Cloud Computing and Security in the IoT Era

IRJET, 2021

The trust evaluation systems, all feedback ratings on a service are usually exploited to evaluate their reputation. Although these methods are simple and effective, they cannot cope well with the impact of malicious users to improve the accuracy of the reputation assessment model of cloud services and mitigate the impact of reputation attacks initiated by malicious users. Internet of Things (IoT) plays a vital role almost in every field of engineering. Now days, almost every system has adopted this technology due to its ease in access, design and development. However the technology still suffers from the issues of available resources for computing of huge amount of IoT data. In order to solve these issues, it is necessary to adopt trustworthy cloud based architecture. The trust level calculation of these cloud services is a challenging task. In this paper, we have developed a triple integrated assessment for the trust evaluation of a cloud network. This assessment has been carried out using the three major parameters i.e. security, privacy and reputation. Security assessment of the cloud service has been carried out using the security metrics like security controls deliverable. The privacy assessment is evaluated using the Privacy Impact Assessment(PIA) tool. Finally the reputation assessment of the cloud network is carried out using the reputation of it's cloud services. Experiments are carried out on different real-world web service datasets which shows that the proposed assessment model works efficiently than all other assessment models.

2024

In light of the rapid expansion of IoT deployments and the increasing reliance on cloud services, understanding and addressing the security implications of IoT cloud integration have become paramount. The shift towards centralized storage and processing of IoT data in cloud environments offers scalability and flexibility but also exposes sensitive information to potential threats. Thus, it becomes imperative to analyze the security posture of IoT cloud architectures comprehensively. This paper explores how IoT devices and cloud systems work together, showing the weaknesses and ways attackers could target them. Through a detailed examination of security challenges such as data breaches, unauthorized access, and inadequate encryption, the paper provides insights into the multifaceted nature of IoT cloud security. Moreover, it underscores the importance of proactive measures such as robust encryption protocols, access controls, and continuous monitoring to safeguard IoT deployments against evolving cyber threats. This paper aims to help stakeholders and practitioners make better decisions and take effective security measures to protect IoT ecosystems by enhancing their understanding of IoT cloud security dynamics.

Expert Clouds and Applications, 2021

The current Internet of things (IoT) technologies will also have a profound economic, industrial and social influence on society. Teh ability of nodes to participate in IoT networks is generally resource-constrained, that also allows them to attract objectives for cyber threats. An extensive research has been conducted to address the issues of security and privacy in IoT networks, primarily through the conventional authentication approaches. The main goal is to review the current research learning related to the safety issues of the IoT and provides an information understanding on the topic. The manual scientific visualization study was carried out over a number of articles, and out of which, 58 research articles are selected for careful observation. From the articles, impacts and solutions with future research on security within IoT gateway have been identified. These visualization researchers have indicated key concerns as well as number of solutions. The findings also indicated the challenges in achieving secured data protection management and data center integration, which still require efficient solutions. The outcome of the physical ınstitutional mapping

Eastern-European Journal of Enterprise Technologies

Security issues and Internet of Things (IoT) risks in several areas are growing steadily with the increased usage of IoT. The systems have developed weaknesses in computer and memory constraints in most IoT operating systems. IoT devices typically cannot operate complicated defense measures because of their poor processing capabilities. A shortage of IoT ecosystems is the most critical impediment to developing a secured IoT device. In addition, security issues create several problems, such as data access control, attacks, vulnerabilities, and privacy protection issues. These security issues lead to affect the originality of the data that cause to affects the data analysis. This research proposes an AI-based security method for the IoT environment (AI-SM-IoT) system to overcome security problems in IoT. This design was based on the edge of the network of AI-enabled security components for IoT emergency preparedness. The modules presented detect, identify and continue to identify the ...

