An algorithmic approach to improving cloud security: The MIST and Malachi algorithms

Cloud computing offers storage, infrastructure, computing, networking, databases, platform, software, and analytics services over the Internet. It provides numerous benefits including scalability, cost management, broad access to resources, elasticity, resource pooling. Though cloud computing is mature and widely adopted computing model in software as well as non-software industries, it has several issues regarding security as it provides most of the cloud services over the public infrastructure. Denial of service (DOS), malware injection, insecure APIs, data loss, data breaches, hypervisor vulnerabilities, VM escape are a few major issues in cloud computing. In this paper, authors tried to provide a comprehensive analysis of critical security issues in cloud computing. Furthermore, this paper critically analyses the existing solutions to the various security issues in the cloud computing model.

The development of Cloud Computing is growing and becoming a trend that is widely used in the world of telecommunications today. In education, cloud computing becomes something that is needed to support the teaching and learning process. We analyze and design the network infrastructure in schools to implement cloud computing in local area schools. Then we built a system to create a server for cloud computing that can be accessed through the wired network and wireless network. Once the server cloud computing is installed and then we test functions such as uploading and downloading files whether to run well. The next step is to perform the test by doing DOS (Denial Of Service). The results show when no server DoS attack goes well with a large throughput. However, after the DoS attacks decreased throughput value and the greater the number of attacks is increasingly reducing the throughput of the server cloud. This shows that Cloud Server that have been built are still vulnerable to DoS attacks.

PurposeThis study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.Design/methodology/approachIn this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.FindingsThe outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.Research limitations/implicationsThis study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.Practical implicationsThis study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.Originality/valueAPI recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.

An artificial intelligence (AI)-based smart Internet protocol (IP) camera system for examination monitoring has been designed, realized, and rigorously tested under realistic environment. The purpose of the proposed IP cameras is to carry out real-time remote surveillance of the examination hall as well as store corresponding videos recordings in cloud storage. Internet-of-things (IoT) technology is used to remote access of the IP cameras and real-time video streaming on web-browser of any computer/smart phone connected to either internal or external network; a dedicated cloud service (named “remoteit”) is used for this purpose. Practically, 640 × 480 sq. pixels with 30–35 frames per second (fps) video capturing are used for the online streaming. On the other hand, compressed video size of 320 × 240 sq. pixels with 30–35 fps is used for recording and storing in cloud. In addition, a custom TensorFlow lite model has been built for detecting undesirable chaos in the examination hall, and a bulk short messaging service (SMS) application programming interface (API) is used to send real-time alert to the competent authority in order to initiate necessary action. In future, the proposed system may be customized for various other sectors for security and alerting purposes.

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.

Cloud computing and big data: Technologies and applications

Abstract— In the evolving digital landscape, cloud computing has become a cornerstone for scalable and flexible IT infrastructure. However, the rapid adoption of cloud services introduces a wide range of security threats, compliance challenges, and operational risks. This paper, From Threats to Trust: Securing Cloud Infrastructure through Resilience and Compliance presents a comprehensive analysis of cloud security challenges and explores effective mitigation strategies to enhance resilience and regulatory adherence. The study examines critical issues such as data breaches, insider threats, vendor lock-in, insecure interfaces, and distributed denial-of-service (DDoS) attacks within cloud environments. It further highlights the importance of robust security mechanisms, including identity and access management, encryption techniques, continuous monitoring, intrusion detection systems, and secure key management. Emphasis is also placed on compliance with global data protection regulations and the implementation of governance frameworks. By integrating best practices with proactive risk management approaches, this paper provides a structured framework for building secure, resilient, and compliant cloud infrastructures. The findings aim to support organizations in strengthening their security posture, ensuring data integrity and availability, and fostering trust in cloud-based systems. Keywords— Cloud Security, Cloud Computing, Identity and Access Management and Intrusion Detection Systems.

