Research on Intelligent Cybersecurity Protection System in Cloud Computing Environment

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalability and efficiency but also exposing organizations to evolving cyber threats such as data breaches, insider threats, and advanced persistent threats (APTs). Traditional security mechanisms struggle to address these dynamic challenges, necessitating the integration of AI-driven threat detection and prevention strategies. This conceptual paper explores the comparative effectiveness of supervised learning, unsupervised learning, reinforcement learning, and hybrid AI models in cloud security. Supervised learning excels in identifying known attack patterns, while unsupervised learning is crucial for detecting zero-day threats and anomalies. Reinforcement learning enables self-adaptive security measures, and hybrid models offer a comprehensive, multi-layered approach to cloud security. However, AI-driven cybersecurity faces significant challenges, including data privacy risks, bias in threat detection, adversarial AI attacks, and lack of model interpretability. Emerging AI trends such as federated learning, quantum security, and explainable AI (XAI) are shaping the future of cloud security, while regulatory frameworks like GDPR, NIST AI Risk Management, and the EU AI Act play a crucial role in standardizing ethical AI use. This study provides insights into the strengths, weaknesses, and future directions of AI-driven cloud security, offering recommendations for researchers, policymakers, and cybersecurity practitioners to enhance AI resilience against emerging threats.

The number of digital health records made each day will likely keep increasing a lot. Medical images have important details that help doctors make correct diagnoses and start treatments early. Securing medical images in a cloud computing environment is important. Medical images contain sensitive patient information. This information must be protected to keep it private and safe. This paper systematically reviews the current literature from leading academic databases including (ReseachGate, ScienceDirect, Scopus) to examine need for Medical Images security in Cloud Computing Environments. The findings reveal that cloud systems store data online, which makes it easier to access but also more vulnerable to security threats. To protect medical images, strong security measures are needed. These measures include encryption, access control, and regular monitoring. Proper security ensures that only authorized personnel can view or modify the images. It also helps prevent data breaches and unauthorized access. Protecting medical images in the cloud is necessary to maintain patient confidentiality and trust. This paper reviews and classify the issues, challenges and needs for securing medical images in cloud computing environments which a hot area that needs to be addressed by future research.

Cloud computing offers cost effective services on-demand which encourage critical infrastructure providers to consider migrating to the cloud. Critical infrastructures are considered as a backbone of modern societies such as power plants and water. Information in cloud computing is likely to be shared among different entities, which could have various degrees of sensitivity. This requires robust isolation and access control mechanisms. Although various access control models and policies have been developed, they cannot fulfil requirements for a cloud based access control system. The reason is that cloud computing has a diverse sets of security requirements and unique security challenges such as multi-tenant and heterogeneity of security policies, rules and domains. This thesis provides a detailed study of cloud computing security challenges and threats, which were used to identify security requirements for various critical infrastructure providers. We found that an access control system is a crucial security requirement for the surveyed critical infrastructure providers. Furthermore, the requirement analysis was used to propose a new criteria to evaluate access control systems for cloud computing. Moreover, this work presents a new cloud based access control model to meet the identified cloud access control requirements. The model does not only ensure the secure sharing of resources among potential untrusted tenants, but also has the capacity to support different access permissions for the same cloud user. Our focused in the proposed model is the lack of data isolation in lower levels (CPU caches), which could lead to bypass access control models to gain some sensitive information by using cache side-channel attacks. Therefore, the thesis investigates various real attack scenarios and the gaps in existing mitigation approaches. It presents a new Prime and Probe cache side-channel attack, which can give detailed information about addresses accessed by a virtual machine with no need for any information about cache sets accessed by the virtual machine. The design, implementation and evaluation of a proposed solution preventing cache side-channel attacks are also presented in the thesis. It is a new lightweight solution, which introduces very low overhead (less than 15,000 CPU cycles). It can be applied in any operating system and prevents cache side-channel attacks in cloud computing. The thesis also presents a new detecting cache side-channel attacks solution. It focuses on the infrastructure used to host cloud computing tenants by counting cache misses caused by a virtual machine. The detection solutions has 0% false negative and 15% false positive.

