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Cloud computing (CC) offers online access to Network Services, in specific data collection and processing capability, with advanced, transparent user management. CC has become a more and more private and public data center set that provides the user with a shared Internet portal. A wide variety of smart devices can form an IoT network to collect an...
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... Storage is a preferred application for individuals and companies, providing cost reductions, greater productivity, speed, reliability, and configurability. Different kinds of clouds vary from one another as shown in figure 2. [7,8]. ...
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IoT is the interconnection of people and things. When our home is connected to IoT devices it is referred to as smart home. The idea behind smart home is to make life easier such that there is little human intervention. The IoT devices in our smart home exchange data for storage and processing. This exchange of data leads to users concerns on data...
Federated learning (FL) is a novel methodology aiming at training machine learning (ML) and deep learning (DL) models in a decentralized manner in order to solve three main problems seen in the artificial intelligence (AI) sector, namely, (a) model optimization, (b) data security and privacy, and (c) resource optimization. FL has been established a...
Cloud computing (CC) offers online access to Network Services, in specific data collection and processing capability, with advanced, transparent user management. CC has become a more and more private and public data center set that provides the user with a shared Internet portal. A wide variety of smart devices can form an IoT network to collect an...
The increasing adoption and use of IoT devices in smart home environments have raised concerns around the data security or privacy of smart home users. Several studies employed traditional machine learning to address the key security challenge, namely anomaly detection in IoT devices. Such models, however, require transmitting sensitive IoT data to...
Citations
... By analysing user behaviour and data trends, machine learning algorithms can detect privacy issues, allowing for preventative measures such as data anonymisation and differential privacy strategies. Implementing AI-driven strategies improves the resilience of security and data protection measures (Kavitha et al., 2021). ...
The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is transforming data and technology exchange across a number of industries, including smart cities, transportation, healthcare, and agriculture. Adoption of IoT has transformed many industries, but it has also sparked worries about security flaws in network integrity, authentication, and data privacy. AI models have proven to be a pro-active and successful means to deal with these security issues in IoT networks. AI-based solutions are essential to mitigate security risks in Internet of Things settings because they include advanced features like behaviour analysis, anomaly identification, and threat prediction. This artificial intelligence (AI)-driven solutions improve behaviour analysis, anomaly detection, and real-time threat detection, protecting networks against cyberattacks and guaranteeing data security, privacy, and confidentiality. To maximise the benefits of AI in mitigating IoT security issues and fostering confidence in IoT technology, future efforts should concentrate on overcoming current limitations and challenges in AI for IoT security, exploring potential developments and research directions, and addressing ethical and regulatory considerations.
... In [16], cloud computing (CC) provides online communication between devices, to innovative, straightforward user control and information gathering and computational power. The public cloud has evolved into a proprietary and regional contact center network that offers users a shared interactive website. ...
Bio-inspired computing approach is based on the nature and biology for solving complex real-world challenges with enhanced solutions. In this modern era smart technology, connectivity and information play a vital role in the enhancement of human daily life. In the insolent connected world, an eruption of data is increasing and it became exceedingly vulnerable to the threat surface while transfer, collection, and storage of real-time information. Through an international network, this collected information is stored in the public environment. Invaders switch and open the users’ trustworthy material without authorized access for malicious activity. And the hackers are substituting the unique information with the forged records. The collected information is exposed to the assaults, such as malware, phishing, flooding, replay, password attacks, and so on. With this new expertise, various mathematical and optimized computational methods are developed for data safety through biology and nature. Guaranteeing and comforting the security of information necessitate stimulation from the natural sphere. With this advancement, nature has developed biology and nature-based inspired techniques for resolving data security in real time through variation. For data security challenges, these techniques have various graceful solutions with high accurateness and efficacy. In this paper, we review the generous trends in bio-inspired computing methodologies for solving information security issues with extraordinary elucidations. In the future, to design a hybrid bio-inspired security model to enhance data safeguarding on various smart world applications.
... The field had a few patterns of trust, known as AI winter, which were trailed by discouraging times and subsidizing misfortunes. After profound learning beat all earlier AI strategies in 2012 and the transformer design arose in 2017, subsidizing and interest in the field soar [5]. Accordingly, there was an AI spring in the mid-2020s, when significant progressions in artificial intelligence were spearheaded generally by American organizations, scholastic establishments, and labs. ...
