Security Of Cloud Services Research Articles

Cloud Computing Security Issues, Challenges, and Solutions

Cloud computing has revolutionized the way agencies manage and deliver their IT offerings by means of imparting scalable, bendy, and fee-effective solutions. However, with these good sized blessings come complicated protection demanding situations. The allotted nature of cloud environments, coupled with multi-tenant infrastructures, offers new risks that require sturdy and adaptive security frameworks. This paper goals to provide an in-intensity exploration of the primary security problems in cloud computing, the underlying challenges companies face in securing cloud services, and the solutions designed to mitigate these dangers

A proposed biometric authentication hybrid approach using iris recognition for improving cloud security

Biometric systems have gained attention as a more secure alternative to traditional authentication methods. However, these systems are not without their technical limitations. This paper presents a hybrid approach that combines edge detection and segmentation techniques to enhance the security of cloud systems. The proposed method uses iris recognition as a biometric paradigm, taking advantage of the iris' unique patterns. We performed feature extraction and classification using hamming distance (HD) and convolutional neural networks (CNN). We validated the experimental findings using various datasets, such as MMU, IITD, and CASIA Iris Interval V4. We compared the proposed method's results to previous research, demonstrating recognition rates of 99.50 % on MMU using CNN, 97.18 % on IITD using CNN, and 95.07 % on CASIA using HD. These results indicate that the proposed method outperforms other classifiers used in previous research, showcasing its effectiveness in improving cloud security services.

Securing Cloud Computing Services with an Intelligent Preventive Approach

Cloud computing is a technological marvel that transcends conventional boundaries by utilizing an Internet-based network of remote servers to store, manage, and process data and many other services. It represents a contemporary paradigm for delivering information technology services. Today, cloud computing services have become indispensable for both individuals and corporations. However, adopting cloud services presents fresh challenges in terms of service quality, resource optimization, data integration, cost governance, and operational security. The security of cloud services is of supreme importance, given the open and distributed nature of the environment, making it susceptible to various cyberattacks, such as Denial of Service (DoS) or Distributed DoS (DDoS) attacks, among others. Cyberattacks can have severe repercussions on the availability of cloud services, potentially causing complete DoS. In numerous instances, the detection of attacks is delayed, pushing cloud platforms to a breaking point. Emphasizing the importance of proactive measures, it becomes crucial to identify and alert about any suspicious access long before the latter reaches a critical stage, mitigating the risks and preventing potential service disruptions. This study introduces a preventive approach that utilizes artificial intelligence techniques to improve the security of cloud services. The proposed method aims to detect and flag potential attack behaviors well in advance before they affect service quality. To achieve this, the particular method involves periodic identification and measurement of critical information on service access and resource utilization. This can be accomplished by analyzing cloud server logs or integrating dedicated sniffing software to capture and store technical traffic details. Subsequently, the collected data are processed by analyzing traffic properties to proactively identify and report any indications of cyberattacks.

