ABSTRACT
The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively. © 2022
ABSTRACT
Drug Discovery is a process by which new potential drugs are discovered and clinically trialed for commercial medicinal purposes. It has several stages of development, where each stage requires a prescribed time for its completion. The stages of drug development are discovery and development, pre-clinical research, clinical development, Food and Drug Administration (FDA) review, and post-market monitoring. The first three stages themselves take nearly 6.5 years. These stages take a huge time in cases where there is an urgent need for a drug. For example, during the COVID-19 pandemic, there was an urgent need for a vaccine. Many research institutes worked $24 \times 7$ to develop a vaccine, but it still took a considerable time to get to a bare minimum vaccine. To tackle this problem, we propose DuBloQ, a novel methodology for drug discovery using Q-Learning. Our Q-Learning model consists of a generator and a predictor model. The generator generates a set of Simplified Molecular Input Line Entry System (SMILES) strings and the Predictor predicts its logp values. Based on the logp values, the reward for the generator is provided to improve its performance. We integrate this model with a blockchain User Interface (UI) that ensures security and privacy. We achieved an accuracy of 76.1% for the generator model. © 2022 IEEE.
ABSTRACT
The fake news "infodemic", facilitated by social media and mobile message sharing platforms, has progressed from causing a nuisance to seriously impacting law and order through deliberate and large-scale manipulation of public sentiments. There are social, religious, political, and economic dimensions to the fake news phenomenon, providing enough motivation for interested parties to push biased opinions, claims, conspiracies and fraud to many naive information consumers. The ease with which fake news can be created and propagated makes it extremely challenging to detect and mitigate. To combat the fake news, the researchers have utilized mechanisms which are largely based on Artificial Intelligence (AI) algorithms and social network analysis. However, no viable solution has yet been deployed at a scale. This paper present a comprehensive survey on combating fake news and evaluates the challenges involved in its detection with the help of existing detection mechanisms and techniques to control its spread. The challenges associated with combating fake news have been addressed based on the various aspects such as psychological, economic, and technical. Furthermore, we consider the fake news combat spectrum to analyze the stakeholder interventions due to the spread of fake news. Finally, various technology-based solutions have been presented for combating fake news and the associated future challenges and opportunities.
ABSTRACT
The emergence of wearable technology for assessing health data has revolutionized the health sector. Consequently, medical practitioners can now virtually examine the patient's health and provide immediate medications. However, when the security of this equipment is considered, there is a grave hazard. The data is delivered across an open channel, i.e., the internet, from the patient's device to the doctor, and it may be tampered with by intruders. Insurance firms keep a record of their patient's health and subsequently offer appropriate treatments. In case of data tampering, the insurer will consider the conduct fraudulent. As a result, ensuring the system's integrity and granting access only to authorized stakeholders becomes critical. Blockchain has surpassed conventional technologies in terms of guaranteeing security for information held. Motivated by these, this paper has developed a novel approach that uses blockchain technology to transmit a patient's health information to a medical expert. Machine Learning (ML) techniques, K-Nearest Neighbours (KNN), and Logistic Regression (LR) is used to categorize Coronavirus Disease (COVID) positive users and malevolent wearable devices, respectively. The performance of the proposed model is evaluated considering parameters such as accuracy, precision, recall, and F1 score. The proposed model achieves an accuracy of 98.15% for COVID positive detection and 96.78% for malevolent user detection. © 2022 IEEE.
ABSTRACT
Due to COVID-19, engineering education has moved to an exclusive online mode, imposing various challenges for students and teachers. Many activities and methods have been introduced to improve teaching and increase students' engagement in online education. In this work, a trajectory-based pedagogy was used to enhance the associative learning and it was integrated with a trajectory based adaptive assessment to evaluate the students learning in the Power Plant Instrumentation Course of seventh semester. Evaluation was done in two modes;as a regular IRT mode, where students scored on their performance against individual question, and in trajectory-based assessment, students scored on the performance of previous question. For both assessments, scores were calculated as 'Learning coefficients.' Analysis of these learning coefficients demonstrated that in absence of innovative teaching pedagogy and assessment techniques, students may score good grades, but these are insufficient to measure the Course Learning outcomes. It is important to integrate new teaching pedagogies with complimenting assessments tools as a summative assessment to measure the Course Learning outcomes quantitatively during continuous evaluation conducted throughout the semester. This provides a robust method of assessment of students' knowledge in exclusive online education. © 2022 IEEE.
ABSTRACT
Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days’prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations. Author
ABSTRACT
An Artificial Intelligence (AI)-enabled and blockchain-driven Electronic Health Record (EHR) maintenance system has a tremendous potential to facilitate reliable, secure, and robust storage systems for EHRs. Such an EHR system would also facilitate researchers, doctors, and government authorities to access data for research, perform analytics, and help in making well-informed decisions. The Artificial Neural Network (ANN) is employed to classify the patients as potentially COVID-19 positive and potentially COVID-19 negative based on the clinical reports and reports of CT-scan. The data of potentially COVID-19 positive patients is stored on blockchain employing InterPlanetary File System (IPFS) protocol. The accessibility of EHR can be done by authorized entities post verification and validation of entities. We analyze the performance of various AI-based algorithms employing metrics such as loss curve, accuracy, etc. for the task of predicting the patient's potential COVID-19 infection. The 6G network significantly mitigates the network latency and reliability issues and also facilitates the real-time transmission of information. The amount of data generated is pretty high amidst this pandemic and so we employed IPFS protocol which suffices to be a cost-effective solution, moreover satisfying all are stringent requirements. At last, we evaluate the network, security, and storage performance of our architecture MedBlock, which outperformed other state-of-the-art systems.
