The IIoT network involves smart sensors, actuators, and technologies extending IoT capabilities across industrial sectors. With the rapid development in connected technology and communications in industrial applications, IIoT networks and devices are increasingly integrated into less secure physical environments. Anomaly detection in IIoT is crucial for cybersecurity. This paper proposes a novel anomaly detection model for IIoT systems, leveraging a hybrid deep learning (DL) model. The hybrid DL approach combines Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) for anomaly detection in IoT edge computing. The proposed CNN+GRU model achieves a notable 94.94% accuracy, underscoring the importance of careful model selection for IIoT anomaly detection. The paper suggests exploring XGBoost with hybrid CNN+GRU architectures as a future direction for high accuracy in complex IIoT contexts. The Experimental results indicate a 96.41% accuracy, excelling in metrics like false alarm rate (FAR), recall, precision, and F1score. Based on these findings, we recommend future researchers consider advanced hybrid architectures and enhance efficiency using XGBoost with hybrid CNN+GRU. This approach holds promise for significant contributions to IIoT systems' security and Performance evolution.

Wireless Sensor Networks (WSN) are comprised of many sensing devices that can exchange data for monitoring and tracking. Data is exchanged over different paths where a few of malicious nodes may exist to capture the data. To handle this issue, several data security schemes have been presented that are discussed in the literature. In this paper, we perform a Systematic Literature Review (SLR) to analyse existing schemes for data security in WSN as per the research question. It involves an inclusion and exclusion criteria for article selection. In literature, we explore the energy efficient schemes in the category of elliptic curve, AES, RSA, chaotic maps, efficient block ciphers, and various other techniques. These schemes are also evaluated in terms of key sizes, plain text sizes, and dominating features of related techniques and results. We identified that most of the data security schemes are extensive due to extensive computations and delayed responses. This work focuses various types of data security approaches that are compared to examine which can be more suitable for secure data exchange.

Smart cities have emerged to tackle life critical challenges that can thwart the overwhelming urbanization process, such as expensive health care, increasing energy demand, traffic jams, and environmental pollution. This paper proposes efficient and high-quality cloud-based machine-learning solutions for safe urban smart city environment. For that, supervised MLbased models, i.e., regression and classification, are developed utilizing cloud-based solutions to forecast high performance in execution time and enhanced quality of the solution in terms of the accuracy of the implemented cloud-based ML solution. To predict AQI, i.e. air quality index, ML models utilize pollutants in the air data sets. The mean absolute error, mean squared error, root means the squared error, R2 score are used to validate and test the designed models. As classification models, we perform the support vector machine and random forest algorithms, which are measured using the accuracy score and confusion matrix. Execution times and accuracy of the developed models are computed and contrasted with the times for the cloud-based versions of these models. The results show that among the regression algorithms, lasso regression has an r2 score of 80 percent, while linear regression has an r2 score of 75 percent. Furthermore, among the classification models, the random forest algorithm performs better with an accuracy of 99 percent than the support vector machine approach with 95 percent accuracy. In conclusion, our findings demonstrate that run-time is minimized when models are executed on a cloud platform compared to a desktop machine. Moreover, the accuracy of our models is maintained with reduced execution time.

Unsupervised ML-based approaches have emerged for driving critical decisions about training data samples to help solve challenges in many life critical applications. This paper proposes parallel and distributed computing unsupervised ML techniques to improve the execution time of different ML algorithms. Various unsupervised ML models are developed, implemented, and tested to demonstrate the efficiency, in terms of execution time and accuracy, of the serial methods as compared to the parallelized ones. We developed sequential, parallel, and distributed cloud computing unsupervised ML models based and determined the most efficient model through comparative analysis. As a case study, sequential, parallel, and distributed approaches of Simple K-Means, Minibatch K-means, and Fuzzy C-Means are investigated to study the developed models' efficiency using country datasets for multiple organizations to train and test the developed model. Parallel and distributed computing models are developed utilizing could computing architect, i.e., cloud Amazon SageMaker, to study their efficiency in the execution time and model accuracy. The results show that the proposed parallel and distributed Fuzzy C-Means outperforms the other two clustering methods in terms of execution time with 0.932ms and 0.623ms with a minimal impact on the accuracy of the developed models.