RESEARCH METHODS FOR ASSESSING THE QUALITY OF CLOUD SERVICES, INCLUDING RELIABILITY, PERFORMANCE AND SECURITY METRICS

In today's world, cloud services play an important role in the success of any global business. Evaluating the quality of these services is of paramount importance for companies and individuals who use them for their operations and data management. This article is an analysis of various methods for assessing the quality of cloud services. Reliability, performance, and security metrics are discussed, and different assessment models are compared. In the introduction, the authors describe the importance of cloud services and the need to assess their quality. This article provides an overview of cloud service quality assessment methods and shows how they can be applied to different aspects of these services. In the main part of the article, the author describes different methods of assessing the quality of cloud services and compares different assessment models. The author also provides examples of how these methods are used. The methods for assessing the quality of cloud services are discussed, covering reliability, performance, and security. While these metrics provide valuable insights into the performance of cloud services, some counterarguments highlight the potential limitations of using standardized metrics for evaluation. The author also recommends choosing a cloud provider and optimizing cloud services. It is emphasized that reliability metrics, such as uptime and disaster recovery time, are important to ensure the smooth operation of cloud services. Performance metrics, such as response time and throughput, indicate how quickly and efficiently cloud services can process data and requests. Security metrics, such as data encryption and access control, are important to protect data from unauthorized access and cyber threats. The author also emphasizes the importance of considering subjective factors such as user experience and customer support when assessing the quality of cloud services. Finally, the author discusses various risk assessment models that can be used to assess the security of cloud services. The author proposes a new approach to assessing the quality of cloud services for educational institutions. This approach is based on using a combination of quantitative and qualitative indicators that consider the reliability, performance, security, and usability of cloud services. In conclusion, the author recommends choosing a cloud provider and optimizing cloud services. Assessing the quality of cloud services is a complex process that requires a comprehensive and systematic approach. You need to consider various factors such as reliability, performance, and security, as well as your organization's individual needs and requirements.

AI-HybridChain: Picturized authentication and DRL based access control method with secure two fold revocation for ensuring cloud computing security

The Internet of Things is susceptible to seepage of private information during the data sharing. To circumvent this problem, access control and secure data sharing have been familiarized in cloud- IoT which is prevalent now days due to it storage and data access services. An intrusion detection implementation defends the veracity of the data sharing confining record are stored in a data base by recognizing when something changed unpredictably so that security and privacy are major anxieties. Therefore, it is crucial to develop the security apparatus that perceive and mitigate any feasible attack in cloud security services. In this paper, we proposed secure access control method with two-fold revocation for ensuring cloud computing security by hybrid blockchain (AI-Hybrid chain). Initially, all the users and services are registered their credentials to Secure Agent (SA) and provides image-based user authentication. Then, SA provides a long-term secret key which is generated by Improved Advanced Encryption Standard (IAES) algorithm, both user and service credentials are encrypted and stored in cloud chain. After that, gateway verifies the incoming requests are legitimate or illegitimate. For verifying the incoming packets, the proposed work utilizes Di-Fuzzy Inference System (Di-FIS) based on effective metrics. Further, legitimate request is optimally classified as sensitive and non-sensitive request for achieving secure service allocation using Tomtit Flock Optimization algorithm (TFO) based on optimal features. Once the service is allocated to users, we perform novel access control mechanism based on role and attribute of the users, the trust can be validated using Transfer Learning based Double Deep Q Network (TL-DDQN). Furthermore, virtual firewall is deployed in the cloud environment for analyzing the user storage request traffic as normal validated using Transfer Learning based Double Deep Q Network (TL-DDQN). Furthermore, virtual firewall is deployed in the cloud environment for analyzing the user storage request traffic as normal and malicious based on Contractive Residual Network (Con-ResNet). The implementation of proposed work is conducted by Network Simulator – 3.26 and the performance of the proposed AI-Hybrid chain model is itemized based on various performance metrics in terms of attack detection rate, overhead, success ratio, throughput, traffic rate, delay, response time and unauthorized access.

Secure Cloud Services for the Healthcare Industry: Addressing Unique Challenges and Ensuring Compliance

Abstract: The healthcare industry is increasingly adopting cloud computing to manage patient data, provide telehealth services, and support remote work. However, this adoption presents unique challenges due to stringent regulations governing the handling of personal health information (PHI), such as HIPAA in the United States and GDPR in the European Union. Noncompliance with these regulations can result in significant financial penalties, reputational damage, and loss of patient trust. This article explores the specific data security needs of the healthcare industry, the challenges in adopting cloud services, and the essential features of secure cloud services tailored for healthcare. It also discusses the integration of artificial intelligence (AI) and automation in cloud services to enhance data security and operational efficiency, as well as future trends in cloud computing for healthcare, such as the increasing adoption of AI and machine learning for predictive analytics and automated threat detection. By providing a comprehensive overview of the data security needs, challenges, and solutions in adopting cloud services for healthcare, this article aims to assist healthcare organizations in making informed decisions when considering the adoption of cloud services and to highlight the importance of prioritizing data security and compliance in the digital transformation of the healthcare industry.

