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Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one of the most important issues for businesses and consumers that use cloud computing systems. A few of the security flaws that are associated with cloud computing were passed down from earlier computer systems. In contrast, the other flaws were brought about by the distinctive qualities and design of cloud computing. The newly developed platform has measures that restrict data access to just those users who are authorized to do so. Using the user's identification and authentication/authorization information, a third-party service is responsible for managing access to the data. This service checks on all requests. Sensitive information and facts pertaining to users are encrypted both while in transit and while being stored. The platform was put into operation, analysed, and compared to other cloud platforms that were already in existence in terms of how effective it was in comparison to other platforms. When compared to the other security platforms, the findings demonstrated that this platform performed as anticipated in a relatively short amount of time and offered robust protection against the acts of an intruder. [ABSTRACT FROM AUTHOR]

The conventional advanced encryption standard (AES) method requires improvement to accommodate modern security hazards in cloud computing. Outsourcing private and secret information to distant information networks is difficult due to new problems connected with the confidentiality and safety of information. The structure presented by this study has essential components involving improved security and owner-data privacy. The dual-cycle key attribute alters the 128-bit AES method, accelerating encryption at 1000 bits per second. However, a singular circular bar with 800 blocks per second has been used historically. The suggested technique uses less energy and improves the distribution of load, trust, and control of resources on the entire network. The proposed framework calls for the use of AES with 128, 64, 32, and 16 bytes of simple text. Visualizing simulation outcomes shows the technique's capability for obtaining specific quality features. According to the assessments, the proposed framework reduces energy consumption by 14.43 %, connection utilization by 11.53 %, and delay by 15.67 %. As a result, the suggested framework improves protection, decreases resource usage, and shortens latency when delivering computing services over the cloud. The experiment was done with Anaconda Python and Eclipse. [ABSTRACT FROM AUTHOR]

Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, "Machine Learning-Enhanced Hybrid Cryptography and Image Steganography," integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm's effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research. [ABSTRACT FROM AUTHOR]

Cloud computing is a cost-effective way for organizations to access and use IT resources. However, it also exposes data to security threats. Authentication and authorization are crucial components of access control that prevent unauthorized access to cloud services. Organizations are turning to identity management solutions to help IT administrators face and mitigate security concerns. Identity management (IDM) has been recognized as a more robust solution for validating and maintaining digital identities. Identity management (IDM) is a key security mechanism for cloud computing that helps to ensure that only authorized users have access to data and resources. Traditional IDM solutions are centralized and rely on a single authority to manage user identities, which makes them vulnerable to attack. However, existing identity management solutions need to be more secure and trustworthy. Blockchain technology can create a more secure and trustworthy cloud transaction environment. Purpose: This paper investigates the security and trustworthiness of existing identity management solutions in cloud computing. Comparative results: We compared 14 traditional IDM schemes in cloud systems to explore contributions and limitations. This paper also compared 17 centralized, decentralized, and federated IDM models to explain their functions, roles, performance, contribution, primary metrics, and target attacks. About 17 IDM models have also been compared to explore their efficiency, overhead consumption, effectiveness to malicious users, trustworthiness, throughput, and privacy. Major conclusions: Blockchain technology has the potential to make cloud transactions more secure and reliable. It featured strong authentication and authorization mechanisms based on smart contracts on the Ethereum platform. As a result, it is still regarded as a reliable and immutable solution for protecting data sharing between entities in peer-to-peer networks. However, there is still a large gap between the theoretical method and its practical application. This paper also helps other scholars in the field discover issues and solutions and make suggestions for future research. [ABSTRACT FROM AUTHOR]

Recently, network users have been scared of storing sensitive information, such as bank details, health records, and other vital information, on the Internet because it is vulnerable to attack by a third party. Several threat models are impacting the security of the cloud. Having a secure cloud system will help to be at ease in using cloud computing facilities. This study aims at providing a cryptography approach to eliminating the vulnerabilities in the cloud-based system, and making access and data storage in the cloud very safe. The system uses Rivest-Shamir-Adleman (RSA) to encrypt files and the Advanced Encryption Standard (AES) key to encrypt the encrypted files. The hash function is used for extra key security, and Python programming language was used to implement the system, and for cloud storage, MongoDB was used. Generally, results indicate that the Double Stage Encryption (DSE) takes an average time for encryption of 83% and decryption of 75% compared to RSA and AES singly. The RSA is 68% faster than AES during the encryption process, but there is no significant difference between the two during decryption. The Avalanche effect testing showed the DSE to be 17% higher than singly testing AES and RSA, which implies it is more secure than RSA and AES as single encryption schemes. Therefore, the study recommends using DSE to secure valuable data on the cloud. [ABSTRACT FROM AUTHOR]

