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Internet of Things (IoT) and sensor devices have been connected due to the development of the IoT and Information Communication Technology (ICT). It offers automatic environments in smart city and IoT scenarios and describes investments in advanced resources in futuristic human lives as sustainable growth of quality-wise life with intelligent infrastructure. Nowadays, IoT devices are continuously increasing and utilized in advanced IoT applications, including Smart Homes, Smart Farming, Smart Enterprises, and others. However, security and privacy are significant challenges with Ransomware-based Cyber-attack detection in IoT due to the lack of security design and heterogeneity of IoT devices. In the last few years, various advanced paradigms and technologies have been utilized to mitigate the security issues with Ransomware attack detection in IoT devices and data. This paper comprehensively surveys Ransomware-based Cyber Attacks and discusses solutions based on advanced technologies such as Artificial Intelligence (AI), Blockchain, and Software Defined Networks (SDN). Then, we design service scenarios for ransomware-based cyber-attack detection. Finally, we summarize the open research challenges and future directions for ransomware in IoT. &nbsp

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

Nowadays, Digital Twins (DTs) are being integrated into various sectors thanks to the continuous progress of computing power and data science. We are surrounded by a tremendous number of sensors and connected objects that produce data regularly. These data represent the fuel for a DT as they are used to represent the most accurate digital model of a system or an object and to predict and simulate all the possible scenarios. Recently, DT has been adapted into the healthcare sector as well for an accurate medical and surgery simulation and medical resources’ orchestration. However, DT technology is still a novice to the healthcare system and security threats urge immediate consideration. To this end, we propose in this paper a Blockchain-based secure Digital Twin framework for a smart healthy city. We discuss as a case study the current COVID-19 pandemic and argue on the help of DT to control the situation, prevent future cases, and personalize the treatment.

Nowadays, the world is experiencing a pandemic crisis due to the spread of COVID-19, a novel coronavirus disease. The contamination rate and death cases are expeditiously increasing. Simultaneously, people are no longer relying on traditional news channels to enlighten themselves about the epidemic situation. Alternately, smart cities citizens are relying more on Social Network Service (SNS) to follow the latest news and information regarding the outbreak, share their opinions, and express their feelings and symptoms. In this paper, we propose an SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Sustainable Healthy City, where Twitter platform is adopted. Over 10000 Tweets were collected during two months, 38% of users aged between 18 and 29, while 26% are between 30 and 49 years old. 56% of them are males and 44% are females. The geospatial location is USA, and the used language is English. Natural Language Processing (NLP) is deployed to filter the tweets. Results demonstrated an outbreak cluster predicted seven days earlier than the confirmed cases with an indicator of 0.989. Analyzing data from SNS platforms enabled predicting future outbreaks several days earlier, and scientifically reduce the infection rate in a smart sustainable healthy city environment.

The service-based architecture of the Fifth Generation(5G) had combined the services and security architectures and enhanced the authentication process of services to expand the coverage of the network, including heterogeneous devices. This architecture uses the secondary authentication for mutual authentication between the User Equipment (UE) and the Data Network (DN) to authenticate devices and services. However, this authentication mechanism can cause a signaling storm in the Non-Access Stratum (NAS) because the end node needs to communicate with the authentication server of the NAS area. This problem could affect the availability of the network when the network is extended. This research proposes a mutual authentication framework that can efficiently perform a mutual authentication process through secondary authentication between UE and DN. The proposed framework uses newly devised network functions: Secondary Authentication Function (SAF) and the Authentication Data Management Function (ADMF). This framework proposes a methodology at the protocol level for efficient mutual authentication using the mobile edge computing architecture. We analyzed the proposed framework in the point of security considerations, and we evaluated the effect of the framework on the traffic of the NAS layer and user experience. Our simulation results show that the proposed framework can reduce the NAS traffic by 39% and total traffic of the overall network by 10%.