Secure Cloud Environment Research Articles

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Artificial neural networks (ANN), fuzzy inference systems, approximate reasoning, and derivative-free optimization approaches like evolutionary computation, among others, are all components of Soft Computing (SC), an innovative method for building computationally intelligent methods. Due to its widespread use and benefits, cloud computing is currently a major focus for researchers. The distributed nature of cloud computing and its complete reliance on the internet for service provision present security challenges, the most serious of which is insider Distributed Denial of Service (DDoS) which results in complete deactivation of services. Based on machine learning and the cloud, this study proposes a novel method for improving computing network security. Using a trust-based secure cloud environment and the Kernel principal component encoder architecture, the network monitoring is carried out. The soft computing environment-based detection of a cyberattack on the network is the purpose for the security enhancement. Throughput, QoS, latency, and packet delivery ratio for various monitored cyber security datasets are the focus of the experimental analysis. The proposed technique attained network security analysis of 89%, throughput of 98%, QoS of 66%, latency of 59%, packet delivery ratio of 83%.

Cloud Security Metrics and Measurement

This research aims to investigate the cloud security metrics by exploring the existing frameworks, integrated approaches and quantitative measurements to enhance overall security in cloud environments. This study explains several established frameworks and standards, such as the CSA Cloud Controls Matrix, NIST SP 800-53 and ISO/IEC 27001, which are important in guiding organizations towards strong security practices. Moreover, a qualitative research method has been used in this research study, conducting a literature review of past studies on the same topic. The challenges and limitations regarding cloud security have also been mentioned in this research study, which is helpful for future researchers to explore in detail and develop stronger strategies for enhancing cloud security as a whole. This research study states that the emerging threats and technologies related to cloud security must be explored continuously to develop a detailed understanding of cloud security measures.

Cloud Security Metrics and Measurement

This research aims to investigate the cloud security metrics by exploring the existing frameworks, integrated approaches and quantitative measurements to enhance overall security in cloud environments. This study explains several established frameworks and standards, such as the CSA Cloud Controls Matrix, NIST SP 800-53 and ISO/IEC 27001, which are important in guiding organizations towards strong security practices. Moreover, a qualitative research method has been used in this research study, conducting a literature review of past studies on the same topic. The challenges and limitations regarding cloud security have also been mentioned in this research study, which is helpful for future researchers to explore in detail and develop stronger strategies for enhancing cloud security as a whole. This research study states that the emerging threats and technologies related to cloud security must be explored continuously to develop a detailed understanding of cloud security measures.

Data security in cloud environment using cryptographic mechanism

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

Implementation of a Hybrid Triple-Data Encryption Standard and Blowfish Algorithms for Enhancing Image Security in Cloud Environment

Implementation of a Hybrid Triple-Data Encryption Standard and Blowfish Algorithms for Enhancing Image Security in Cloud Environment

Securing cloud environments: A comparative analysis of RSA and MRGA for enhanced data protection

Cloud computing revolutionizes the way businesses are stored and managed, and ensures robust security measures. This paper explores the use of Rivest-Shamir-Adleman (RSA) and the Magic Rectangle Generating Algorithm (MRGA) to enhance security in cloud environments. This highlights the growing significance of cloud computing and the need for effective security measures. It then introduces RSA and MRGA as cryptographic algorithms with their unique strengths and applications. It further delves into the benefits of using RSA and MRGA for data protection, key management, authentication, and identity management in cloud environments. The potential of these algorithms to address security challenges in the cloud and improve the overall data security is discussed. Additionally, the abstract acknowledges the limitations and considerations associated with the implementation of the RSA and MRGA. This emphasizes the importance of this research in enhancing cloud security and provides insights into the potential of RSA and MRGA as security measures in the cloud.

Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

Nowadays, numerous applications are associated with cloud and user data gets collected globally and stored in cloud units. In addition to shared data storage, cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud, public cloud, community cloud and private cloud. Though cloud-based computing solutions are highly convenient to the users, it also brings a challenge i.e., security of the data shared. Hence, in current research paper, blockchain with data integrity authentication technique is developed for an efficient and secure operation with user authentication process. Blockchain technology is utilized in this study to enable efficient and secure operation which not only empowers cloud security but also avoids threats and attacks. Additionally, the data integrity authentication technique is also utilized to limit the unwanted access of data in cloud storage unit. The major objective of the projected technique is to empower data security and user authentication in cloud computing environment. To improve the proposed authentication process, cuckoo filter and Merkle Hash Tree (MHT) are utilized. The proposed methodology was validated using few performance metrics such as processing time, uploading time, downloading time, authentication time, consensus time, waiting time, initialization time, in addition to storage overhead. The proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.

ElGamal algorithm with hyperchaotic sequence to enhance security of cloud data

PurposeIn the cloud-computing environment, privacy preservation and enabling security to the cloud data is a crucial and demanding task. In both the commercial and academic world, the privacy of important and sensitive data needs to be safeguarded from unauthorized users to improve its security. Therefore, several key generations, encryption and decryption algorithms are developed for data privacy preservation in the cloud environment. Still, the outsourced data remains with the problems like minimum data security, time consumption and increased computational complexity. The purpose of this research study is to develop an effective cryptosystem algorithm to secure the outsourced data with minimum computational complexity.Design/methodology/approachA new cryptosystem algorithm is proposed in this paper to address the above-mentioned concerns. The introduced cryptosystem algorithm has combined the ElGamal algorithm and hyperchaotic sequence, which effectively encrypts the outsourced data and diminishes the computational complexity of the system.FindingsIn the resulting section, the proposed improved ElGamal cryptosystem (IEC) algorithm performance is validated using the performance metrics like encryption time, execution time, decryption time and key generation comparison time. The IEC algorithm approximately reduced 0.08–1.786 ms of encryption and decryption time compared to the existing model: secure data deletion and verification.Originality/valueThe IEC algorithm significantly enhances the data security in cloud environments by increasing the power of key pairs. In this manuscript, the conventional ElGamal algorithm is integrated with the pseudorandom sequences for a pseudorandom key generation for improving the outsourced cloud data security.

Authorization Model for Securing Cloud SaaS Services (Netflix)

At the enterprise level, cloud software as a service (SaaS) is in high demand. As software as a service (SaaS) can be easily provided in a multitenant way, its execution assessment, information inactivity, and authorization are critical issues related to the cloud supplier at the time of providing administrations to sellers. Netflix is a mixed media supplier that gives online streaming from a library of movies and TV arrangement. Netflix services are exclusively based on cloud SaaS. The highlight of the study is to analyze the finest hones over secure cloud environment in authorization perspective based on behavioral biometrics. A conceptual authorization is displayed in this paper to secure cloud services, especially SaaS, when unauthorized access is supported by legitimate users. The use case is taken as Netflix.

Modeling and simulation of a novel secure quantum key distribution (SQKD) for ensuring data security in cloud environment

In recent days, providing data security over cloud is a complicated process. There are many research works that are developed for authentication based data security over cloud, using cryptographic methods. In contrast, physical rules are used for encrypting data. When the quantum models are appeared, it is called quantum cryptography. And, the key distribution in such models is called, Quantum Key Distribution (QKD). Using QKD in securing data is more effective against several attacks. This paper develops a novel simulation model called Secure Quantum Key Distribution for Cloud Data Security (SQKD-CDS) Model. For encrypting the user data, the simulation model uses Non-Abelian Encryption (NAE) for providing secure data security and, further, the quantum key is used for accessing the stored data from cloud. Moreover, the keys are shared between nodes in secure manner using the quantum channel. This proposed simulation model is evaluated using cloud simulator. The results show that the proposed simulation model outperforms other classical security simulation model in terms of efficiency, time complexity and computational complexity.

