This paper studies the cybersecurity issues that have occurred during the coronavirus (COVID‐19) pandemic. During the pandemic, cyber criminals and Advanced Persistent Threat (APT) groups have taken advantage of targeting vulnerable people and systems. This paper emphasizes that there is a correlation between the pandemic and the increase in cyber‐attacks targeting sectors that are vulnerable. In addition, the growth in anxiety and fear due to the pandemic is increasing the success rate of cyber‐attacks. We also highlight that healthcare organizations are one of the main victims of cyber‐attacks during the pandemic. The pandemic has also raised the issue of cybersecurity in relation to the new normal of expecting staff to work from home (WFH), the possibility of state‐sponsored attacks, and increases in phishing and ransomware. We have also provided various practical approaches to reduce the risks of cyber‐attacks while WFH including mitigation of security risks related to healthcare. It is crucial that healthcare organizations improve protecting their important data and assets by implementing a comprehensive approach to cybersecurity.

Abstract--The increased interconnectivity and complexity of Supervisory Control and Data Acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper we present a novel approach for a next generation SCADA-specific Intrusion Detection System (IDS). The proposed system analyses multiple attributes in order to provide a comprehensive solution able to mitigate varied cyberattacks threats. The multi-attribute IDS comprises a heterogeneous whitelist and behavior-based concept in order to make SCADA cyber systems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cyber-security in Smart Grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cyber-security test-bed to investigate simulated attacks and which has been used in the paper to validate the proposed approach.

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The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.

Software-Defined Networks (SDN) is an emerging area that promises to change the way we design, build, and operate network architecture. It tends to shift from traditional network architecture of proprietary based to open and programmable network architecture. However, this new innovative and improved technology also brings another security burden into the network architecture, with existing and emerging security threats. The network vulnerability has become more open to intruders: the focus is now shifted to a single point of failure where the central controller is a prime target. Therefore, integration of intrusion detection system (IDS) into the SDN architecture is essential to provide a network with attack countermeasure. The work designed and developed a virtual testbed that simulates the processes of the real network environment, where a star topology is created with hosts and servers connected to the OpenFlow OVS-switch. Signature-based Snort IDS is deployed for traffic monitoring and attack detection, by mirroring the traffic destine to the servers. The vulnerability assessment shows possible attacks threat exist in the network architecture and effectively contain by Snort IDS except for the few which the suggestion is made for possible mitigation. In order to provide scalable threat detection in the architecture, a flow-based IDS model is developed. A flow-based anomaly detection is implemented with machine learning to overcome the limitation of signature-based IDS. The results show positive improvement for detection of almost all the possible attacks in SDN environment with our pattern recognition of neural network for machine learning using our trained model with over 97% accuracy.