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security. [ABSTRACT FROM AUTHOR]

Multi-tenant public cloud architecture is plagued by numerous security issues in cloud computing, including host access restrictions, unlawful access, and data security. The National e-governance division is discouraged from launching numerous e-governance projects, including the Adhaar-digital locker, e-sign, financial-PayGov, Jan Dhan Yojna, E-participation-MyGov, and e-Sampark. Due to the limited computational capabilities in the private cloud, the performance of the applications will also be impacted. In these e-governance initiatives, a significant amount of user data will be produced that will be impossible to handle on the private cloud. To manage the user's data, a private, secure space with high-performance computing resources in a public cloud environment will be required. This article presents DPC2-CD, a secure architecture, and methods that (i) offer a secure private space in a public cloud environment, (ii) perform distributed processing and concurrency control of cloud databases, and (iii) guarantee the high performance of e-governance applications. The National e-Government Division will be able to successfully implement the numerous e-Government projects with the help of our proposed solution. [ABSTRACT FROM AUTHOR]

When studying an unfamiliar system, we first look for the symmetry that the system has, so that we can make many predictions about the possible properties of the system. The symmetry in ship network security needs to maintain a stable state and maintain a constant state of ship network security. With the rapid development of network information technology, smart ships have become a new hot spot in the international shipping industry. The smart ships cybersecurity discussion is also at the top of the list in the maritime field. More and more shipping companies feel that their smart ship systems need to be upgraded and the main reason behind this is that the systems are maliciously attacked by cyber hackers. Therefore, it is extremely important to detect and protect the security of intelligent ship network systems in real time. The issue of network security has always accompanied the whole process of the development of the Internet. At the same time, with the development of Internet technology, network hacking attacks against the Internet have never stopped developing, and traditional ship network security risk detection and protection cannot achieve good results. After understanding the operation mode of intelligent ship networks, this paper deeply studied the characteristics of cloud computing technology and proposed a real-time risk detection method and protection strategy for intelligent ship network security based on cloud computing. This paper mainly used multi-sensor nodes to analyze data containing malicious attack information and implemented self-execution protection strategy generation nodes to intercept and protect from the attack, so as to achieve the purpose of maintaining the network security of intelligent ships. Through experiments, the virus intrusion detection and defense rate of the algorithm proposed in this paper was able to reach 85% to 95%, while the virus intrusion detection defense rate of the traditional intelligent ship network security protection algorithm was 55% to 65%. The detection rate of the algorithm proposed in this paper was able to reach 96.95% and the false positive rate was 2.56%. The detection rate of the traditional algorithm was only 70.76%, while the false positive rate reached 4.69%. All of the proposed algorithm's data were significantly better than that of traditional algorithms, which proved that the performance of cloud computing-based real-time risk detection and protection algorithms for intelligent ship network security was significantly better than that of traditional algorithms. [ABSTRACT FROM AUTHOR]

Fully homomorphic encryption allows to perform arbitrary computation over encrypted data which has great practical implications in the secure outsourced computation on an untrusted computation environment. This paper investigates secure matrix multiplication based on fully homomorphic encryption. We propose an efficient secure matrix multiplication scheme for arbitrary matrix such as A m × l × B l × n based on the hypercube structure. Our proposal is the first single-ciphertext and composable secure matrix multiplication scheme for arbitrary matrix based on fully homomorphic encryption. Our scheme takes only l homomorphic multiplications and experimental results show that it has excellent performance for the matrices of different dimensions. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is on‐demand deliverable services over computer network for data processing and storage. CC has emerged with multiple datacenters available all over the world and providing services with a pay‐as‐you‐go criteria. Even though the cloud system has numerous advantages still it faces one of the major challenges like vulnerability to security attacks. There are multiple types of attacks categorized to break the security like Man‐in‐the‐middle, Malware injection, and Denial‐of‐service (DOS), etc. In case of deep learning (DL), the network called as convolutional neural network (CNN) is a class of deep neural networks (DNN). It is generally used to explore visual imagery. CNN are computationally comprehensive for a commercial computer. In this paper, the focus is to highlight various security attacks in the cloud with machine and deep learning methods for the detection of attacks. Also review different Machine Learning (ML) methods to overcome the cloud security challenges. [ABSTRACT FROM AUTHOR]

