Detecting COVID-19-Related Fake News Using Feature Extraction.

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

Ransomware: Recent advances, analysis, challenges and future research directions.

The COVID-19 pandemic has witnessed a huge surge in the number of ransomware attacks. Different institutions such as healthcare, financial, and government have been targeted. There can be numerous reasons for such a sudden rise in attacks, but it appears working remotely in home-based environments (which is less secure compared to traditional institutional networks) could be one of the reasons. Cybercriminals are constantly exploring different approaches like social engineering attacks, such as phishing attacks, to spread ransomware. Hence, in this paper, we explored recent advances in ransomware prevention and detection and highlighted future research challenges and directions. We also carried out an analysis of a few popular ransomware samples and developed our own experimental ransomware, AESthetic, that was able to evade detection against eight popular antivirus programs.

Have You Been a Victim of COVID-19-Related Cyber Incidents? Survey, Taxonomy, and Mitigation Strategies.

Cybercriminals are constantly on the lookout for new attack vectors, and the recent COVID-19 pandemic is no exception. For example, social distancing measures have resulted in travel bans, lockdowns, and stay-at-home orders, consequently increasing the reliance on information and communications technologies, such as Zoom. Cybercriminals have also attempted to exploit the pandemic to facilitate a broad range of malicious activities, such as attempting to take over videoconferencing platforms used in online meetings/educational activities, information theft, and other fraudulent activities. This study briefly reviews some of the malicious cyber activities associated with COVID-19 and the potential mitigation solutions. We also propose an attack taxonomy, which (optimistically) will help guide future risk management and mitigation responses.