Context-aware computing applications depend on embedded hardware systems, utilizing sensors embedded in the hardware to gather real-time data and interact with specialized OS software (firmware) for autonomous processing and analysis of intelligent data. Securing embedded IC hardware systems and ensuring their trustworthiness requires an intelligent approach to effectively detect spontaneous hardware Trojans (HTs) insertions and modification attacks aiming to compromise the system's integrity, potentially leaking sensitive information or causing destruction. Implementing robust and advanced intrusion detection systems against supply chain hardware Trojan to countermeasure and continuous monitoring the behavior of these malicious hardware is essential to enable trust in context-aware computing applications. AI-enabled hardware side-channel analysis, involving power and timing assessments, assists in detection of anomalies that may signify potential Trojans. This paper propose intelligent AI approach utilizing unsupervised machine learning in conjunction with hardware side-channel analysis to eliminate the need for golden data samples and efficiently detect hardware Trojan detection. Employing unsupervised clustering, the methodology not only showcased a superior false positive rate but also demonstrated a comparable accuracy level when compared to supervised counterparts such as the K-Nearest Neighbors (KNN) classifier, Support Vector Machine (SVM), and Gaussian classifier-methods reliant on the availability of golden data for training. Notably, the proposed model exhibited an impressive accuracy rate of 93%, particularly excelling in pinpointing diminutive Trojans triggered by concise events, surpassing the capabilities of preceding techniques. In conclusion, this research advances a groundbreaking paradigm in hardware Trojan detection, accentuating its potential in bolstering the integrity of semiconductor IC supply chains.

Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you. Abstract: Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results.

World Health Organization declared COVID-19 as a pandemic after the breakout in the city of Wuhan, China. The disease has negatively affected the global economy and daily life. Most of the countries around the world have imposed travel restrictions, locked down, and social distancing measures. In the current situation, Information and Communications Technology is playing a significant role in connecting people. Majority of the education organizations have adopted online platforms, students and staff are working from home. Besides, these businesses, e-healthcare systems, food deliveries, and online grocery shopping have witnessed a very high demand. Malicious attackers have considered COVID-19 as an opportunity to launch attacks for financial gains and to promote their evil intents. Healthcare systems are being attacked with ransomware and resources such as patient’s records confidentiality, and integrity is being compromised. People are falling prey to phishing attacks through COVID-19 related content. In this research, we have identified the top ten cybersecurity threats that had and could take place during the pandemic. We have also discussed the privacy concerns raised amid COVID-19.

No one predicted the corona virus outbreak, and naturally, nobody was prepared. The corona virus, otherwise, adoringly known as COVID-19, has put the entire world in a difficult position because of the apparent life-threatening danger it poses and the rate of spread and infection worldwide. Besides taking away so many lives, it has caused a variety of problems such as unemployment, social distancing, disruption of businesses and daily life. Circumstances are worse for those who live from hand to mouth. Within the three months of its presence, COVID-19 has forced humanity to find and implement alternative ways to sustain businesses and life. In this research paper, we have unearthed 7 lessons learned from the COVID-19 pandemic. These lessons include aspects of business, education, online presence, network communication, cyber security, healthcare, and the purpose of life. This research delves deeper into the response to the unannounced pandemic in hand. It aims to provide the right direction to address a potential pandemic in the future should it happen.

Novel Coronavirus (2019-nCoV) or Human Coronavirus (HCoV) could be a new coronavirus that belongs to Betacoronaviruses type Human Coronaviruses, just like the Middle-East Respiratory Syndrome (MERS) coronavirus and Severe Acute Respiratory Syndrome (SARS) coronavirus. Wuhan, the capital city of Hubei province, China recorded the initial cases of this virus in December 2019. Until 2 May 2020, 10:00 CET, near 23,8287 deaths were reported out of 3.34 million confirmed cases recorded across the world. By the tip of January 2020, China confirmed that the COVID-19 transmitted from human to human. This study aims to check a completely unique medicament called “Echinacea purpurea” against the crystal structure of 2019-nCoV Main Protease. This study presents an ideal model for Echinacea purpurea to be tested in silico against 2019-nCoV Main Protease.

COVID-19 has stunned the global economy and threatened human life. Due to rapidly emerging fatalities and enormous cases appearing every day, researchers across the globe are producing significant contributions to mitigate this pandemic. Besides the race for discovering a vaccine and treatment for COVID-19, there is utmost focus on flattening the curve by undertaking appropriate measures. The remarkable role of frontline medical practitioners, who are eagerly treating the affected people will be penned in the history books. The efforts of scientists and technologists will be remembered for their extraordinary contributions to assist healthcare professionals and governments in mitigating the threats of COVID-19. Leading technology firms have formed consortiums and research groups, which provide funding and free access to supercomputers for solving complex computational problems to eliminate COVID-19. In this research, we have unveiled five state-of-the-art technologies and their remarkable applications that can be used to mitigate and eliminate the problems of COVID-19. These technologies include Artificial Intelligence (AI), 3D Printing Technology (3DPT), Big Data Analytics (BDA), High Performance Computing (HPC) and Telecommunication Technology (TT). This research investigates the use of technology to encounter COVID-19 and aims to serve as the primary reference for promoting future research as well as developments to produce solutions for COVID-19 using AI, 3DPT, BDA, HPC and TT.