With confusion and uncertainty ruling the world, 2020 created near-perfect conditions for cybercriminals. As businesses virtually eliminated in-person experiences, the COVID-19 pandemic changed the way we live and caused a mass migration to digital platforms. However, this shift also made people more vulnerable to cyber-crime. Victims are being targeted by attackers for their credentials or financial rewards, or both. This is because the Internet itself is inherently difficult to secure, and the attackers can code in a way that exploits its flaws. Once the attackers gain root access to the devices, they have complete control and can do whatever they want. Consequently, taking advantage of highly unprecedented circumstances created by the Covid-19 event, cybercriminals launched massive phishing, malware, identity theft, and ransomware attacks. Therefore, if we wish to save people from these frauds in times when millions have already been tipped into poverty and the rest are trying hard to sustain, it is imperative to curb these attacks and attackers. This paper analyses the impact of Covid-19 on various cyber-security related aspects and sketches out the timeline of Covid-19 themed cyber-attacks launched globally to identify the modus operandi of the attackers and the impact of attacks. It also offers a thoroughly researched set of mitigation strategies which can be employed to prevent the attacks in the first place. Moreover, this manuscript proposes a fuzzy logic and data mining-based intelligence system for detecting Covid-19 themed malicious URL/phishing attacks. The performance of the system has been evaluated against various malicious/phishing URLs, and it was observed that the proposed system is a viable solution to this problem.
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.
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