Retraction Note to: A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment

Retraction Note to: A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment

REVIEW ON COMBINING BIOMETRIC AND BEHAVIORAL AUTHENTICATION TECHNIQUES FOR SECURE MOBILE CLOUD COMPUTING

Cloud computing is one of the widely used technology and its use is increasing day by day due to its sharing of services over the internet. The area of concern is its security and privacy issues especially for the smart-phone users. Several security approaches had been introduced to safeguard users from unauthorized access but the security standard with the smart-phone devices are still not up to the mark and uses the typical username and passwords based authentication techniques. This paper is proposing a hybrid authentication approach combining two security modalities i.e. biometric and behavioral keystroke dynamics based user authentication which is designed to provide authentication with the existing username and password along with the biometric and behavioral keystroke dynamics based authentication with the use of biometric fingerprint scanner and keyboard thereby reducing the high cost of additional hardware devices since both are the integral part of the smart-phones these days. This approach will provide a secure access to the smart-phone users for a secure cloud environment efficiently than the existing methods.

Improved Data Security in Cloud Environment for Test Automation Framework and Access Control for Industry 4.0

In analyzing project regressions, automation has emerged as a major agenda in managing changes in software which requires minimum manual intervention. For rapid testing environment, software development processes such as Agile, Scrum, and XP processes depend on continuous integration tools. There is no single tool to handle the project automation, and the main challenge is dependency on multiple tools. The proposed automation tool should support configuration, execution, and debugging facility. Integrating the project automation works such as software configuration management tools Mercurial and Git, job scheduling tools like Jenkins and Apache Continuum, test management tools like TestNG and Selenium need tight integration which is a challenge. The existing PKI infrastructure for access control does not share data among the software tools and processes increasing the complexity when an organization needs to leverage the existing cloud services. The proposed approach optimizes the execution time by taking single CSV with input test case and metadata information and efficiently group and executes the tests automatically. The proposed method includes implementation of security access control mechanism for the jobs execution platform in cloud environment.

A novel model to enhance the data security in cloud environment

Nowadays cloud computing has given a new paradigm of computing. Despite several benefits of cloud computing there is still a big challenge of ensuring confidentiality and integrity for sensitive information on the cloud. Therefore to address these challenges without loss of any sensitive information and privacy, we present a novel and robust model called ‘Enhanced Cloud Security using Hyper Elliptic Curve and Biometric’ (ECSHB). The model ECSHB ensures the preservation of data security, privacy, and authentication of data in a cloud environment. The proposed approach combines biometric and hyperelliptic curve cryptography (HECC) techniques to elevate the security of data accessing and resource preservations in the cloud. ECSHB provides a high level of security using less processing power, which will automatically reduce the overall cost. The efficacy of the ECSHB has been evaluated in the form of recognition rate, biometric similarity score, False Matching Ratio (FMR), and False NonMatching Ratio (FNMR). ECSHB has been validated using security threat model analysis in terms of confidentiality. The measure of collision attack, replay attack and non-repudiation is also considered in this work. The evidence of results is compared with some existing work, and the results obtained exhibit better performance in terms of data security and privacy in the cloud environment.

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

SummaryIn cloud computing, the resources and memory are dynamically allocated to the user based on their needs. Security is considered as a major issue in cloud as the use of cloud is increased. Intrusion detection is considered as a significant tool to develop a reliable and secure cloud environment. Performing intrusion detection in cloud is a difficult task because of its distributed nature and extensive usage. Intrusion detection system (IDS) is widely considered to find the malicious actions in network. In cloud, most conventional IDS are vulnerable to attacks and have no capability for maintaining the balance between sensitivity and accuracy. Thus, we proposed an effective dragonfly improved invasive weed optimization‐based Shepard convolutional neural network (DIIWO‐based ShCNN) to detect the intruders and to mitigate the attacks in cloud paradigm and are more feasible to detect the intruders with ShCNN. The proposed method outperforms the existing method with maximum accuracy of 0.960%, sensitivity of 0.967%, and specificity 0.961%, respectively.

