Search

Users can purchase virtualized computer resources using the cloud computing concept, which is a novel and innovative way of computing. It offers numerous advantages for IT and healthcare industries over traditional methods. However, a lack of trust between CSUs and CSPs is hindering the widespread adoption of cloud computing across industries. Since cloud computing offers a wide range of trust models and strategies, it is essential to analyze the service using a detailed methodology in order to choose the appropriate cloud service for various user types. Finding a wide variety of comprehensive elements that are both required and sufficient for evaluating any cloud service is vital in order to achieve that. As a result, this study suggests an accurate, fuzzy logic-based trust evaluation model for evaluating the trustworthiness of a cloud service provider. Here, we examine how fuzzy logic raises the efficiency of trust evaluation. Trust is assessed using Quality of Service (QoS) characteristics like security, privacy, dynamicity, data integrity, and performance. The outcomes of a MATLAB simulation demonstrate the viability of the suggested strategy in a cloud setting. [ABSTRACT FROM AUTHOR]

In the era of cloud computing, cybersecurity has assumed paramount importance. As organizations transition to cloud-based solutions, cyberattackers increasingly target cloud services as a lucrative avenue for unauthorized access to sensitive information. The traditional security perimeter, once robust, now exhibits porosity, necessitating a reevaluation of security strategies to counter these evolving threats. This paper delves into the critical role of artificial intelligence (AI) within zero trust security technologies. The convergence of AI and zero trust has garnered significant attention, particularly in the domains of security enhancement, risk mitigation, and the redefinition of trust paradigms. My exploration aims to uncover how AI actively observes and supports various technologies in zero trust model. By evaluating existing research findings, I illuminate the transformative potential of AI in fortifying security within zero trust security models. This scholarly perspective underscores the critical interplay between AI and zero trust technologies, highlighting their collective potential in safeguarding digital ecosystems. [ABSTRACT FROM AUTHOR]

Cloud computing technology delivers services, resources, and computer systems over the internet, enabling the easy modification of resources. Each field has its challenges, and the challenges of data transfer in the cloud pose unique obstacles for forensic analysts, making it necessary for them to investigate and adjust the evolving landscape of cloud computing. This is where cloud forensics emerges as a critical component. Cloud forensics, a specialized field within digital forensics, focuses on uncovering evidence of exploitation, conducting thorough investigations, and presenting findings to law enforcement for legal action against perpetrators. This paper examines the primary challenges encountered in cloud forensics, reviews the relevant literature, and analyzes the strategies implemented to address these obstacles. [ABSTRACT FROM AUTHOR]

Cloud storage is a utility where data is remotely stored cloud environment and then the data is accessible to end users over internet. It permits the client to collect the files through online and access from anywhere via internet. The main objective of the cloud storage is to store the data safely in the Cloud space and fetch the data whenever requested by the client. In this research analysis the IoT devices collected data (IoT-PCM) are store into the cloud and access the data from the cloud. Here we are used the private cloud (AWS) for data storage purpose and the IoT devices collects the patient health data. In proposed PCMAE (Patient Care Monitoring based Authorized Encryption) technique it provides a secured way to view the health data, decrypting with verified secret key before downloading. Main work on how the doctors retrieval the IoT-PCM health data. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field. [ABSTRACT FROM AUTHOR]

The widespread use of cloud computing has brought cloud security to the forefront. The cloud provider and the firm assume varying degrees of joint responsibility for cloud security with cloud service models including IaaS, PaaS, and SaaS, to defend the strategic hacker. This paper builds a game-theoretical model to study cloud security management, in which we find that ignoring the strategic hacker leads to the dislocation security investment decisions (overinvestment or underinvestment) for the provider and the firm in bilateral refund contracts (BRCs). The strategic hacker's attack effort is inverse U-shaped with cloud service models, leading to a free-riding problem between the provider and the firm. Furthermore, from the perspective of social welfare maximization, both the provider and the firm would underinvest or overinvest in cloud security. To solve the problem, we propose two new contract mechanisms: one is an internal effort-based contract, in which the provider oversees the firm internally and the compensation rate depends on the firm's effort once the breach occurs. The other is an external effort-based contract, in which the monitoring agency supervises the efforts of the provider and the firm. We compare the two new contracts with BRCs and obtain the optimal choice for principals. [ABSTRACT FROM AUTHOR]

The proliferation of IoT services has spurred a surge in network attacks, heightening cybersecurity concerns. Essential to network defense, intrusion detection and prevention systems (IDPSs) identify malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, infiltration, and Heartbleed. This study focuses on leveraging unsupervised learning for training detection models to counter these threats effectively. The proposed method utilizes basic autoencoders (bAEs) for dimensionality reduction and encompasses a three-stage detection model: one-class support vector machine (OCSVM) and deep autoencoder (dAE) attack detection, complemented by density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. Accurately delineated clusters aid in mapping attack tactics. The MITRE ATT&CK framework establishes a "Cyber Threat Repository", cataloging attacks and tactics, enabling immediate response based on priority. Leveraging preprocessed and unlabeled normal network traffic data, this approach enables the identification of novel attacks while mitigating the impact of imbalanced training data on model performance. The autoencoder method utilizes reconstruction error, OCSVM employs a kernel function to establish a hyperplane for anomaly detection, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determining cluster count, ensuring scalability, and minimizing false positives and false negatives. Evaluated on standard datasets such as CIC-IDS2017 and CSECIC-IDS2018, the proposed model outperforms existing state of art methods. Our approach achieves accuracies exceeding 98% for the two datasets, thus confirming its efficacy and effectiveness for application in efficient intrusion detection systems. [ABSTRACT FROM AUTHOR]