Session password Blum–Goldwasser cryptography based user three layer authentication for secured financial transaction

Cloud computing is an eminent and evolving technology that offers various services such as data transaction, authentication, distribution and so on. In this article, session password Blum–Goldwasser Cryptography based user authentication (SPBGCUA) method is proposed to enhance the communication security of cloud services with minimum overhead. Moreover, SPBGCUA is a cryptographic technique that provides secured authentication mechanism for financial transaction with higher confidentiality rate. In order to fill the security lack of recent authentication techniques, this method develops dynamic security structure with key generation, encryption, authentication and decryption techniques. Finally, analysis is performed using SPBGCUA Method on factors such as authentication accuracy, data confidentiality rate and data integrity for number of financial data and cloud users.

Blockchain‐Based Piecewise Regressive Kupyna Cryptography for Secure Cloud Services

Cloud computing (CC) is a network‐based concept where users access data at a specific time and place. The CC comprises servers, storage, databases, networking, software, analytics, and intelligence. Cloud security is the cybersecurity authority dedicated to securing cloud computing systems. It includes keeping data private and safe across online‐based infrastructure, applications, and platforms. Securing these systems involves the efforts of cloud providers and the clients that use them, whether an individual, small‐to‐medium business, or enterprise uses. Security is essential for protecting data and cloud resources from malicious activity. A cloud service provider is utilized to provide secure data storage services. Data integrity is a critical issue in cloud computing. However, using data storage services securely and ensuring data integrity in these cloud servers remain an issue for cloud users. We introduce a unique piecewise regressive Kupyna cryptographic hash blockchain (PRKCHB) technique to secure cloud services with higher data integrity to solve these issues. The proposed PRKCHB method involves user registration, cryptographic hash blockchain, and regression analysis. Initially, the registration process for each cloud user is performed. After registering user particulars, Davies–Meyer Kupyna’s cryptographic hash blockchain generates the hash value of data in each block. When a user requests data from the server, a piecewise regression function is used to validate their identity. Furthermore, the Gaussian kernel function recognizes authorized or unauthorized users for secure cloud information transmission. The regression function results in original data by enhanced integrity in the cloud. An analysis of the proposed PRKCHB technique evaluates different existing methods implemented in Python. The results contain different metrics: data confidentiality rate, data integrity rate, authentication time, storage overhead, and execution time. Compared to conventional techniques, findings corroborate the assertion that the proposed PRKCHB technique improves data confidentiality and integrity by up to 9% and 9% while lowering storage overhead, authentication time, and execution time by 10%, 12%, and 12%.

C3S-TTP: A Trusted Third Party for Configuration Security in TOSCA-Based Cloud Services

C3S-TTP: A Trusted Third Party for Configuration Security in TOSCA-Based Cloud Services

The method of assessing the cyber security of cloud services of information infrastructure objects

The method of assessing the cyber security of cloud services of information infrastructure objects

Building a Comprehensive Trust Evaluation Model to Secure Cloud Services From Reputation Attacks

Building a Comprehensive Trust Evaluation Model to Secure Cloud Services From Reputation Attacks

A Review of Cloud Security Issues and Challenges

The advancement of information technology today depends on cloud computing. Every cloud consumer continues to search for the best services available, particularly in terms of security. The absence of uniform standards in the security architecture of the cloud, especially within the cloud computing community, has become an ongoing problem. Most cloud service providers automatically follow the best security practises and actively protect the operation of their systems. Enterprises are required to independently judge the security of their data, apps, and the cloud’s workload. The complexity of security concerns is increasing as the digital environment develops. Virtualization, multi-tenancy, security controls, rules, and risk profiling are all concepts in cloud security. DoS, service injection, user-to-root attacks, man-in-the-middle attacks, data loss leakage andloss, identity theft, and backdoor channel attacks are a few of the dangers associated with cloud computing. Cloud platforms, data outsourcing, data storage standardisation and security, data backup, and data recovery are all examples of cloud security services. Data storage, computation, untrusted computing, and virtualization security challenges are all a part of the cloud.