ABSTRACT
The COVID-19 pandemic has adversely affected the lives of millions of people worldwide. With an alarming increase in COVID-19 cases, it is important to detect and diagnose COVID-19 in its early stages to prevent its spread. To diagnose remote patients, the Internet can be useful for accessing data of that patient. But, the Internet has also had issues related to data security, reliability, and privacy. Motivated by these challenges, in this paper, we propose a Blockchain (BC) based COVID-19 detection scheme (BCovX) for fast and reliable diagnosis of COVID-19 using chest X-Ray (CXR) images. For fast and accurate detection of COVID-19 using CXR, BCovX consists of a Convolutional Neural Network (CNN) model, using which a patient can be diagnosed for COVID-19 remotely. CNNs have performed successfully in medical imaging classification. BCovX provides reliable and secure data access and exchange using BC and smart contracts (SC). To solve issues related to data storage and its associated cost, the InterPlanetary File System (IPFS) protocol is used to store medical data. We also present a real-time SC developed in Solidity to govern the transaction between the patient and the doctor. The SC has been compiled and deployed on Remix Integrated Development Environment (IDE). Finally, we have evaluated the performance of BCovX with traditional schemes in terms of storage cost, bandwidth requirements, and accuracy of the CNN model.
ABSTRACT
CCybersecurity primarily deals with a clear interpretation of various issues related to attacks that occur on digital resources and subsequent remedies therein. While discussing the safety against attacks or crimes against digital resources, the confidentiality, integrity, and availability of such information must be preserved. The COVID-19 pandemic showed us that most human activities such as education, work in various fields, shopping, and communication shifted to online quickly. Therefore, the cybersecurity of computers, networks, and communication systems is critical. Cybersecurity is a challenging and rapidly evolving domain, and it is essential to stay up to date and use the latest protection technologies. This chapter introduces the basic cybersecurity concepts and terminology. The domains and the threats and actors are characterized extensively. Also, this chapter tries to explain the need for cybersecurity in the context of digitalization and increasing Internet usage. A short book outline is also presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Nowadays, mission-critical applications need delay-free responses, which can be achieved by computation at the edge devices known as edge computing. But edge computing needs cloud computing services to perform massive intelligent tasks like AI-based prediction and analysis, which still possesses high latency in making intelligent decisions. This can be resolved by bringing intelligence at the edge device or edge server, which introduces complex problems either at the proximity of edge devices, that is, consumer electronic devices (CED), or at the CED by bringing intelligence at the edge. The process of bringing intelligence to the edge is called edge intelligence (EI). The computation at edge servers is susceptible to various security and privacy issues and possesses high latency due to data propagation from the device to the dedicated edge server. To overcome the aforementioned issues, this study presents a blockchain-based edge intelligence system to ensure the CEDs' data security, privacy, latency, and efficiency. The proposed system uses public and private blockchains to fulfil the gaps mentioned above of the traditional systems. The public blockchain ensures CEDs' data communication privacy security, whereas private blockchain ensures secure communication among the EI servers. Then, we present the use case scenario of blockchain and edge intelligence (EI) in the COVID-19 pandemic and evaluate its performance over computation cost by comparing the intelligence at CED with the intelligence at the edge server and centralized cloud server (CCS). IEEE
ABSTRACT
To study fungal infections such as Mucormycosis, Aspergillosis, Candidiasis, Cryptococcosis associated with Covid-19. A detailed study was done with the information gathered from the articles in specified databases, online sources, and online published materials to have current details of the situation of fungal infections in covid patients. Fungal infections were seen among covid-19 patients mostly due to opportunistic fungal pathogens such as Mucor, Candida, Aspergillus, and Cryptococcus. The reason behind rising opportunistic fungal infections among covid-19 patients may be the immunocompromised host. The most common species responsible for fungal infections in covid-19 were noticed to be of genus Mucor, A. flavus, and A. fumigatus species of genus Aspergillus, C. albicans species of genus Candida, C. neoformans, and C. gattii species of genus Cryptococcus. Patients suffering or recovered from covid-19 are now facing numerous Secondary Infections. The majority of secondary infections associated with covid-19 are Fungal Infections. Mucormycosis, candidiasis, aspergillosis, cryptococcosis as opportunistic infections are seen widely in the covid-19 treated patients. Rapid progression of such fungal infections is required to be controlled by early diagnosis of infection and by identifying the underlying risk parameters. Protocols for disease management will be beneficial too.