The rapid adoption of cloud computing has transformed the way data is stored, shared, and accessed across organizationsand individuals. Despite its scalability and cost efficiency, cloud storage introduces serious security and privacy challenges, particularly when sensitive data is outsourced to third-party cloud service providers. Traditional access control mechanisms often rely on the cloud provider’s trustworthiness, which may expose data to unauthorized access, insider threats, and data breaches. To address these concerns, CryptCloud+ is proposed as a secure and expressive data access control framework that enforces fine-grained authorization directly through cryptographic techniques. CryptCloud+ focuses on ensuring data confidentiality while enabling flexible and policy-driven access control over encrypted cloud data. The system leverages encryption-based access control, where data is encrypted before being uploaded to the cloud, and only authorized users possessing valid cryptographic credentials can decrypt and access the data. This approach eliminates the need to trust the cloud service provider, as all sensitive operations related to data access are handled at the client side. A key contribution of CryptCloud+ is its support for expressive access policies, allowing data owners to define complex rules based on user attributes such as roles, departments, access levels, or time constraints. These policies enable fine grained access control without requiring data owners to manage individual permissions manually. The framework ensures that users can access only the data they are authorized for, while unauthorized users, including cloud administrators, are unable to infer any meaningful information from the encrypted data. The proposed system is implemented using Python, taking advantage of its robust cryptographic libraries, simplicity, and integration capabilities. Python-based modules handle key generation, encryption, decryption, and policy enforcement, making the system modular and easy to extend. The implementation demonstrates how secure key management and attribute-based access enforcement can be achieved efficiently in a real-world cloud environment. CryptCloud+ also addresses scalability and performance concerns by minimizing computation overhead during data access. Encryption and decryption operations are optimized to ensure that system performance remains practical even as the number of users and data files increases. Experimental results indicate that the framework maintains strong security guarantees while providing acceptable response times for authorized users. In addition to security, the system enhances usability by allowing seamless data sharing among multiple users without re encrypting data for each individual. This significantly reduces management complexity for data owners while preserving strict access control policies. The system design ensures compatibility with existing cloud storage services, making itadaptable to various deployment scenarios. Overall, CryptCloud+ presents a secure, flexible, and effiient solution for cloud data access control. By combining cryptographic enforcement with expressive policy definition and Python-based implementation, the frameworkstrengthens data security, protects user privacy, and improves trust in cloud storage systems. The proposed model demonstrates the feasibility of practical, cryptography-driven access control for modern cloud environments.

Due the evolution of technology, a new class of sophisticated threats, called Advanced persistent threats (APTs), has arisen and became a pressing problem specially for the industrial security sector. APTs ignited a fire of information warfare where they form cyber-attacks with a high-risk for very critical and secured infrastructures. They targeted a specific companies and governments. The most significant feature of APTs is their ability for bypass high-profile of security systems and steal or tamper information for doing negative impacts on physical objects. Stuxnet virus is an example of APTs that has a specific target and has an ability to destroy its physical infrastructures. It did not need Internet for spreading. It revealed in 2010 and targeted Iran’s nuclear program by exploiting four zero-day vulnerabilities in Windows Operating System. In the recent years, a number of different APTs have emerged which increased researchers’ attention to analyze them in order to find solutions to protect against existing and future APTs. In this paper, we examine the majority of existing reports and research papers of Stuxnet and APTs to give an overview of characteristics, features, and operations of each of them. We cover Duqu, Flame, Shamoon and Triton as recent variances of Stuxnet. In addition, we highlight the differences and similarities between Stuxnet and these APTs to help in predicting future attacks and encourage the researchers to find solutions to protect against them.

The integration of Zero-Trust Architecture (ZTA) and Blockchain-based Security Models in IoT-driven industrial power electronics systems has emerged as a transformative approach to mitigating cyber threats and ensuring robust access control. Traditional security mechanisms, which rely on perimeter-based defenses, are increasingly ineffective against advanced persistent threats (APTs), insider attacks, and lateral movement techniques within industrial IoT (IIoT) environments. Zero-Trust security enforces continuous verification, least-privilege access, and micro-segmentation, ensuring that no device or user was inherently trusted. Implementing ZTA in resource-constrained IoT ecosystems presents significant challenges related to computational overhead, authentication latency, and secure data transmission. To address these limitations, blockchain technology enhances decentralized identity management, immutable access logs, and tamper-resistant security frameworks, fortifying Zero-Trust-based access control. Privacy-preserving cryptographic techniques, including zero-knowledge proofs (ZKPs) and homomorphic encryption, safeguard sensitive industrial data while maintaining compliance with evolving regulatory frameworks. AI-driven anomaly detection models reinforce continuous authentication and behavior-based threat monitoring, enabling proactive defense mechanisms against zero-day exploits and sophisticated cyber intrusions. This chapter presents a comprehensive analysis of Zero-Trust implementation models for IIoT systems, highlighting the role of secure communication protocols, distributed ledger-based identity verification, and adaptive security automation. The integration of blockchain-enabled access control and AI-powered real-time security analytics ensures a resilient security posture for industrial power electronics networks, mitigating risks associated with unauthorized access, data breaches, and operational disruptions. The proposed framework enhances scalability, privacy, and computational efficiency, paving the way for next-generation cybersecure industrial ecosystems.

