVID19. Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore the social distancing aspects of COVID-19 pandemic. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. Also, it uses different Python libraries, Rattle, RStudio, Anaconda, and Jupyter Notebook. This study shows superior prediction performance comparing with the related approaches and the classical machine learning approaches. © 2022 IEEE.

New Waves of Cyber Attacks in the Time of COVID19

For hackers and many actors in the cyber world, crises can make opportunities. With the continuous spread of Covid-19 pandemics, new waves of cyber-attacks are witnessed. Attackers are taking advantage of possibly increasing vulnerabilities due to lack of awareness of best security practices when using online resources by many new users. While the data and network security specialist do their best to protect the cyber users, and Information Technology companies strive to look for threats countermeasures, in this chapter, we are presenting, revising, and analyzing different types of cyberattacks which might exist before, but evolved at the COVID-19 era. Moreover, we are addressing the new cyberattacks that flourished during COVID-19 pandemic from different prospective including their economical and social impact. Finally, we are discussing possible countermeasures to detect and prevent these attacks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Generating Optimal Attack Paths in Generative Adversarial Phishing

Phishing attacks have witnessed a rapid increase thanks to the matured social engineering techniques, COVID-19 pandemic, and recently adversarial deep learning techniques. Even though adversarial phishing attacks are recent, attackers are crafting such attacks by considering context, testing different attack paths, then selecting paths that can evade machine learning phishing detectors. This research proposes an approach that generates adversarial phishing attacks by finding optimal subsets of features that lead to higher evasion rate. We used feature engineering techniques such as Recursive Feature Elimination, Lasso, and Cancel Out to generate then test attack vectors that have higher potential to evade phishing detectors. We tested the evasion performance of each technique then classified different evasion tests as passed or failed depending on their evasion rate. Our findings showed that our threat model has better evasion capability compared to the original Generative Adversarial Deep Neural Network (GAN) which perturbs features in a random manner. © 2021 IEEE.

COVID-19 Candidate Treatments, a Data Analytics Approach

COVID-19, short for 'coronavirus disease 2019' has majorly affected millions of people worldwide. In the U.S. alone as of the end of this week (June 1, 2020), there have been 1,790,191 total cases, with 104,383 deaths. There have been 6,166,978 cases in the entire world, with 372,037 deaths, these are just the reported cases. Our focus in this research is in evaluating a repository of research papers to extract knowledge related to COVID-19 and possible treatments. Driven by the COVID-19 Open Research Dataset Challenge from Kaggle, we focused on a subset of that, COVID-19 Pulmonary Risks Literature Clustering. The second dataset we are using is from the Maryland Transportation Institute (MTI). The data is broken up into four categories: (1) Mobility and Social Distancing, (2) COVID and Health, (3) Economic Impact, and (4) Vulnerable Population. The data is extracted from NPR, ESRI, the COVID tracking project, CDC, and several other sources. MTI has been the source of several papers regarding mobility impact, social distancing, stay at-home orders, and non-pharmaceutical interventions. © 2020 IEEE.