The technology that is most likely to change the corporate world in the next ten years is not social networks, Big Data, cloud computing, robots, not even artificial intelligence, but blockchain. 10.3390/sym14102167 5 Huang D.-C., Liu L.-C., Deng Y.-Y., Chen C.-L., Huang D.-C., Liu L.-C., Deng Y.-Y., Chen C.-L. An artwork rental system based on blockchain technology. Using blockchain technology, in an environment where there is no central authority, under the premise of preventing the tampering of artwork-related information, ensures that the detailed information of the art-work information is shared. 10.3390/sym14020237 4 Huang D.-C., Liu L.-C., Deng Y.-Y., Chen C.-L. A Digital Media Subscription Management System Combined with Blockchain and Proxy Re-Encryption Mechanisms. [Extracted from the article]

The increasing development of decentralized computer systems that interact extensively has increased the criticality of confronting cyberattackers, hackers, and terrorists. With the development of cloud computing and its widespread use, as well as its dispersed and decentralized character, a unique security measure is required to safeguard this architecture. By monitoring, validating, and managing settings, records, internet traffic, usage data, as well as the operations of specific activities, firewalls can distinguish between normal and unexpected behaviours, thus adding additional network security to cloud computing systems. The location of network security mechanisms in cloud computing environment and also the methods employed in such methods are the two primary aspects where many studies have concentrated their efforts. The objective of such studies is to reveal as many incursions as feasible and to improve the pace and correctness of sensing while minimizing false alarms. Nevertheless, these methods have a large computing burden, a poor degree of precision, and a large time consumption. We propose an accurate and complete approach for detecting and preventing assaults in cloud computing environment via the use of a machine learning techniques both supervised and un-supervised. The operational findings demonstrate that the suggested approach substantially increases attack detection, network security correctness, dependability, and accessibility in cloud computing environment, while drastically reducing false alarms. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the rapidly growing technologies in computing. It includes many benefits such as improved reliability, enormous scalability, decreased costs, portability, enhanced geographic coverage with fastest time, less infrastructure investment though it has challenges such as data security, insufficient resources, skill, etc. For past few years, cloud computing has grown considerably in information technology. Safety of information is a great concern as enormous information of individuals and companies was being stored in cloud. Many software giants such as Microsoft joined hands to build cloud services. The growth of cloud computing had been affected by data security issues, which leads to complexity with data privacy and protection. The objective of this work is to study and analyze the security issues in cloud computing. The security issues pertaining to network, data, application, storage, and information in cloud domain with the algorithms and tools deployed were addressed and reviewed elaborately. [ABSTRACT FROM AUTHOR]

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. Cloud computing is not a computer network only it's about hosting and services that are provided, run, and maintain over that network. Application or documentation is hosted on a single company server and accessed via the company network with network computing. Cloud computing is bigger than that it Includes multiple companies, multiple servers, as well as multiple networks. In this paper, we mainly discuss three main issues of cloud computing issues in the cloud, Attacks on cloud infrastructure, and security at different layers in the cloud. The main objectives of this research are to identify the meaning of cloud computing, get knowledge about the history of cloud computing, identify issues in cloud computing, identify challenges in cloud computing, and identify solutions for the issues in cloud computing. The research also touches on the cloud services are now an integral part of corporate life, bringing with them the opportunity to accelerate business with their fast-tracking capabilities, allowing us to accelerate our resources, and provide new opportunities for collaboration. The world today is becoming increasingly cloudy, and the effects of businesses and consumers alike are far-reaching. Cloud Computing brings many benefits to its users such as reduced costs, easy shipping, scale as you want, etc. [ABSTRACT FROM AUTHOR]

Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

The tremendous development in mobile technology attracts users' attention. Thus, the users are shifting from traditional computational devices to smartphones and tablets, and because of that, mobile devices have anticipated most of the global IP traffic. However, mobile device's resource-constrained behaviour cannot handle the heavy computational load. Mobile cloud computing (MCC) mitigates resource-constrained issues by enabling computing resources with minimal effort. However, providing security in MCC is an obstacle due to users' uncertain and dynamic behaviour and the explosion of online computerized data. Providing security, confidentiality, and authentication is not enough in MCC; therefore, the users need authorization. Thus, the paper designs an access control mechanism by computing the trust based on the user's uncertain behaviour. This mechanism mitigates the malicious actions caused by authenticated users. Performance results indicate that the access control mechanism accurately detects and mitigates malicious users from the MCC environment. [ABSTRACT FROM AUTHOR]