Cloud computing paradigm is still an evolving paradigm but has recently gained tremendous momentum due to its potential for significant cost reduction and increased operating efficiencies in computing. However, its unique aspects exacerbate security and privacy challenges that pose as the key roadblock to its fast adoption. Cloud computing has already become very popular, and practitioners need to provide security mechanisms to ensure its secure adoption. In this chapter, the authors discuss access control systems and policy management in cloud computing environments. The cloud computing environments may not allow use of a single access control system, single policy language, or single management tool for the various cloud services that it offers. Currently, users must use diverse access control solutions available for each cloud service provider to secure data. Access control policies may be composed in incompatible ways because of diverse policy languages that are maintained separately at every cloud provider. Heterogeneity and distribution of these policies pose problems in managing access policy rules for a cloud environment. In this chapter, the authors discuss challenges of policy management and introduce a cloud based policy management framework that is designed to give users a unified control point for managing access policies to control access to their resources no matter where they are stored.

In this paper, a variety of cloud service combinations is used to form the control core of a smart home, realize data forwarding, storage, and analysis in the cloud, and complete the remote management of smart home devices. This paper studies the secure access control of smart home data combined with the blockchain technology and password technology to realize the secure and efficient access control of smart home data. The system can achieve the goal of independent research and development. Aiming at the problem that the access control of a smart home is generally managed by third‐party authorized institutions and there is an unauthorized access, a blockchain‐based smart home (BSH) access control scheme is proposed. The scheme extends the attribute‐based access control model (ABAC) and applies the blockchain technology to the smart home ABAC model to realize fine‐grained access control. In the scheme, the resource provider first publishes the access control policy of the resource to the blockchain. If the resource visitor wants to access the resource, he needs to submit an access request to the blockchain and use an SM2 threshold signature to process the transaction proposal. The endorsement peer node in the blockchain runs the access control policy smart contract to decide whether to grant access rights. This scheme can ensure that resource providers can participate in all the processes of access control and avoid the risk of unauthorized access caused by the centralized management of the third party. Finally, the simulation experiment is carried out on the Hyperledger Fabric alliance chain development platform. The results show that the BSH access control scheme has good applicability in the smart home scenario.

Traditional access control models often assume that the entity enforcing access control policies is also the owner of data and resources. This assumption no longer holds when data is outsourced to a third-party storage provider, such as the cloud. Existing access control solutions mainly focus on preserving confidentiality of stored data from unauthorized access and the storage provider. However, in this setting, access control policies as well as users’ access patterns also become privacy sensitive information that should be protected from the cloud. We propose a two-level access control scheme that combines coarse-grained access control enforced at the cloud, which provides acceptable communication overhead and at the same time limits the information that the cloud learns from his partial view of the access rules and the access patterns, and fine-grained cryptographic access control enforced at the user’s side, which provides the desired expressiveness of the access control policies. Our solution handles both read and write access control.KeywordsAccess ControlAccess PatternCloud ProviderData OwnerAccess Control PolicyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The focal point of this research maneuvers over the challenges and threats that are continuously being faced in cloud computing. Cloud computing is considered as one of the most versatile technology in the recent years. It is an innovative approach towards Information Systems and it has become an integral part of the IT industry. The paper aims to highlight the seriousness of the threats and challenges encountered in cloud computing. In this paper, we give an overview on what makes this technology the future of computing. Immense literature study was conducted to gather adequate data on the recent techniques and terminologies related to the research field. An in-depth research analysis presents a competent mechanism that identifies the challenges and threats in cloud computing. The paper also refers to different security breaches that occurred over the years and what measures were taken to overcome the catastrophic loss. Along with that, it also caters the alternative strategies and approaches available to overcome the possible security threats and challenges. Different alternative methods such as Cryptography and Access Control Mechanisms are identified which are feasible and can be utilized for enhancing the security in cloud computing. Cloud computing is no doubt growing in popularity but even the immensity of this technology cannot disguise the security issues that linger with it. After thoroughly investigating and inspecting all the literary resources, gathered data and information, it is discovered that although cloud computing is tremendously gaining its popularity in the IT field, concerns about its security impact have also been raised considerably. The conclusion of this paper provides different strategies to be adopted to condense the effect of security challenges in the chosen area of the research. Keywords: Access Control, Cloud Application, Cloud Computing, Challenges, Cryptography, Data Portability, Information System, Interoperability, PaaS, SaaS, Security, Threats, Virtualization;