This paper explores the security challenges faced in multi-cloud environments and how machine learning (ML) can be used to address them. Multi-cloud environments offer businesses flexibility and scalability but also introduce complexities in securing data and applications. The paper discusses the challenges of distributed data, identity and access management, interoperability, and compliance. It then explores how multi-cloud security strategies can be implemented to mitigate these risks. Finally, the paper examines the role of machine learning in enhancing multi-cloud security by providing anomaly detection, intrusion prevention, and data encryption.
... The field had a few patterns of trust, known as AI winter, which were trailed by discouraging times and subsidizing misfortunes. After profound learning beat all earlier AI strategies in 2012 and the transformer design arose in 2017, subsidizing and interest in the field soar [5]. Accordingly, there was an AI spring in the mid-2020s, when significant progressions in artificial intelligence were spearheaded generally by American organizations, scholastic establishments, and labs. ...
This paper explores the security challenges faced in multi-cloud environments and how machine learning (ML) can be used to address them. Multi-cloud environments offer businesses flexibility and scalability, but also introduce complexities in securing data and applications. The paper discusses the challenges of distributed data, identity and access management, interoperability, and compliance. It then explores how multi-cloud security strategies can be implemented to mitigate these risks. Finally, the paper examines the role of machine learning in enhancing multi-cloud security by providing anomaly detection, intrusion prevention, and data encryption.
... Security risks, availability, confidentiality, and integrity are known as the most prevalent cloud computing risks. Security risks refer to the lack of trustworthy in cloud computing, availability risks are defined as inaccessibility of users to their data, confidentiality is related to intruders' threat that can access to user data unauthorized, and integrity risks refer to the division of records in customer data and insufficient regulation in user data (Kavitha et al., 2021). Cloud computing security risks are so much important that should be identified in both aspects of physical security to application security (Bautista-Villalpando & Abran, 2021). ...
Cloud computing has become a disruptive technology in recent years which brings numerous benefits to enterprises; however, as any other technology it is accompanied with some risks. Firms can achieve competitive advantage if they simultaneously explore new opportunity and exploit their existing knowledge and capabilities through investing on cloud computing. However, cloud computing risks can hamper these abilities. Applying the ambidexterity theoretical lens, we have provided evidence that potential cloud computing risks play a significant role on firms’ performance in two integral aspects of explorative and exploitative innovation. To address this objectives, fuzzy VIKOR technique has been deployed in which the fuzzy approach helps experts to express their opinion using linguistic variables and VIKOR technique ranks cloud computing risks considering ambidexterity criteria. The results show that how each cloud computing risk can disable firms from achieving innovative performance.
Smart villages, which use the Internet of Things (IoT), offer a viable solution to the issues that rural communities confront around the world. This study provides a complete overview of the role of IoT technology in transforming rural communities into long-term, technologically advanced centers. It investigates the fundamental elements of smart villages, such as renewable energy, digital connection, agriculture, healthcare, and community interaction. It also addresses the benefits, constraints, and future prospects of using IoT in smart village programs. This research will analyze existing literature and case studies to provide insights into the potential benefits and best practices for using IoT solutions in rural development.
Cloud computing has profoundly changed the face of data management for enterprises, providing increased scalability, ease of access, and cost savings. Nonetheless, this change has highlighted the crucial need for strengthened security measures to protect sensitive data from the ever-changing spectrum of cyber threats. Following the cloud's rise as a storehouse for large datasets, the quest for sophisticated security solutions has gained traction. This motivation has resulted in the incorporation of artificial intelligence (AI) into the cloud security architecture. As cloud storage becomes increasingly popular, organizations are becoming more concerned about data security. Sensitive data is transmitted, ranging from messages and images to financial and health information. As technology advances, there is a growing threat to customer data in the cloud, making greater cloud security more important than ever. This study aims to give a comprehensive insight into the role of AI in data storage and transmission security in the cloud.
Artificial intelligence (AI) techniques, particularly those in machine learning (ML), have been successfully applied in various areas, leading to widespread belief that AI will collectively play an important role in future wireless communications. Risks associated with the utilisation of cloud components during service delivery can be mitigated through the implementation of safety measures. Protection and efficiency are the two pillars upon which the security and scalability of cloud computing rest. AI is the study of algorithmic enhancements to the real world. The issues and worries associated with utilising one or more AI algorithms in the cloud are outlined, including supervised, unregulated, semi-controlled, and enhanced cloud safety issues. In the future framework, cutting-edge algorithms usher in a new era of cloud data security. Specifically, it is the aim of improving cloud security and privacy. Computers with AI are particularly keen on voice recognition technology, ML systems, decision-planning systems, and problem-solving systems.