Long-term Survival after Hematopoietic Cell Transplant for Sickle Cell Disease Compared to the United States Population

Hematopoietic cell transplant for sickle cell disease is curative but is associated with life threatening complications most of which occur within the first 2 years after transplantation. In the current era with interest in gene therapy and gene editing we felt it timely to report on sickle cell disease transplant recipients who were alive for at least 2-year after transplantation, not previously reported. Our objectives were to (1) report the conditional survival rates of patients who were alive for 2 or more years after transplantation (2) identify risk factors for death beyond 2 years after transplantation and (3) compare all-cause mortality risks to those of an age-, sex- and race-matched general population in the United States. By limiting to 2-year survivors, we exclude deaths that occur as a direct consequence of the transplantation procedure. De-identified records of 1149 patients were reviewed from a publicly available data source and 950 patients were eligible (https://picsure.biodatacatalyst.nhlbi.nih.gov). All analyses were performed in this secure cloud environment using the available statistical software package(s). The validity of the public database was confirmed by reproducing results from an earlier publication. Conditional survival estimates were obtained using the Kaplan-Meier method for the sub-cohort that had survived a given length (x) of time after transplantation. Cox regression models were built to identify risk factors associated with mortality beyond 2 years after transplantation. The standardized relative mortality risk (SMR) or the ratio of observed to expected number of deaths, was used to quantify all-cause mortality risk after transplantation and compared to age, race and sex-matched general population. Person-years at risk were calculated from an anchor date (i.e., 2-, 5- and 7-years) after transplantation until date of death or last date known alive. The expected number of deaths was calculated using age, race and sex-specific US mortality rates. The median follow up was 5 years (range 2-20) and 300 (32%) patients were observed for more than 7 years. Among those who lived for at least 7 years after transplantation the 12-year probability of survival was 97% (95% CI, 92%-99%). Compared to an age-, race- and sex-matched US population, the risk for late death after transplantation was higher as late as 7 years after transplantation (hazard ratio (HR) 3.2; P= .020) but the risk receded over time. Risk factors for late death included age at transplant and donor type. For every 10-year increment in patient age, an older patient was 1.75 times more likely to die than a younger patient (P= .0004). Compared to HLA-matched siblings the use of other donors was associated with higher risk for late death (HR 3.49; P= .003). Graft failure (beyond 2-years after transplantation) was 7% (95% CI, 5%-9%) and graft failure was higher after transplantation of grafts from donors who were not HLA-matched siblings (HR 2.59, P< .0001). Long-term survival after transplantation is excellent and support this treatment as a cure for sickle cell disease. The expected risk for death recedes over time but the risk for late death is not negligible.

Preserving Data Security in Cloud Environment Using an Adaptive Homomorphic Blockchain Technique

Preserving Data Security in Cloud Environment Using an Adaptive Homomorphic Blockchain Technique

User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient manner. The advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation (RMI). The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any user. Due to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient service. To improve the performance in data security, the block chains are used for securing the data in the cloud environment. However, the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the cloud. In this paper, an efficient user centric block level Attribute Based Encryption (UCBL-ABE) scheme is presented to provide the efficient security of cloud data in cloud environment. The proposed approach performs data transaction by employing the block chain. The proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust (DLAT). Based on DLAT, the user access has been restricted in the cloud environment. The proposed protocol is implemented in real time using Java programming language and uses IBM cloud. The implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

Secure Data Sharing with Confidentiality, Integrity and Access Control in Cloud Environment

Cloud storage is an incipient technology in today’s world. Lack of security in cloud environment is one of the primary challenges faced these days. This scenario poses new security issues and it forms the crux of the current work. The current study proposes Secure Interactional Proof System (SIPS) to address this challenge. This methodology has a few key essential components listed herewith to strengthen the security such as authentication, confidentiality, access control, integrity and the group of components such as AVK Scheme (Access List, Verifier and Key Generator). It is challenging for every user to prove their identity to the verifier who maintains the access list. Verification is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication process. Here, RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric encryption. Various methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS protocol. The proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality, authentication and access control. Stochastic Timed Petri (STPN) Net evaluation tool was used to verify and prove the formal analysis of SIPS methodology. This evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

AI-Powered Security in Cloud Environments: Enhancing Data Protection and Threat Detection

AI-Powered Security in Cloud Environments: Enhancing Data Protection and Threat Detection