Abstract Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

Cloud Computing offers on-demand infrastructure, platform and software services over the Internet on a pay-as-you-use model. Many e-commerce businesses and social networking sites are moving towards the cloud. Security breaches have also started to grow along with their popularity. The malicious processes can target virtualization-based cloud infrastructure and harm virtual resources, thereby becoming a threat to the cloud. Several Intrusion Detection Systems (IDSs) have been developed to detect attacks based on predefined behavior patterns. However, malicious processes can hide their behavior in the presence of security mechanisms on a Virtual Machine (VM). With the increasing frequency of such attacks, IDSs must be improved using Machine Learning (ML) and Deep Learning (DL) techniques. This study proposes a novel intrusion detection framework that can detect known and unknown attacks by system call sequence analysis. The framework analyzes the system call sequences of VMs with a hybrid model of Long Short Term Memory (LSTM) and system call frequency-based anomaly detection techniques. The proposed framework is evaluated using the Australian Defence Force Academy-Linux Dataset (ADFA-LD) dataset. Compared to the existing frameworks, the highest accuracy of 97.2% and a false positive rate of 2.4% are achieved for our proposed framework. [ABSTRACT FROM AUTHOR]

The economics of cloud computing result from amassing computation resources while being able to distribute workload in space and time. The backbone of this ability is virtualization, which abstracts the host hardware, sharing it through virtual machines. This means of interface is also a primary vehicle and target for attackers. The counter-measures to this threat consider the costs and benefits to the cloud's essential functions. Where the future development of the cloud is also considered, this competition between attackers and victims can be modeled in extended game theory. Yet, the attacker and victim costs and benefits, expressed as measures of expense and utility, necessary for game-theory methods are elusive. This paper establishes such a game as a predator–prey contest played out on a data-center environment. A set of contestant parameters are applied at the threshold of a viable model to the characteristic boundaries. Measurement of system health is extracted in relief with individual cost and benefit then contrasted to risk. An examination of metrics capable of validating extended interaction is found to demonstrate variation on three orders of magnitude. [ABSTRACT FROM AUTHOR]

Sensor clouds are formed by IP-enabled wireless sensors and Internet of Things devices that are used for sensing and actuation in commercial and industrial applications. Data collected by the sensors are consolidated by distributed cloud data consolidation (DCS) servers to be utilized as raw sensory information by applications running data analytics and actuation functions. Alternatively, DC servers may feed sensor data to the cloud-hosted Big Data Analytics (BDS) servers. Sensor clouds and their respective DCS servers, as well as BDS servers, may form different security realms. These security realms' ownership structures are complicated and differ from standard database servers, necessitating a dependable authentication technique to provide trusted access to DC and BDS servers. This paper proposes a new multiparty authentication framework to authenticate applications requesting access to the DCS and BDS servers without direct human or application access to the sensors and actuators. Only DC servers are permitted to communicate with sensors/actuators, and only applications certified by a Session Authority Cloud are granted access to DCS/BDS servers via an authentication protocol that includes many information and key exchanges. This solution may assure the reliable deployment of sensor clouds in different critical application domains (i.e., industry, commercial, national security, and defense, etc.) while reducing the potential of direct espionage of sensed/actuated systems. Linear Temporal Logic is used to explicitly analyze and establish the correctness of the presented framework. OPNET modeling and simulations are used to illustrate the protocol's design and operations. The results demonstrate that multiparty authentication is conceivable for Sensor cloud computing systems. [ABSTRACT FROM AUTHOR]

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security. [ABSTRACT FROM AUTHOR]

To address the concerns about security and privacy in cloud-based data storage, this study proposes a unique approach to secure cloud-based data governance using hybrid machine learning architectures. With the increasing reliance on cloud resources and the need for improved security measures, this research aims to provide a solution that enhances data access governance while ensuring data privacy. To achieve this, the study introduces a Distributed Data Service (DDS) specifically designed for collecting and analyzing data in Internet of Things (IoT) contexts. The DDS enables various IoT middleware methods to exchange common data services from a loosely coupled provider. The functional specifications of the DDS are revised to facilitate effective data gathering, filtering, and storage in this environment. The proposed approach leverages distributed cloud architecture and hybrid machine learning techniques to establish cloud data access governance with enhanced security. A privacy-aware architecture is built upon distributed middleware services and supply chain quantum neural networks to ensure data privacy and protection. Additionally, multi-agent federated Bayes vector-based blockchain and partial homomorphic encryption techniques are employed to enhance network security. Throughout the experimental investigation, several performance metrics are considered. Scalability, latency, quality of service, data integrity, and time consumption are analyzed to assess the efficiency of the proposed approach. By addressing security and privacy concerns in cloud-based data storage through the utilization of hybrid machine learning architectures and distributed cloud infrastructure, this research provides a novel approach to cloud data access governance. Overall, the study offers a comprehensive solution to secure and privacy-aware data management in the cloud, paving the way for further advancements in this field. [ABSTRACT FROM AUTHOR]