In recent years, with the accelerated development of social informatization, digital economy has gradually become the core force of economic growth in various countries. As the carrier for the digital economy, the number of IDCs is also increasing day by day, and their construction volume and scale are expanding. Energy consumption and carbon emissions are growing rapidly as IDCs require large amounts of electricity to run servers, storage, backup, cooling systems and other infrastructure. IDCs are facing serious challenges of energy saving and greenhouse gas emission. How to achieve green, low-carbon and high-quality development is of particular concern. This paper summarizes and classifies all the current green energy-saving technologies in IDCs, introduces AI-based energy-saving solutions for IDC cooling systems in detail, compares and analyzes the energy-saving effects of AI energy-saving technologies and traditional energy-saving technologies, and points out the advantages of AI energy-saving solutions applied in green IDCs.

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation.

With the use of AI and digital forensics, this paper outlines a complete strategy for handling security incidents in the cloud. The research is meant to improve cloud-based security issue detection and response. The results indicate the promise of this integrated strategy, with AI models improving the accuracy of issue detection and digital forensics speeding incident triage. Improved cloud security, proactive threat detection, optimized resource allocation, and conformity with legal and regulatory standards are only some of the practical consequences discussed in the paper. Advanced AI models, automated incident response, human-machine cooperation, threat intelligence integration, adversarial machine learning, compliance and legal issues, and cross-cloud security are all areas the report suggests further investigation into. In sum, this study aids in developing a more proactive and resilient strategy for handling cloud security incidents in a dynamic digital environment

Cloud computing has become increasingly popular in the modern world. While it has brought many positives to the innovative technological era society lives in today, cloud computing has also shown it has some drawbacks. These drawbacks are present in the security aspect of the cloud and its many services. Security practices differ in the realm of cloud computing as the role of securing information systems is passed onto a third party. While this reduces managerial strain on those who enlist cloud computing it also brings risk to their data and the services they may provide. Cloud services have become a large target for those with malicious intent due to the high density of valuable data stored in one relative location. By soliciting help from the use of honeynets, cloud service providers can effectively improve their intrusion detection systems as well as allow for the opportunity to study attack vectors used by malicious actors to further improve security controls. Implementing honeynets into cloud-based networks is an investment in cloud security that will provide ever-increasing returns in the hardening of information systems against cyber threats.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI s promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

Cloud computing has become increasingly popular in the modern world. While it has brought many positives to the innovative technological era society lives in today, cloud computing has also shown it has some drawbacks. These drawbacks are present in the security aspect of the cloud and its many services. Security practices differ in the realm of cloud computing as the role of securing information systems is passed onto a third party. While this reduces managerial strain on those who enlist cloud computing it also brings risk to their data and the services they may provide. Cloud services have become a large target for those with malicious intent due to the high density of valuable data stored in one relative location. By soliciting help from the use of honeynets, cloud service providers can effectively improve their intrusion detection systems as well as allow for the opportunity to study attack vectors used by malicious actors to further improve security controls. Implementing honeynets into cloud-based networks is an investment in cloud security that will provide ever-increasing returns in the hardening of information systems against cyber threats.

With the rapid development of cloud computing services and big data applications, the number of data centers is proliferating, and with it, the problem of energy consumption in data centers is becoming more and more serious. Data center energy-saving has received more and more attention as a way to reduce carbon emissions and power costs. The main energy consumption of data centers lies in IT equipment energy consumption and end air conditioning energy consumption. In this paper, we propose a data center energy-saving application system based on fog computing architecture to reduce air conditioning energy consumption, and thus reduce data center energy consumption. Specifically, the intelligent module is placed in the fog node to take advantage of the low latency, proximal computing, and proximal storage of fog computing to shorten the network call link and improve the stability of acquiring energy-saving policies and the frequency of energy-saving regulation, thus solving the disadvantages of high latency and instability in the energy-saving approach of cloud computing architecture. The AI technology is used in the intelligent module to generate energy-saving strategies and remotely regulate the end air conditioners to achieve better energy-saving effects. This solves the shortcomings of the traditional manual regulation based on expert experience with low adjustment frequency and serious loss of cooling capacity of the terminal air conditioner. According to the experimental results, statistics show that compared with the traditional manual regulation based on expert experience, the data center energy-saving application based on fog computing can operate safely and efficiently, and reduce the PUE to 1.04. Compared with the AI energy-saving strategy based on cloud computing, the AI energy-saving strategy based on fog computing generates strategies faster and with lower latency, and the speed is increased by 29.84\%.

