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The preventive control is one of the best well advance control for recent complex IS Security Application to protect the data and services from the uncertainty, hacker, and unauthorized users. Now, increasing the demand and importance of business, information & communication system & growing the external risks is a very common phenomenon for everywhere. The RTS security put forward to the management focus on IT infrastructure. This work contributes to the development of an optimization pattern that aims to determine the optimal cost to be apply into security mechanisms deciding on the measure components of system security and resources. The author's mechanism should be design in such way, the Confidentiality, Integrity, Availability, Authenticity and Accountability are automatically PDC for all the time. The author has to optimize the system attacks and down time by implementing semi-group structure CIA pattern, mean while improving the throughput of the Business, Resources & Technology. Finally, the author has to maximize the protection of IT resources & Services for all the time and every time. This proposed CIA Pattern is the part of protection, detection, benchmarking, fault analysis and risk assessment of real time operating system and applicable to efficient resource management on web application.

Continuous and emerging advances in Information and Communication Technology (ICT) have enabled Internet-of-Things (IoT)-to-Cloud applications to be induced by data pipelines and Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet of Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or ineffective authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected edge to cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication were proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on the Dolev–Yao adversarial model while satisfying aspects of the Confidentiality, Integrity, and Availability (CIA) triad.

Cloud computing environment (CCE) can empower an association to re-appropriate computing resources to increase monetary benefits. For both developers and the cloud users (CUs), CCE is transparent. Accordingly, it presents new difficulties when contrasted with precedent types of distributed computing. The precision of assessment results in CCE security risk assessment to take care of the issue of the multifaceted nature of the system and the classified fuzzy cloud method (CFCM) applied to CCE chance ID stage that captures the CCE risk factors through a complete investigation of CCE security area. Current CCE frameworks present a specific restriction on ensuring the client’s INFO privacy. We offer a homomorphic proxy re-encryption (HPRE) in this paper that enables various CU to share INFO that they redistributed HPRE encrypted utilizing their PubKs with the plausibility by a close procedure such as INFO remotely. The test of giving secrecy, uprightness, and access control (AC) of INFO facilitated on cloud stages is not provided for by conventional AC models. CFCM models were created through the duration of numerous decades to satisfy the association’s necessities, which accepted full authority over the physical structure of the assets. The hypothesis of the INFO proprietor, an INFO controller, and a supervisor is available in the equivalent trusted area. Besides, CCESR features like the essential unit, fuzzy set (FS) hypothesis, and EW strategy utilized to precisely measure the likelihood of CCE security risks (SR) and the subsequent damages of CCESR estimation. Eventually, the computation and authentication model specified, and the lack of CCE SECU threat evaluation examined.

The challenge of data deduplication has piqued the interest of the research community in order to provide effective and efficient solutions for cloud data centres to remove bottlenecks caused by duplicate data on the exponential growth of outsourced data. An Imminent challenge is the presence of high redundant existence in the storage memory of the cloud environment. Traditional algorithm has been employed to mitigate the data duplication in the cloud environment but it is a daunting task since it requires an effective solution to manage the duplication in the application basis to eliminate the privacy violation on sensitive data. In order to tackle above mentioned issues, a new paradigm named as Security Aware High Scalable has been proposed on large scale computing data centres. Proposed method employed to secure the data Deduplication process using access control mechanism on data of the privileged authorities. Outsourced data will be in the form of encrypted files. Access control mechanism is provided to the secure authorities to conduct the data deduplication on the data that was outsourced. Access Control Mechanism is employed using encryption techniques. It uses randomised encryption that is convergent and stable distribution of ownership party keys to enable the cloud service provider to handle outsourced access to data even though control changes on a regular basis This prevents data from being leaked not just to to those who have had their rights removed who previously owned the data, but even to a degree trustworthy yet suspect server for cloud storage. Furthermore, the proposed technique protects integrity of data from attacks based on label inconsistencies. As a measure, the proposed technique has been modified that enhances security. The efficiency review reveals that the proposed system is nearly as successful as the existing ones, with just a small increase in computational overhead.

