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With the emergence of one of this century's deadliest pandemics, coronavirus disease (COVID-19) has an enormous effect globally with a quick spread worldwide. This made the World Health Organization announce it as a pandemic. COVID-19 has pushed countries to follow new behaviors such as social distancing, hand washing, and remote work and to shut down organizations, businesses, and airports. At the same time, white hats are doing their best to accommodate the pandemic. However, while white hats are protecting people, black hats are taking advantage of the situation, which creates a cybersecurity pandemic on the other hand. This paper discusses the cybersecurity issues at this period due to finding information or finding another related research that had not been discussed before. This paper presents the cybersecurity attacks during the COVID-19 epidemic time. A lot of information has been collected from the World Health Organization (WHO), trusted organizations, news sources, official governmental reports, and available research articles. This paper then classifies the cybersecurity attacks and threats at the period of COVID-19 and provides recommendations and countermeasures for each type. This paper surveys the cybersecurity attacks and their countermeasures and reports the ongoing cybersecurity attacks and threats at this period of time. Moreover, it is also a step towards analyzing the efficiency of the country's infrastructure as well as hackers and criminals' social behavior at the time of the pandemic., Author(s): Rabie A. Ramadan (corresponding author) [1,2]; Bassam W. Aboshosha [3]; Jalawi Sulaiman Alshudukhi [1]; Abdullah J. Alzahrani [1]; Ayman El-Sayed [4]; Mohamed M. Dessouky [4,5] 1. Introduction Humanity has [...]

The Internet of Things (IoT) has grown more pervasive in recent years. It makes it possible to describe the physical world in detail and interact with it in several different ways. Consequently, IoT has the potential to be involved in many different applications, including healthcare, supply chain, logistics, and the automotive sector. IoT-based smart healthcare systems have significantly increased the value of organizations that rely heavily on IoT infrastructures and solutions. In fact, with the recent COVID-19 pandemic, IoT played an important role in combating diseases. However, IoT devices are tiny, with limited capabilities. Therefore, IoT systems lack encryption, insufficient privacy protection, and subject to many attacks. Accordingly, IoT healthcare systems are extremely vulnerable to several security flaws that might result in more accurate, quick, and precise diagnoses. On the other hand, blockchain technology has been proven to be effective in many critical applications. Blockchain technology combined with IoT can greatly improve the healthcare industry's efficiency, security, and transparency while opening new commercial choices. This paper is an extension of the current effort in the IoT smart healthcare systems. It has three main contributions, as follows: (1) it proposes a smart unsupervised medical clinic without medical staff interventions. It tries to provide safe and fast services confronting the pandemic without exposing medical staff to danger. (2) It proposes a deep learning algorithm for COVID-19 detection-based X-ray images; it utilizes the transfer learning (ResNet152) model. (3) The paper also presents a novel blockchain-based pharmaceutical system. The proposed algorithms and systems have proven to be effective and secure enough to be used in the healthcare environment. [ABSTRACT FROM AUTHOR]

The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer’s disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques. [ABSTRACT FROM AUTHOR]