An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.

A survey on security challenges in cloud computing: issues, threats, and solutions

Intelligent systems are wanting for cities to cope with limited spaces and resources across the world. As a result, smart cities emerged mainly as a result of highly innovative ICT industries and markets, and additionally, they have started to use novel solutions taking advantage of the Internet of Things (IoT), big data and cloud computing technologies to establish a profound connection between each component and layer of a city. Several key technologies congregate to build a working smart city considering human requirements. Even though the smart city concept is an advanced solution for today’s cities, recently, more living spaces should be discovered, and the concept of a smart city could be moved to these alternative living spaces, namely floating cities. The concept of a floating city emerged as a novel solution due to rising sea levels and land scarcity in order to provide alternative living spaces for humanity. In this article, our main research question is to raise awareness on the current state of smart city concepts across the world by understanding the key future trends, including floating cities, by motivating researchers and scientists through new IoT technologies and applications. Therefore, we present a survey of smart city initiatives and analyze their key concepts and different data management techniques. We performed a detailed literature survey and review by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living. We also discuss multiple perspectives of smart floating cities in detail. With the proposed approach, recent advances and practical future opportunities for smart cities can be revealed.

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Future Trends and Current State of Smart City Concepts: A Survey

On cloud security requirements, threats, vulnerabilities and countermeasures: A survey

Fifth generation (5G) networks are the base communication technology for connecting objects in the Internet of Things (IoT) environment. 5G is being developed to provide extremely large capacity, robust integrity, high bandwidth, and low latency. With the development and innovating new techniques for 5G-IoT, it surely will drive to new enormous security and privacy challenges. Consequently, secure techniques for data transmissions will be needed as the basis for 5G-IoT technology to address these arising challenges. Therefore, various traditional security mechanisms are provided for 5G-IoT technologies and most of them are built on mathematical foundations. With the growth of quantum technologies, traditional cryptographic techniques may be compromised due to their mathematical computation based construction. Quantum walks (QWs) is a universal quantum computational model, which possesses inherent cryptographic features that can be utilized to build efficient cryptographic mechanisms. In this paper, we use the features of quantum walk to construct a new S-box method which plays a significant role in block cipher techniques for 5G-IoT technologies. As an application of the presented S-box mechanism and controlled alternate quantum walks (CAQWs) for 5G-IoT technologies a new robust video encryption mechanism is proposed. As well as to fulfill needs of encryption for varied files in 5G-IoT, we utilize the features of quantum walk to propose a novel encryption strategy for secure transmission of sensitive files in 5G-IoT paradigm. The analyses and results of the proposed cryptosystems show that it has better security properties and efficacy in terms of cryptographic performance.

Secure Data Encryption Based on Quantum Walks for 5G Internet of Things Scenario

Recent security challenges in cloud computing

The proposed effort has the purpose of predicting an individuals insurance expenses also identifying people having medical insurance plans and clinical data, irrespective of their health concerns. A patient will require many types of health insurance. Regardless of the type of insurance coverage a person has, it is feasible to estimate their health insurance expenditures depends on the degree of critical care they get. The  random forest  Regression is one of the regressors used in this investigation. When the accuracies were compared, hyper parameter tuning was the most effective of all the approaches, with a 98 percent accuracy. Finally, the prediction fit will calculate the insurance expense of the user and calculate the insurance costs.

https://sciencescholar.us/journal/index.php/ijhs/article/download/11762/8024

Random forest regression with hyper parameter tuning for medical insurance premium prediction

Video Surveillance plays an important role in every aspect of life like theft detection, unusual happenings in crowded places, monitoring the suspicious activities of each individual to provide a secure and hassle free environment. Footage of closed circuit television (CCTV) camera is taken as an evidence to track the suspicious act. It is very tough to operate surveillance cameras with human intervention to detect abnormal activities. Fully automating surveillance with smart video capturing capabilities using deep learning technique is one of the most advanced means of remotely monitoring strange activities with exact location, time of event occurred along with facial recognition of criminal. Finding misdemeanor activity in a public place is very difficult to observe, as many objects are involved in the real time scenario. An uncommon or doubtful incidents in public places are captured in CCTV cameras which promotes police force to safeguard people before any mishap happens. It helps police to reach that spot on time and rescue victim. All these are meant to be achieved by using YOLO (You Only Look Once) object detection models and its variants like YOLO V1, V2, V3, V4 and latest V5 which is 88% faster than yolov4 in Deep Learning. This proposed system helps in identifying weapons held by a person as well as face recognition to identify the suspicious user. Using YOLO v5, it is very simple to track objects like weapons in a crowd. Low resolution images, far away and out of focus in the scene can also be captured and identified accurately

Smart Video Survillance Based Weapon Identification Using Yolov5

Security is one of the important challenges faced by cloud computing. There are so many methods like encryption, firewall, and providing access control to achieve security. These methods alone cannot provide a flawless access to the available resources. We are in need of a paradigm that permits only authenticated users to access the resources from the cloud. The objective of the proposed paradigm is to provide a strong authentication. Our proposed method considers only the appropriate parameters from the available plenty of parameters, which is used to prove a user to be an authenticated person. The selection of the required parameters is achieved using Principle Component Analysis, then Support Vector Machine classifies accurately the authenticated and unauthenticated users. The simulation result shows that our proposed paradigm achieves high security. We compute our paradigm with various evaluation metrics and we compared with similar models like Support Vector Machine and Artificial Neural Networks. We got great improvement. In order to reduce the complexity, the number of parameters is reduced because, if the parameters are huge, the algorithm prediction will be more complex, which leads to false negatives.

Strong authentication framework using statistical approach for cloud environments

In order to overcome all the flaws and limitations related to the existing system in the medical field regarding the storing, sharing and accessing the medical data or records of the users, this research study come up with a solution to solve this issue by creating a user friendly mobile application to store all the medical information of the users in a single platform, which enables easy sharing, storing and accessing of medical records of the users. This system brings up an easy way to store the medical records of the users in a mobile application using blockchain technology. Since blockchain technology is being used in the system, it enables quite a good amount of security and enables the safety of the data being stored in the application. Once the account is created for a particular patient and all the data is entered by the hospital admins, the user can then have an account for themselves and can also track their medical information in the app as to who are the ones viewing their data. The hospital can feed all kinds of patient information in the application and can save and store them. The data in the application is stored safely and securely ensuring high levels of security and thus keeping the data safe without getting lost and enabling a trustworthy interface to store people's health-related data. Medical data such as scans, x-rays, test reports, prescriptions etc., can be stored in the application in different formats such as images, pdf etc.

Nalini Subramanian

Papers

Recent security challenges in cloud computing

Random forest regression with hyper parameter tuning for medical insurance premium prediction

Smart Video Survillance Based Weapon Identification Using Yolov5

Strong authentication framework using statistical approach for cloud environments

Blockchain based Medical Record Storage and Retrieval using NFT Tracking System