The Internet of Things (IoT), the convergence of blockchain and deep learning (DL) technologies presents exciting opportunities for innovation. The integration of a secured IoT architecture with a blockchain framework and DL technology offers a promising avenue for personalized marketing. The primary challenges in the realm of IoT-based personalized marketing are the vulnerability of user data and the need for a secure framework to protect sensitive information. The objective of the study is to develop a holistic solution by integrating blockchain technology and DL, offering a secure and personalized marketing architecture that safeguards user privacy while enhancing the effectiveness of targeted marketing strategies. Data collection, leveraging a blockchain network proves instrumental for expediting data transactions between the seed node and destination node while ensuring robust security measures. In this approach, a consortium blockchain integrates dispersed clusters of private to store encrypted data, increasing data transmission efficiency while ensuring operator privacy and security through off-chain storing and on-chain transmission synergy. The study then presented a lightweight hierarchical blockchain-based multi-chain code access control (AC) to safeguard the security and secrecy of IoT devices. Furthermore, federated DL is used to determine the best threshold and pertinent AC parameters, hence improving AC accuracy and privacy protection. Stacking involves training machine learning algorithms initially on training datasets and subsequently using these models to generate predictions for a new dataset. This new dataset, consisting of the predictions from the initial models, is then utilized as input for the ensemble algorithm. As a finding, the researchers presented an Ensemble Stacking approach combined with a deep long short-term memory-based intrusion detection method for detecting malicious or regular network traffic flow patterns in a cloud context. The proposed work is implemented using Python software. The findings show that the high accuracy value of 97.5% indicates the model’s proficiency in making precise and reliable predictions. The specificity value of 0.9045% indicates the maximum accuracy of the method. The accuracy reaches a remarkable 91.879%, outperforming the existing methods. The integration of a secured IoT architecture with a blockchain framework and DL technology offers a robust solution for personalized marketing while addressing challenges related to data refuge and privacy.

Internet of Things (IoT) applications are popularly involved in day-to-day life. The increase in utilization leads to an increase in network traffic. The incoming users have different intentions in the network and hence security is essential. The data user accesses the data in the cloud that is collected from IoT devices. A large-scale IoT environment has challenges in the provisioning of security as well as the management of access control mechanisms. The problem is a generation of policies and authenticating devices with minimum credentials. In this paper, Blockchain-based decentralized authentication and access control systems are designed. The process of authentication is conducted for the data owner and data user by considering identity, device type, IP address and signature, PUF, and biometric respectively. PUF stands for Physical Unclonable Function, which is a hardware-based security feature that generates a unique identifier for a device based on its physical properties, SALSA20 and PRESENT are encryption algorithms used in the proposed system to encrypt data chunks. SALSA20 is a stream cipher that generates a keystream to encrypt data, while PRESENT is a block cipher that encrypts data in fixed-size blocks These authentication credentials are managed in the blockchain. The credentials are stored in encrypted form using the Key schedule PRESENT algorithm. In the authentication of data users, the number of credentials is selected using fuzzy logic that improves security. To assure data storage security, the data is split into two chunks, and it is encrypted using SALSA20 and PRESENT algorithm. The proposed model is developed in an ifogsim simulator, and the performance metrics are evaluated in terms of authentication time, storage efficiency, running time, throughput, latency, and blocksize.

Big data is a high volume data, as it comprises complex and large volume of information. A successful solution is to redistribute the data to a cloud server that has the capacity of storing and processing big data in an effective manner. The main intention of the research is to secure storage of big data and effective access control mechanism. The main stages of the proposed method are map reduce framework, secure storage process and access control mechanism process. Map Reduce is a distributed programming framework used to process big data. In mapper, the input dataset is grouped using hybrid kernel fuzzy c means (HKFCM) clustering algorithm. Finally, the reduced output is fed to the data owner for secure storage. In secure storage process, the suggested method utilizes optimal elliptic curve cryptography (OECC). Here the fundamental values are optimally selected by Modified grasshopper optimization algorithm (MGOA). In the access control mechanism, the effective policy update is proposed along with data storage construction and data deconstruction stage. The routine of the recommended method is assessed using memory and execution time by differentiating the number data size, number cluster size and the number of mapper. The proposed method attains the minimum time and memory utilization when compared to the existing method. The suggested method is implemented in cloud sim with Hadoop Map-reduce framework.

The information security problems for open multiagent systems using access control schemes based on RSA algorithm are analyzed. It is shown that when using such schemes, a significant part of the agents’ lifetime is spent on authentication processes. In order to increase the efficiency of multiagent systems by reducing the number of agent authentication processes, a method of access differentiation based on Goppa codes is justified and proposed. For the proposed methodology, an assessment of the security level of agents' information is given.

Storage in cloud computing is the fundamental service which is widely used by consumers of cloud. Cloud offer many advantages such as flexibility, elasticity, scalability and sharing of data among users. However, cloud storage throws many privacy and security challenges. Especially, the most significant problem is access control mechanism which ensures sharing of dataonly to authorized users. Most of the cloud service providers offer Role Based Access Control (RBAC) where users are grouped into roles and access is given to resources based on roles. The problem with this scheme is that once a role gets access to a resource, further restrictions are not possible, where there are security limitations for which data owner needs to restrict access to a part of an object but not entire object. This work proposes to useSwift, an object storage service in open source cloud named OpenStack. Swift restricts access to objects using Access Control Lists (ACLs). As per ACL, users can gain access to an object. However, once access is given, users can access the complete object without further restrictions. The proposed work is evaluated in real cloud environment Amazon cloud, Microsoft Azure, and Open stack cloud. A framework termed Predicate Based Access Control (PBAC) is proposed to render fine grained access control to Swift storage. Access is provided to predicates that are part of an object. Instead of following an “all or nothing” approach, an access control mechanism that makes the Swift storage and retrieval more secure is preferred.