Electronics

Cloud computing provides the flexible architecture where data and resources are dispersed at various locations and are accessible from various industrial environments. Cloud computing has changed the using, storing, and sharing of resources such as data, services, and applications for industrial applications. During the last decade, industries have rapidly switched to cloud computing for having more comprehensive access, reduced cost, and increased performance. In addition, significant improvement has been observed in the internet of things (IoT) with the integration of cloud computing. However, this rapid transition into the cloud raised various security issues and concerns. Traditional security solutions are not directly applicable and sometimes ineffective for cloud-based systems. Cloud platforms’ challenges and security concerns have been addressed during the last three years, despite the successive use and proliferation of multifaceted cyber weapons. The rapid evolution of deep...

Internet of Things, 2021

Physical Education, Health and Social Sciences, 2025

The convergence of the Internet of Things (IoT) and cloud computing has created a highly distributed, data-intensive ecosystem that drives innovation across industries. However, the same integration introduces complex cybersecurity risks due to device heterogeneity, scalability requirements, and dynamic threat landscapes [1], [4], [7]. Traditional security measures are insufficient in such environments, creating demand for adaptive, intelligent, and proactive defense mechanisms [16], [17]. Artificial intelligence (AI) offers powerful capabilities for intrusion detection, anomaly detection, malware analysis, and predictive threat modeling [3], [6], [9]. This paper explores how AI techniques ranging from machine learning and deep learning to federated and reinforcement learning are being applied to strengthen IoTcloud ecosystems against evolving cyberattacks [2], [10], [11]. The discussion covers architectural models, real-world deployments, challenges such as adversarial AI, privacy, and compliance, and emerging directions like explainable AI and quantum-safe security [13], [24], [30]. The study concludes that AI-driven cybersecurity has transformative potential but requires careful balancing of efficiency, interpretability, and resilience to ensure trust in IoTcloud ecosystems [19], [23], [31].

https://www.researchgate.net/publication/331545884_Critical-Issues-in-the-Invasion-of-the-Internet-of-Things-IoT_-Security-Privacy-and-Other-Vulnerabilities, 2019

In this chapter, the authors collected data from issues related to threats in the applications of IoT-based technologies that describe the security and privacy issues from 30 peer reviewed publications from 2014 to 2017. Further, they analyzed each threat type and its percentages in each application of the internet of things. The results indicated that the applications of smart transportation (20%) face the highest amount of security and privacy issues followed by smart home (19%) and smart cities (18%) compared to the rest of the applications. Further, they determined that the biggest threats were denial of service attack (9%) followed by eavesdropping (5%), man in the middle (4%), and replay (4%). Denial of service attacks and man in the middle attack are active attacks that can severely damage human life whereas eavesdropping is a passive attack that steals information. This study has found that privacy issues have the biggest impacts on people. Therefore, researchers need to find possible solutions to these threats to improve the quality of IoT applications.

Computer Communications

Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. Contents * Corresponding author.

The investigation of learning in antagonistic situations is a developing control at the point between Deep learning and PC security. The enthusiasm for learning-based strategies for security-and framework plan applications originates from the high level of intricacy of marvels fundamental the security and dependability of PC frameworks. As it turns out to be progressively troublesome to achieve the craved properties exclusively utilizing statically planned components, learning strategies are being utilized increasingly to acquire a superior comprehension of different information gathered from these perplexing frameworks. In any case, learning methodologies can be dodged by enemies, who change their conduct because of the learning strategies. To-date, there has been constrained research into learning methods that are strong to assaults with provable strength ensures. The Perspectives Workshop, "Deep Learning Methods for Computer Security" was convened to unite intrigued scientists from both the PC security and Deep learning groups to talk about systems, difficulties, and future research bearings for secure learning and learning-based security applications. As an aftereffect of the twenty-two welcomed presentations, workgroup sessions and casual examination, a few need ranges of research were distinguished. The open issues recognized in the field extended from customary utilizations of Deep learning in security, for example, assault location and investigation of pernicious programming, to methodological issues identified with secure learning, particularly the improvement of new formal methodologies with provable security ensures. At last various other potential applications were pinpointed outside of the conventional extent of PC security in which security issues may likewise emerge in association with information driven strategies. Cases of such applications are web-based social networking spam, literary theft discovery, initiation recognizable proof, copyright implementation, PC vision (especially in the setting of biometrics), and estimation investigation.