Edge computing enables the computation and analytics capabilities to be brought closer to data sources. The available literature on AI solutions for edge computing primarily addresses just two edge layers. The upper layer can directly communicate with the cloud and comprises one or more IoT edge devices that gather sensing data from IoT devices present in the lower layer. However, industries mainly adopt a multi-layered architecture, referred to as the ISA-95 standard, to isolate and safeguard their assets. In this architecture, only the upper layer is connected to the cloud, while the lower layers of the hierarchy get to interact only with the neighbouring layers. Due to these added intermediate layers (and IoT edge devices) between the top and lower layers, existing AI solutions for typical two-layer edge architectures may not be directly applicable in this scenario. Moreover, not all industries prefer to send and store their private data in the cloud. Implementing AI solutions tailored to a hierarchical edge architecture would increase response time and maintain the same degree of security by working within the ISA-95-compliant network architecture. This paper explores a possible strategy for deploying a centralized federated learning-based AI solution in a hierarchical edge architecture and demonstrates its efficacy through a real deployment scenario.

The development of 5G, cloud computing, artificial intelligence (AI) and other new generation information technologies has promoted the rapid development of the data center (DC) industry, which directly increase severe energy consumption and carbon emissions problem. In addition to traditional engineering based methods, AI based technology has been widely used in existing data centers. However, the existing AI model training schemes are time-consuming and laborious. To tackle this issues, we propose an automated training and deployment platform for AI modes based on cloud-edge architecture, including the processes of data processing, data annotation, model training optimization, and model publishing. The proposed system can generate specific models based on the room environment and realize standardization and automation of model training, which is helpful for large-scale data center scenarios. The simulation and experimental results show that the proposed solution can reduce the time required of single model training by 76.2\%, and multiple training tasks can run concurrently. Therefore, it can adapt to the large-scale energy-saving scenario and greatly improve the model iteration efficiency, which improves the energy-saving rate and help green energy conservation for data centers.

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation.

The use of encryption for medical images offers several benefits. Firstly, it enhances the confidentiality and privacy of patient data, preventing unauthorized individuals or entities from accessing sensitive medical information. Secondly, encrypted medical images may be sent securely via unreliable networks, like the Internet, without running the danger of data eavesdropping or tampering. Traditional methods of storing and retrieving medical images often lack efficient encryption and privacy-preserving mechanisms. This project delves into enhancing the security and accessibility of medical image storage across diverse cloud environments. Through the implementation of encryption methods, pixel scrambling techniques, and integration with AWS S3, the research aimed to fortify the confidentiality of medical images while ensuring rapid retrieval. These findings collectively illuminate the security, and operational efficiency of the implemented encryption, scrambling techniques, AWS integration, and offer a foundation for advancing secure medical image retrieval in multi-cloud settings.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI s promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

Cloud computing has become increasingly popular in the modern world. While it has brought many positives to the innovative technological era society lives in today, cloud computing has also shown it has some drawbacks. These drawbacks are present in the security aspect of the cloud and its many services. Security practices differ in the realm of cloud computing as the role of securing information systems is passed onto a third party. While this reduces managerial strain on those who enlist cloud computing it also brings risk to their data and the services they may provide. Cloud services have become a large target for those with malicious intent due to the high density of valuable data stored in one relative location. By soliciting help from the use of honeynets, cloud service providers can effectively improve their intrusion detection systems as well as allow for the opportunity to study attack vectors used by malicious actors to further improve security controls. Implementing honeynets into cloud-based networks is an investment in cloud security that will provide ever-increasing returns in the hardening of information systems against cyber threats.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI s promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

LLMs face content security risks such as prompt information injection, insecure output processing, sensitive information leakage, and over-dependence, etc. By constructing a firewall for LLMs with intelligent detection strategies and introducing multi-engine detection capabilities such as rule matching, semantic computing, and AI models, we can intelligently detect and dispose of inputs and outputs of the LLMs, and realize the full-time on-line security protection of LLM applications. The system is tested on open-source LLMs, and there is a significant improvement in terms of the detection rate of insecure content.

The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (IoT) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service s architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure IoT service, mainly IoT Hub and IoT Central, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability.

The HTTP protocol is the backbone for how traffic is communicated over the Internet and between web applications and users. Introduced in 1997 with HTTP 1.0 and 1.1, HTTP has gone through several developmental changes throughout the years. HTTP/1.1 suffers from several issues. Namely only allowing a one-to-one connection. HTTP/2 allowed for multiplexed connections. Additionally, HTTP/2 attempted to address the security issues that were faced by the prior version of HTTP by allowing administrators to enable HTTPS, as well as enable certificates to help ensure the encryption and protection of data between users and the web application. One of the major issues HTTP/2 faces is that it allows users to have multiplexed connections, but when there is an error and data needs to be retransmitted, this leads to head of line blocking. HTTP/3 is a new protocol that was proposed for formalization to the IETF in June of 2022. One of the first major changes is that unlike prior versions of HTTP that used the TCP/IP method of networking for data transmission, HTTP/3 uses UDP for data transmission. Prior research has focused on the protocol itself or investigating how certain types of attacks affect your web architecture that uses QUIC and HTTP/3. One area lacking research in this topic is how to secure web architecture in the cloud that uses this new protocol. To this end, we will be investigating how logging can be used to secure your web architecture and this protocol in the cloud.

