ABSTRACT
Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage. [ FROM AUTHOR] Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. Methods: We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. Results: The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. Discussion: The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines.
ABSTRACT
Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage. [ FROM AUTHOR] Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters.
ABSTRACT
This chapter presents the fruit of our research by merging smart transportation and smart health to provide IoT transportation solutions based on green smart city intelligence and safety to fight against the COVID-19 pandemic. For this, we have realized a model that allows transporting citizens via a means of transportation based on electric mobility to reduce energy consumption, reduce CO 2 emission and cost by searching the optimal path that this vehicle will be used. And to transport people, we need a system that allows us to check the health situation of citizens to avoid and prevent the spread of the COVID-19 pandemic. In order to find solutions to this work, we have proposed approaches to calculate the most optimal path that meets our needs, as well as to propose scenarios that allow checking the situation of the citizens. And to complete this work with minimum consumption of memory and time, we made a comparative study on the nodes used for the different IoT network topologies to choose the best one for our platform. Concerning the communication, we chose to use the CoAP protocol to ensure the communication between the nodes, and we used the AES-SHA256 encryption algorithm to compare it with RSA-SHA256 to ensure the elements of security and protection the data from any intrusion. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Our work revolves around creating and developing a medical platform that acts on the citizen’s right to access a public space by differentiating between vaccinated and those who have done the PCR test to avoid the fast spread of the covid-19 virus. In this scope, three algorithms were applied to complete this platform: the first one serves to verify the citizens who are vaccinated, the second allows to see and check the validity of the PCR test for people who are not vaccinated, and, finally, the third algorithm serves to check the temperature and the identity of citizens. This last algorithm is applied when the citizen is not vaccinated or does not have the PCR test. For this project, we used four IoT nodes to run our platform in real time: a Passive infrared (PIR) client node, a temperature sensor, a RFID tag, and a camera client node. We also used artificial intelligence for facial recognition, which will allow us to verify the citizen’s identity. We applied the Constrained Application Protocol to ensure a fluid communication between the platform’s nodes, the integrity, and the confidentiality of the data shared. We secured payloads with two encryption algorithms which are Advanced Encryption Standard (AES)-Secure Hash Algorithm (SHA) 256-bit and Rivest Shamir Adleman (RSA)-SHA256. The security of the database’s information is also an important aspect;thus, the use of the AT2 blockchain will allow us to strengthen the security of the database against any network attack. As for the network topology, we have opted for the cluster-tree topology, and that is after a study that showed us it is the best in terms of execution time, memory occupation, and energy consumption and even for the reliability of the communication. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.