Investigating Challenges and Future Directions in Cloud Computing Forensics: A Comprehensive Review

Cloud computing forensics has emerged as a critical field due to the widespread adoption of cloud services in various sectors. This comprehensive review aims to investigate the challenges and future directions in cloud computing forensics. The paper identifies key obstacles such as data volatility, multi-tenancy, and jurisdictional issues that hinder effective forensic investigations in cloud environments. It also explores current methodologies, tools, and frameworks used in cloud forensics, evaluating their strengths and limitations. Furthermore, the review highlights emerging trends and proposes potential research directions to address existing gaps. By synthesizing current knowledge and identifying future prospects, this paper seeks to provide a robust foundation for advancing cloud computing forensics, ensuring improved security and reliability in cloud services.

Strategies for efficient resource management in federated cloud environments supporting Infrastructure as a Service (IaaS)

The increased market activity and visibility have resulted in a growing demand for additional computer resources to meet the needs of cloud clients. Delivering promised quality of service (QoS) while fulfilling diverse resource demands dynamically might be difficult for a single Cloud Service Provider (CSP). Federated cloud computing, also known as distributed cloud computing, is an established paradigm in which CSPs collaborate to aggregate unused resources, resulting in financial and QoS benefits. Notably, this strategy improves availability and reliability while overcoming individual CSP difficulties in preserving QoS amid fluctuations in resource demand. The federated cloud successfully leverages computing resources even during low-demand periods, demanding a comprehensive resource management strategy for Infrastructure as a Service (IaaS) inside participating CSPs. Such a strategy is crucial for preserving QoS, ensuring availability and dependability, and optimizing underutilized computing resources. This research presents the novel IaaS cloud design, revealing a unique methodology that reimagines traditional cloud systems. The proposed IaaS cloud framework investigates virtual machine migration and resource consolidation, building a strong foundation founded on IaaS principles and emphasizing the crucial role of virtualization. The technique introduces ground-breaking concepts such as Cloud User (CU) and Reputation Management, which are strengthened by specific algorithms that improve cloud service security and trust. Furthermore, the combination of Trust Manager (TM) and Broker Manager (BM) components strengthens SLA control and trust evaluation, aligning smoothly with IaaS standards to improve service quality and reliability. User Profiling, which is classified into private, social, and corporate profiles, provides a separate lens for successful cloud user management inside the IaaS landscape, allowing for customised service delivery. SMI and cutting-edge ranking algorithms, such as the Deep Q-based Algorithm, optimize cloud service provider selection and ranking—an important aspect of IaaS. The use of the Banker's algorithm and a comprehensive Service Level Agreement (SLA) Management plan provides efficient resource allocation, mirroring recognized IaaS standards. This research study not only throws light on these trailblazing techniques, but it also establishes a new standard for IaaS cloud design and resource management.

Cloud DDoS Attack Detection Model with Data Fusion & Machine Learning Classifiers

In the current situation, digital technology is a necessary component of daily life for people. During the Covid-19 pandemic, every profit and non-profit making businesses organizations moved online, which caused an exponential rise in incursions and attacks on the digital platform. The Distributed Denial of Service (DDoS) attack, which may quickly paralyse Internet-based services and applications, is one of the deadly threats to emerge. The attackers regularly update their skill tactics, which allows them to get around the current detection and protection systems. The standard detection systems are ineffective for identifying novel DDoS attacks since the volume of data generated and stored has multiplied. So, the main goal of this work is to employ data fusion applications for secure cloud services and demonstrate the detection of DDoS attacks with the applications of machine learning classifiers that can further be helpful for cloud forensic investigation process. A variety of machine learning models, including decision trees, Navies Bayes, SVM, and KNN are used to detect and classify cloud DDoS attacks. The outcomes of the experiments demonstrated that decision tree is the most feasible and better performer method to classify cloud DDoS attacks.