Cloud computing is the next stage in evolution of the internet, which provides large amount of computing and storage to customers provisioned as a service over the internet. However, cloud computing facing so many security challenges due to the possible compromise or byzantine failures. In this paper, we focus on Ensuring data storage security in cloud computing, which is an important aspect of Quality of Service (QoS). We propose an effective and flexible distribution verification protocol to address data storage security in cloud computing. In this protocol, we rely on erasure code for the availability, reliability of data and utilize token pre-computation using Sobol Sequence to verify the integrity of erasure coded data rather than Pseudorandom Data in existing system. Unlike prior works, our scheme provides more security to user data stored in cloud computing. The performance analysis shows that our scheme is more secure than existing system against Byzantine failure, unauthorized data modification attacks, and even cloud server colluding attacks.

Cloud computing security (sometimes referred to simply as security) is an evolving sub- domain of Computer security, Network security and, more broadly, Information security It refers to a broad set of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing. Cloud security is not to be confused with security software offerings that are cloud-based (a.k.a. security-as-a-service).There are a number of security issues/concerns associated with cloud computing but these issues fall into two broad categories: Security issues faced by cloud providers (organizations providing software, platform, or infrastructure as a service via the cloud) and security issues faced by their customers. In most cases, the provider must ensure that their infrastructure is secure and that their clients' data and applications are protected while the customer must ensure that the provider has taken the proper security measures to protect their information. The extensive use of virtualization in implementing cloud infrastructure brings unique security concerns for customers or tenants of a public cloud service. Virtualization alters the relationship between the OS and underlying hardware - be it computing, storage or even networking. This introduces an additional layer - virtualization - that itself must be properly configured, managed and secured .Specific concerns include the potential to compromise the virtualization software, or hyper visor. While these concerns are largely theoretical, they do exist. Cloud computing offers great potential to improve productivity and reduce costs, but at the same time it possesses many security risks. In this paper we identify the possible security attacks on clouds including: Denial of Service attack, Authentication attack Man-in-the Middle attack, Wrapping attacks, Malware-Injection attacks, Flooding attacks, Browser attacks, and also Accountability checking problems. We identify the root causes of these attacks and propose specific solutions. In this paper, the authors discuss security issues for cloud computing and present a layered framework for secure clouds and then focus on two of the layers, i.e., the storage layer and the data layer. In particular, the authors discuss a scheme for secure third party publications of documents in a cloud. Next, the paper will converse secure federated query processing with map Reduce and Hardtop, and discuss the use of secure co-processors for cloud computing. Finally, the authors discuss XACML implementation for Hardtop and discuss their beliefs that building trusted applications from untrusted components will be a major aspect of secure cloud computing. Keywords : Accountability, Authentication attacks, Computer security, Cloud Computing, Flooding attacks, Hypervisor, Virtualization, Wrapping attacks, Browser attacks, secure clouds.

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.

Cyber-attacks on Application Programming Interfaces (APIs) have become extremely advanced and sophisticated, posing novel challenges in securing APIs. This has generated a dire need to use equally sophisticated cyber security tools for protection. APIs have become unarguably indispensable in connecting disparate software application systems both within and outside an enterprise. APIs help to move data effectively and even help organizations generate revenue by selling data and services. These factors have significantly surged the number of APIs that are being built, consequently increasing the cyber attack exposure for the companies, exposing them over the web for bad actors to exploit. Attackers often exploit numerous vulnerabilities in APIs left behind due to poor cyber security practices during implementation or hosting. The vulnerabilities enable bad actors to gain unauthorized access to sensitive data and systems within an organization. What has worked as fuel to the fire is the easy availability of malicious no-code type software and tools that can launch automated attacks, bypass standard security measures in place, stay completely undetected, and sometimes even be untraced from intrusion detection systems. There is a gap in current research on these topics which only highlights the necessity to implement some basic cyber defense mechanisms but does not specifically highlight the role and usage of some advance tools like WAAP, WAF, TLS & mTLS, which help bolster API security. This study aims to examine and present these advanced protection tools available to defend against complicated modern cyber-attacks and establish an approach to how organizations can implement these security measures to protect APIs.