ABSTRACT
The COVID-19 pandemic situation has proved to be disastrous for humanity throughout the world. However, during this period, people must take precautions for safety purposes. One of the essential steps towards eliminating or reducing the effect of COVID-19 is maintaining social distancing while in public places. Some people are neglecting the social distancing norms while on the move. Still, no surveillance system exists, which monitors the people’s movement for social distancing and securely &efficiently shares the information with the concerned administration department. There also exists no penalty system which forces the people to ensure social distancing. Motivated from the aforementioned facts, in this paper, we present a blockchain and artificial intelligence (AI)-envisioned scheme for monitoring social distancing to combat COVID-19 situations. The proposed scheme uses fast region-based convolutional neural networks (RCNN) and you only look once (YOLO) models for the object (i.e., human) detection through the live video feed captured from the static CCTV cameras as well as lens-equipped drones. Further, the efficient euclidean distance calculation is embedded for calculating the distance between two humans. Blockchain technology ensures the secure and trusted exchange of information between the entities at the physical layer and the administration departments. Blockchain wallets are also used to pay the fine when people do not follow social distance norms. The performance of the proposed scheme is evaluated based on three broad parameters such as (i) human detection and violation identification, (ii) blockchain simulation and analysis, and (iii) network performance comparison. The parameters considered for (i) is confidence score, for (ii) are scalability, hash rate, and simulation interface, and for (iii) are network bandwidth, throughput, packet loss rate, and network latency. By analyzing all the parameters mentioned above, we observe the proposed scheme outperforms the traditional approaches. Author
ABSTRACT
Technologies play an essential role in mitigating the physical human need and replacing this with robots (bots). Hence reducing social involvement results in a reduction in COVID-19 patients. This proofs to be safe for the human generation and humanity too. The technological aspects of cloud computing resource management and bots can be used for the management and security of the patient’s data and incorporating intelligent decision support in case of the massive reporting of the patients. As the healthcare sector continues to offer life-critical services while working to improve treatment and patient care with new technologies, criminals and cyber threat actors look to exploit the vulnerabilities that are coupled with this expertise. Healthcare organizations collect and store vast amounts of personal information, making them a primary target for cyber-criminals. In this paper, we will explore and discover the security implications and privacy issues of these health care technologies related to the management of patient’s data. We also describe various security breaches in medical data and used a framework called as C2B-SCHMS which usages machine learning-based isolation graph for handling anomaly. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
A virus spread from China to all around the world named COVID-19 has now become a demon. The fear of death can be easily seen in citizens of around 180 countries and fear to force us indoors. This is a demon of the twenty-first century;typically, this demon does not link with any of the evil, occultism, literature, fiction, mythology, and folklore. COVID-19 is a member of the coronavirus family and caused by the SARS-CoV-2 virus. COVID-19 was first identified in December 2019 at Wuhan, China. SARS virus is responsible for respiratory illness known as COVID-19. We have limited articles on COVID-19 with machine learning (ML) and AI. We do not have any antivirus medicine and other dataset that bring in mind about prediction, detection, and stage identification of COVID-19 in human bodies. Therefore, we decided to bring a machine learning-based technique with a list of datasets that will apply to coronavirus dataset for identification. It is believed that ML and artificial intelligence can help accelerate solutions for predicting the stage of infection. Data analysis presented in this paper helps in minimizing the virus impact with all the other research. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Online teaching has become mandatory across the globe during the COVID-19 pandemic situations. Hence, there is a need to uplift the online teaching technology for data privacy preservation and transparency in the system. Various online teaching schemes have been proposed by various authors, but they lack in handling decentralised governance, transparency, trust and communication issues. Blockchain (BC) Technology has emerged to provide decentralised solution in solving the real-time problems. Motivated by these facts, in this paper, we propose a BC-based decentralised online teaching scheme known as BDoTs. The security and data privacy issues in BDoTs are resolved by developing smart contracts (SCs) over BC. Moreover, the data storage cost issues are handled by the Inter Planetary File System (IPFS) protocol for Off-Chain data storage. Moreover, we present a real-time BC simulation and deployment of SC in Truffle suite. Results show that the proposed scheme performs better in comparison to the state-of-the-art schemes in terms of scalability, data storage cost, and packet loss. © 2020 IEEE.
ABSTRACT
With the spread of novel coronavirus, global health concerns have increased as it has flattened the curve of mortality worldwide. To handle such a containment of disease, multi-swarm Unmanned Aerial Vehicles (UAVs) with 5G can be used to reduce human intervention with major benefits of high bandwidth, ultra-low-latency, and reliability. Multi-swarm UAVs sends a huge amount of data to ground stations with real-time connection density of 107/km2, which is a bottleneck on 5G networks;data security is another issue in sharing sensitive data. Motivated by these issues, in this article, we propose a blockchain-envisioned softwarized multi-swarming UAV communication scheme based on a 6G network with intelligent connectivity, Terahertz (THz) frequency bands, and virtualization of link and physical-level protocols. Softwarization makes the communication infrastructure flexible, agile, and easily configurable, and the potential of blockchain supports data security. Results show that the proposed scheme performs better in terms of processing delay, packet loss reduction, and throughput compared to exiting 4G/5G-based systems. IEEE