The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. Current IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, this work develops an IDS using a recurrent neural network based on gated recurrent units (GRUs) and improved long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU. The proposed system efficiently classifies the network flow instances as benign or malevolent. This system is examined using the most up-to-date dataset CICIDS2018. To further optimize computational complexity, the dataset is optimized through the Pearson correlation feature selection algorithm. The proposed model is evaluated using several metrics. The results show that the proposed model remarkably outperforms benchmarks by up to 12.045%. Therefore, the Cu-LSTMGRU model provides a high level of symmetry between cloud computing security and the detection of intrusions and malicious attacks. [ABSTRACT FROM AUTHOR]

Machine learning makes it possible for challenging jobs to be finished totally on their own. Computers as well as mobile devices may make it simpler to regulate interior temperature, keep an eye on security, and carry out periodic maintenance in a smart grid (SG). The smart grids' capacity to detect cyber attacks is crucial for determining the availability and dependability of the operation. Integrity of bogus data cyber attacks in physical layers of smart grids (SGs) is highlighted in this article. This study suggests a unique method for managing the energy usage of smart grids while analysing the security of data transfer. Here, the energy efficiency of the smart grid network is evaluated using renewable energy sources (SM_RES), and the network has been watched for security evaluation in cloud computing. Multi agent Markov reinforcement learning (MAMRL) is used to analyse the security of the cloud network in the smart grid. In terms of scalability, QoS, energy efficiency, power consumption, prediction accuracy, RMSE, average precision, and data integrity, experimental study is conducted for energy management and network security. Proposed technique attained prediction accuracy 95 %, packet loss rate 77 %, RMSE 88 %, Average precision 85 %, network security analysis 97 %; scalability 95.8 %, QoS 86 %, Power consumption 35 % and energy efficiency 96 %. [ABSTRACT FROM AUTHOR]

Users are provided access to on-demand services through the Internet with the assist of cloud computing. Services can be accessed at any time and from any place. Although delivering useful services, this model remains vulnerable to security problems. The accessibility of cloud resources is affected by Distributed Denial of Service (DDoS) attacks, which also present security risks for cloud computing. Unable to access data from cloud services, various advanced risks such as malware injection, packaging as well as virtual machine escapes and DDoS are developed by the attackers. Recently, numerous models were designed for detecting attacks in the cloud, but still they lack certain reasons. To alleviate these concerns, this proposed method presented a DDoS attack prediction using a honey badger optimization algorithm based on feature selection and Bi-LSTM in a cloud environment. Input features are gathered from the DDoS attack dataset as the first step in the process. Following this, input features are transmitted into preprocessing steps, including Bayesian and Z-Score normalization. Preprocessed data is sent into the feature selection phase that employs Honey Badger Optimization (HBO). In this case, the features are chosen by decreasing their MSE to obtain the best feature. Then, optimal features are fed into the Bi-directional Long Short term Memory (Bi-LSTM) classifier for predicting DDoS attacks. The proposed model is also examined using certain existing approaches, including LSTM, DNN, DBN and ANN. When the performance was examined using the existing method, the Bi-LSTM model achieved 97% accuracy, 95% sensitivity, 90% specificity, 3% error, 94% precision and so on. The proposed model is effective at finding DDoS in a cloud environment. [ABSTRACT FROM AUTHOR]

Cloud computing security has been a critical issue with its increase in demand. One of the most challenging problems in cloud computing is detecting distributed denial-of-service (DDoS) attacks. The attack detection framework for the DDoS attack is tricky because of its nonlinear nature of interruption activities, atypical system traffic behaviour, and many features in the problem space. As a result, creating defensive solutions against these attacks is critical for mainstream cloud computing adoption. In this novel research, by using performance parameters, perplexed-based classifiers with and without feature selection will be compared with the existing machine learning algorithms such as naïve Bayes and random forest to prove the efficacy of the perplexed-based classification algorithm. Comparing the performance parameters like accuracy, sensitivity, and specificity, the proposed algorithm has an accuracy of 99%, which is higher than the existing algorithms, proving that the proposed algorithm is highly efficient in detecting the DDoS attacks in cloud computing systems. To extend our research in the area of nature-inspired computing, we compared our perplexed Bayes classifier feature selection with nature-inspired feature selection like genetic algorithm (GA) and particle swarm optimization (PSO) and found that our classifier is highly efficient in comparison with GA and PSO and their accuracies are 2% and 8%, respectively, less than those of perplexed Bayes classifier. [ABSTRACT FROM AUTHOR]