Detection of advanced persistent threat using machine-learning correlation analysis

This work investigates the approaches and challenges of security in cloud computing, with emphasis on cryptography, privacy, and access control. Firstly, the importance of cloud security and the main risks faced by users and service providers are explored. Next, cryptographic techniques used to protect data in transit and at rest are discussed, as well as the challenges associated with their implementation. Additionally, privacy issues related to data collection, storage, and processing in the cloud, including regulations and standards for sensitive data against unauthorized access, are addressed. It is concluded by highlighting the importance of integrated security approaches and suggesting areas for future research to enhance cloud computing security. Among the works that guide the analysis are “O Escudo da Privacidade na Era da Conectividade” (2023), by Juliane Ferreira, “What is identity and access management?” by Matthew Kosinski e Amber Forrest (2024) e “A survey on security issues in service delivery models of cloud computing” (2010), de S. Subashini and V. Kavitha.

With the increasing popularity of network technology and information concepts, Big Data (BD) and cloud computing technologies have emerged and are widely used in all industries. BD technology can fully utilize the application value of information. Cloud computing can store a large amount of data, and improve data usage efficiency, so as to make full use of the value of data. At the same time, data security issues have become increasingly important. However, due to various factors, many users are faced with data breaches and privacy violations in the BD cloud computing environment. In this case, there are some data security risks, and the key issue is to consider how to avoid these risks. By analyzing the relationship and differences between BD and cloud computing, this article studied the issues and influencing factors of Artificial Intelligence (AI) data security in the BD cloud computing environment, and proposed corresponding optimization strategies, so as to improve data security and provide users with a brand new experience. Through comparison, it could be seen that the data processing speed and legal integrity after using the optimization strategy significantly improved. Among them, data processing speed increased by 7.2% and legal integrity increased by 10.4%. BD cloud computing could effectively improve AI data security performance.

The concept of cloud computing has recently changed how hardware, software, and information are handled. However, security challenges and credibility requirements have never changed and may have increased. Protecting cloud computing and providing security for its resources and users is one of the critical challenges. As a result, most users are afraid to use their resources, because many security problems must be met. For example, authentication and reliability are major security constraints and must be provided in a cloud computing environment. There is a wide range of authentication protocols in use, but the researcher has recommended the Kerberos protocol to represent and test it in a complex environment such as a mixed cloud environment. A model has been developed to implement Kerberos authentication in a hybrid cloud computing environment to securely access the cloud computing services provided. This model is represented using the OPNET Modeler 14.5 simulation system. The network efficiency was measured before and after the hacker. Findings presented in this research are supporting the ability of the Kerberos protocol to prevent illegal access to cloud computing services, whether from within the private cloud or the public cloud. While maintaining the efficient performance of the network.

Cloud computing provides significant benefits such as scalability, cost efficiency, and flexibility in accessing computing resources. However, its adoption introduces critical security challenges for organizations, necessitating a thorough analysis of associated risks. This research identifies and categorizes the primary security threats in cloud computing, including data loss, data leakage, cyberattacks, and unauthorized access, and evaluates their impact on organizational operations, data security, and regulatory compliance, with a focus on the financial and healthcare sectors. The study reveals that cyberattacks are the most prevalent and disruptive threat, while data leakage leads to significant legal repercussions and loss of trust. Practical recommendations include adopting encryption, multi-factor authentication, and strict access controls, as well as complying with industry standards like PCI DSS for finance and HIPAA for healthcare. Additionally, a comprehensive risk management framework comprising risk identification, assessment, mitigation, and continuous monitoring is proposed to enhance organizational resilience. By implementing these strategies, organizations can securely leverage cloud computing, maintain regulatory compliance, protect sensitive data, and uphold customer trust