Rethinking Your Application Security Posture from Code to Cloud

This paper explores the importance of reevaluating the application security posture in the context of modern software development practices and the shift towards cloud computing. It highlights the challenges posed by the evolving threat landscape and emphasizes the need for a comprehensive and proactive security strategy. The paper also discusses various techniques and best practices for securing applications from the code level to the cloud environment, encompassing secure coding practices, and the effective utilization of cloud security services. By adopting a holistic approach to application security, organizations can mitigate risks and protect their applications and data throughout the software development lifecycle. Keywords: Application Security, Software Development, Cloud Computing, Secure Coding, Vulnerability Assessment, Penetration Testing, Cloud Security Services Proceedings Citation Format Afolabi, O. (2023): Rethinking Your Application Security Posture from Code to Cloud. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 35-40. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P5

Best Practices to Implement and Pitfalls to Avoid in Cloud Application Security

This paper explores the importance of reevaluating the application security posture in the context of modern software development practices and the shift towards cloud computing. It highlights the challenges posed by the evolving threat landscape and emphasizes the need for a comprehensive and proactive security strategy. The paper also discusses various techniques and best practices for securing applications from the code level to the cloud environment, encompassing secure coding practices, and the effective utilization of cloud security services. By adopting a holistic approach to application security, organizations can mitigate risks and protect their applications and data throughout the software development lifecycle. Keywords: Application Security, Software Development, Cloud Computing, Secure Coding, Vulnerability Assessment, Penetration Testing, Cloud Security Services Proceedings Citation Format Oyitso, E.J. & Ordia, E.D. (2023): Best Practices to Implement and Pitfalls to Avoid in Cloud Application Security. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 41-48. https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P6

Cloud Security: Challenges and Future Scope

Now Cloud computing becomes so much popular coz satisfying business needs efficiently. Cloud features provide best lead to organizations to effectively access data and services with less cost over the internet. This also raise issues related with cloud security. This paper presents brief information regarding cloud computing security challenges. This information includes security mechanisms that should be consider for Cloud Service Models. This Work also focus on detail knowledge of security issues, threats which may use by attacker. It also explains cloud components levels threats and attack so more secure mechanism can identified for each component. This Paper introduced classification of Cloud security areas issues to develop secure Cloud Security services in future.

Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment

Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial bee colony (ABC) named Group-ABC (G-ABC). The IDS detector has been implemented and further simulation results have been determined using the G-ABC. The evaluation has been carried out using the measures such as precision, recall, accuracy, and F-measure. Different attacks such as user to root (U2R), probe, root to local (R2L), backdoors, worms, and denial-of-service (DoS) attacks have been detected. The simulation analysis is performed using two datasets, namely, the NSL-KDD dataset and UNSW-NB15 dataset, and comparative analysis is performed against the existing work to prove the effectiveness of the proposed IDS. The objective of the work is to determine the intruder attacker system using the deep learning technique.

Improved Association Rule Mining-Based Data Sanitization for Privacy Preservation Model in Cloud

Data security in cloud services is achieved by imposing a broad range of privacy settings and restrictions. However, the different security techniques used fail to eliminate the hazard of serious data leakage, information loss and other vulnerabilities. Therefore, better security policy requirements are necessary to ensure acceptable data protection levels in the cloud. The two procedures presented in this paper are intended to build a new cloud data security method. Here, sensitive data stored in big datasets is protected from abuse via the data sanitization procedure relying on an improved apriori approach to clean the data. The main objective in this case is to generate a key using an optimization technique known as Corona-integrated Archimedes Optimization with Tent Map Estimation (CIAO-TME). Such a technique deals with both restoration and sanitization of data. The problem of optimizing the data preservation ratio (IPR), the hiding ratio (HR), and the degree of modification (DOM) is formulated and researched as well.