The purpose of this research is to develop a high-efficiency, low-cost, and easy-to-use tracking system for vehicles, and it is expected that the system can be extended to areas such as service robots, autonomous driving, and manufacturing. In this paper, we introduced an object detection algorithm based on convolutional neural networks to realize face recognition, which has better efficiency and robustness than traditional machine learning methods. With the concept of edge computing, we deployed the model on the local embedded system to improve the information transmission and security issues of cloud computing. In order to realize the tracking system, this paper builds a mecanum-wheel vehicle with omnidirectional mobility, and proposes a parallel-cascade PID controller architecture based on the mecanum-wheel vehicle. The fixed distance linear tracking control can be realized through the dual-loop feedback control of distance and yaw angle; moreover, the vehicle slipping which is caused by difference rotation speed can be improved. Finally, through algorithm optimization, controller parameter adjustment, and system integration, an omnidirectional mobile vehicle with recognition and tracking functions is realized. The experiment results indicate that the system is stable and robust during actual operation. [ABSTRACT FROM AUTHOR]

In the contemporary world, the security of data and privacy policies are major concerns in cloud computing. Data stored on the cloud has been claimed to be unsafe and liable to be hacked. Users have found it difficult to trust their data in the cloud. Users want to know that their data is accessible from anywhere and that an unauthorized user will not be able to access it. Another area of concern is the authentication of users over the cloud. There are a number of security concerns with Cloud Computing which include Distributed Denial of Service, Data leakage, and many more, just to mention a few. In this paper, an Elliptic Curve Cryptography (ECC) algorithm is used for the encryption and decryption of the information stored on the cloud, so that if someone gains access to the server, it would be unable to access the original information. Performance evaluation of the developed system is performed by comparing ECC with the RSA algorithm. The results show that the ECC method is more efficient than RSA when used to secure information in the cloud. [ABSTRACT FROM AUTHOR]

Many consumers participate in the smart city via smart portable gadgets such as wearables, personal gadgets, mobile devices, or sensor systems. In the edge computation systems of IoT in the smart city, the fundamental difficulty of the sensor is to pick reliable participants. Since not all smart IoT gadgets are dedicated, certain intelligent IoT gadgets might destroy the networks or services deliberately and degrade the customer experience. A trust-based internet of things (TM-IoT) cloud computing method is proposed in this research. The problem is solved by choosing trustworthy partners to enhance the quality services of the IoT edging network in the Smart architectures. A smart device choice recommendation method based on the changing networks was developed. It applied the evolutionary concept of games to examine the reliability and durability of the technique of trust management presented in this article. The reliability and durability of the trustworthiness-managing system, the Lyapunov concept was applied.A real scenario for personal-health-control systems and air-qualitymonitoring and assessment in a smart city setting confirmed the efficiency of the confidence-management mechanism. Experiments have demonstrated that the methodology for trust administration suggested in this research plays a major part in promoting multi-intelligent gadget collaboration in the IoT edge computer system with an efficiency of 97%. It resists harmful threads against service suppliers more consistently and is ideal for the smart world's massive IoT edge computer system. [ABSTRACT FROM AUTHOR]

In the rapid world of technology, the IT Industry is growing at a rapid speed with Cloud Image Security. In the developing trends, cloud orchestration is becoming impressive and uncertain of dealing a large amount of information. In the last two decades there has been extensive development in image encryption. Chaos based image encryption techniques form framework for a faster algorithm than traditional techniques. Chaos based techniques are highly efficient in case of multimedia data. The proposed algorithm uses the modified logistic map for the generation of chaotic sequence, encrypts the image in single scan, so runs faster and makes the proper use of confusion and diffusion. It dynamically updates the key during execution. This algorithm is secure against the brute force attack and differential attack. [ABSTRACT FROM AUTHOR]