Technological advancements such as the Internet of Things, mobile technology, and cloud computing are embraced by organizations, individuals, and society. The world is becoming more reliant on open networks, which fosters global communication and cloud technologies like Amazon Web Services to store sensitive data and personal information. This changes the danger landscape and opens new opportunities. As the number of people who use the Internet grows, so does the number of cyber risks and data security challenges that hackers pose. A cybersecurity threat is an action that aims to destroy or damage data, steal data, or otherwise disrupt digital life. Computer viruses, data breaches, and Denial of Service (DoS) assaults are all examples of cyber dangers. We'd be witnessing a notion of scanning enormous volumes of data across the internet if we described how AI systems might discover where hacks came from and recommend solutions to decision-makers within the corporation.

Cloud computing creates exciting opportunities like reduced costs and flexibility to the users. It also comprises of some risks like data security within the cloud. Several common security threats like data leakage, insecure API's, and malicious inside users are applicable to cloud computing environment as well. In this paper, the authors consider a cloud computing service where multiple Virtual Machines (VM's) are co-located on the same physical server. In such systems, physical resources are transparently shared by the VMs belonging to multiple users. In systems like these, a malicious user having control of a VM can try to gain control over other VM's resources or utilize all system resources leading to denial of resource attack over other VM users. A malicious user can also try to steal the data of other users located on the same server by compromising hypervisor file system (logical volumes). In this paper, security threats associated with cloud computing environment are evaluated. Authors also explore how such co-existent of VM's can be exploited to gain access over other user's data or deny service and propose constructive security measures that can be deployed to avoid such attacks.

With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable cloud system. Several standards have been proposed to address this challenge and provide a unified interface to cloud resources. The Open Cloud Computing Interface (OCCI) thereby focuses on the standardization of a common API for Infrastructure-as-a-Service (IaaS) providers, while the Topology and Orchestration Specification for Cloud Applications (TOSCA) focuses on the standardization of a template language to enable the proper definition of the topology of cloud applications and their orchestrations on top of a cloud system. TOSCA thereby does not define how the application topologies are created on the cloud. Therefore, we analyze the conceptual similarities between the two approaches and we study how we can integrate them to obtain a complete standard-based approach to manage both Cloud Infrastructure and Cloud application layers. We propose an automated extensive mapping between the concepts of the two standards, and we provide TOSCA Studio, a model-driven tool chain for TOSCA that conforms to OCCI. TOSCA Studio allows to graphically design cloud applications as well as to deploy and manage them at runtime using a fully model-driven cloud orchestrator based on the two standards. Our contribution is validated by successfully transforming and deploying three cloud applications: WordPress, Node Cellar and Multi-Tier.

This article explores the role of Machine learning in predicting and preventing future cyber-attacks, examining the techniques, applications, Benefits, Challenges, and Future treads in leveraging machine learning for proactive defence. Machine learning has remerged as a powerful tool to enhance cyber security strategies, offering the ability to predict, detect and prevent cyber-attacks with greater accuracy and efficiency. The popularity and usage of cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. Machine learning algorithms can learn from historical attack data, enabling the prediction of future threats and the development of more effective defence mechanisms. Moreover, AI enhanced Authentication and access control mechanisms bolster identity management, reducing the risk of unauthorized access and data breaches. Machine learning examines how ML Models, such as supervised and unsupervised learning, and anomaly detection, are utilized to identify cyber threats, enhance risk assessment and enable proactive security strategies. With the increasing demand and popularity in the usage of cloud computing, there has been a necessity to prevent common attacks and security threats to cloud computing services. Over the past few years, ML techniques have been shown to prevent as well as detect security attacks on the cloud. In this paper, we provide a comprehensive and systematic literature review on the use of ML in cloud security and its applications and techniques to prevent security issues on cloud computing.