With the rapid growth in information technology and being called the Digital Era, it is very evident that no one can survive without internet or ICT advancements. The day-to-day life operations and activities are dependent on these technologies. The latest technology trends in the market and industry are computing power, Smart devices, artificial intelligence, Robotic process automation, metaverse, IOT (Internet of things), cloud computing, Edge computing, Block chain and much more in the coming years. When looking at all these aspect and advancements, one common thing is cloud computing and data which must be protected and safeguarded which brings in the need for cyber/cloud security. Hence cloud security challenges have become an omnipresent concern for organizations or industries of any size where it has gone from a small incident to threat landscape. When it comes to data and cyber/ cloud security there are lots of challenges seen to safeguard these data. Towards that it is necessary that everyone must be aware of the latest technological advancements, evolving cyber threats, data as a valuable asset, Human Factor, Regulatory compliance, Cyber resilience. To handle all these challenges, security and risk prediction framework is proposed in this paper. This framework PRCSAM (Predictive Risk and Complexity Score Assessment Model) will consider factors like impact and likelihood of the main risks, threats and attacks that is foreseen in cloud security and the recommendation of the Risk management framework with automatic risk assessment and scoring option catering to Information security and privacy risks. This framework will help management and organizations in making informed decisions on the cyber security strategy as this is a data driven, dynamic \& proactive approach to cyber security and its complexity calculation. This paper also discusses on the prediction techniques using Generative AI techniques.

Cloud computing has become increasingly popular in the modern world. While it has brought many positives to the innovative technological era society lives in today, cloud computing has also shown it has some drawbacks. These drawbacks are present in the security aspect of the cloud and its many services. Security practices differ in the realm of cloud computing as the role of securing information systems is passed onto a third party. While this reduces managerial strain on those who enlist cloud computing it also brings risk to their data and the services they may provide. Cloud services have become a large target for those with malicious intent due to the high density of valuable data stored in one relative location. By soliciting help from the use of honeynets, cloud service providers can effectively improve their intrusion detection systems as well as allow for the opportunity to study attack vectors used by malicious actors to further improve security controls. Implementing honeynets into cloud-based networks is an investment in cloud security that will provide ever-increasing returns in the hardening of information systems against cyber threats.

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation.

The world has seen a quick transition from hard devices for local storage to massive virtual data centers, all possible because of cloud storage technology. Businesses have grown to be scalable, meeting consumer demands on every turn. Cloud computing has transforming the way we do business making IT more efficient and cost effective that leads to new types of cybercrimes. Securing the data in cloud is a challenging task. Cloud security is a mixture of art and science. Art is to create your own technique and technologies in such a way that the user should be authenticated. Science is because you have to come up with ways of securing your application. Data security refers to a broad set of policies, technologies and controls deployed to protect data application and the associated infrastructure of cloud computing. It ensures that the data has not been accessed by any unauthorized person. Cloud storage systems are considered to be a network of distributed data centers which typically uses cloud computing technologies like virtualization and offers some kind of interface for storing data. Virtualization is the process of grouping the physical storage from multiple network storage devices so that it looks like a single storage device.Storing the important data in the cloud has become an essential argument in the computer territory. The cloud enables the user to store the data efficiently and access the data securely. It avoids the basic expenditure on hardware, software and maintenance. Protecting the cloud data has become one of the burdensome tasks in today’s environment. Our proposed scheme "Certificateless Compressed Data Sharing in Cloud through Partial Decryption" (CCDSPD) makes use of Shared Secret Session (3S) key for encryption and double decryption process to secure the information in the cloud. CC does not use pairing concept to solve the key escrow problem. Our scheme provides an efficient secure way of sharing data to the cloud and reduces the time consumption nearly by 50 percent as compared to the existing mCL-PKE scheme in encryption and decryption process.Distributed Cloud Environment (DCE) has the ability to store the da-ta and share it with others. One of the main issues arises during this is, how safe the data in the cloud while storing and sharing. Therefore, the communication media should be safe from any intruders residing between the two entities. What if the key generator compromises with intruders and shares the keys used for both communication and data? Therefore, the proposed system makes use of the Station-to-Station (STS) protocol to make the channel safer. The concept of encrypting the secret key confuses the intruders. Duplicate File Detector (DFD) checks for any existence of the same file before uploading. The scheduler as-signs the work of generating keys to the key manager who has less task to complete or free of any task. By these techniques, the proposed system makes time-efficient, cost-efficient, and resource efficient compared to the existing system. The performance is analysed in terms of time, cost and resources. It is necessary to safeguard the communication channel between the entities before sharing the data. In this process of sharing, what if the key manager’s compromises with intruders and reveal the information of the user’s key that is used for encryption. The process of securing the key by using the user’s phrase is the key concept used in the proposed system "Secure Storing and Sharing of Data in Cloud Environment using User Phrase" (S3DCE). It does not rely on any key managers to generate the key instead the user himself generates the key. In order to provide double security, the encryption key is also encrypted by the public key derived from the user’s phrase. S3DCE guarantees privacy, confidentiality and integrity of the user data while storing and sharing. The proposed method S3DCE is more efficient in terms of time, cost and resource utilization compared to the existing algorithm DaSCE (Data Security for Cloud Environment with Semi Trusted Third Party) and DACESM (Data Security for Cloud Environment with Scheduled Key Managers).For a cloud to be secure, all of the participating entities must be secure. The security of the assets does not solely depend on an individual s security measures. The neighbouring entities may provide an opportunity to an attacker to bypass the user s defences. The data may compromise due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. Cloudsim allows to create a network that contains a set of Intelligent Sense Point (ISP) spread across an area. Each ISPs will have its own unique position and will be different from other ISPs. Cloud is a cost-efficient solution for the distribution of data but has the challenge of a data breach. The data can be compromised of attacks of ISPs. Therefore, in OSNQSC (Optimized Selection of Nodes for Enhanced in Cloud Environment), an optimized method is proposed to find the best ISPs to place the data fragments that considers the channel quality, distance and the remaining energy of the ISPs. The fragments are encrypted before storing. OSNQSC is more efficient in terms of total upload time, total download time, throughput, storage and memory consumption of the node with the existing Betweenness centrality, Eccentricity and Closeness centrality methods of DROPS (Division and Replication of Data in the Cloud for Optimal Performance and Security).

Blockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest.

Cloud computing is a nascent paradigm in the field of data technology and computer science which is predicated on the use of the Internet, often known as the World Wide Web. One of the prominent concerns within this field is the security aspects of cloud computing. Contrarily, ensuring the preservation of access to the protection of sensitive and confidential information inside financial organizations, banks and other pertinent enterprises holds significant significance. This holds significant relevance. The efficacy of the security measures in providing assurance is not infallible and can be compromised by malevolent entities. In the current study, our objective is to examine the study about the security measures through the use of a novel methodology. The primary objective of this research is to investigate the subject of data access in the realm of cloud computing, with a particular emphasis on its ramifications for corporations and other pertinent organizations. The implementation of locationbased encryption facilitates the determination of accurate geographical coordinates. In experiment apply Integrated Location Based Security using Multi objective Optimization (ILBS-MOO) on different workflows and improve performance metrics significantly. Time delay averagely approximates improvement 6-7\%, storage 10-12\% and security 8-10\%.

Cloud computing allows us to access available systems and pay for what we require whenever needed. When there is access to the internet, it uses some techniques like Service-Oriented Architecture (SOA), virtualization, distributed computing, etc. Cloud computing has transformed the way people utilize and handle computer services. It enables sharing, pooling, and accessing resources on the Internet. It offers tremendous advantages that enhance the cost-effectiveness and efficiency of organizations, which is marked by security challenges or threats that can compromise data, service safety and privacy. This paper gives an overview of cloud computing and explores the threats and vulnerabilities related to cloud computing with its countermeasures. It also explores the recent advancement in cloud computing threats and countermeasures. Further, this paper highlights the case studies on recent attacks and vulnerabilities which are compromised. Finally, this paper concludes that cloud computing is efficiently used to mitigate the threats and vulnerabilities with its countermeasures.

In the rapidly evolving technological landscape, securing cloud computing environments while optimizing resource allocation is of paramount importance. This research study introduces a novel approach that seamlessly integrates deep learning with a nature-inspired optimization algorithm for achieving joint security and resource allocation. The proposed methodology harnesses the power of ResNet, a proven deep learning architecture, to bolster cloud security by identifying and mitigating threats effectively. Complementing this, the Flower Pollination Algorithm (FPA), inspired by natural pollination processes, is employed to strike an optimal balance between resource utilization and cost efficiency. This amalgamation creates a robust framework for managing cloud resources, ensuring the confidentiality, integrity, and availability of data and services, all while maintaining efficient resource allocation. The approach is flexible, adaptive, and capable of addressing the dynamic nature of cloud environments, making it a valuable asset for organizations seeking to enhance their cloud security posture without compromising on resource efficiency.