scholarly journals Cyber Security in IoT-Based Cloud Computing: A Comprehensive Survey

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 16
Author(s):  
Waqas Ahmad ◽  
Aamir Rasool ◽  
Abdul Rehman Javed ◽  
Thar Baker ◽  
Zunera Jalil
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Deep Learning ◽  
Cyber Security ◽  
Industrial Applications ◽  
Future Research ◽  
Cloud Infrastructure ◽  
Security Issues ◽  
Comprehensive Survey ◽  
Security Concerns

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 learning (DL) in the artificial intelligence (AI) domain has brought many benefits that can be utilized to address industrial security issues in the cloud. The findings of the proposed research include the following: we present a comprehensive survey of enabling cloud-based IoT architecture, services, configurations, and security models; the classification of cloud security concerns in IoT into four major categories (data, network and service, applications, and people-related security issues), which are discussed in detail; we identify and inspect the latest advancements in cloud-based IoT attacks; we identify, discuss, and analyze significant security issues in each category and present the limitations from a general, artificial intelligence and deep learning perspective; we provide the technological challenges identified in the literature and then identify significant research gaps in the IoT-based cloud infrastructure to highlight future research directions to blend cybersecurity in cloud.

scholarly journals Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges

2021 ◽  
Vol 11 (19) ◽  
pp. 9005
Author(s):  
Yara Alghofaili ◽  
Albatul Albattah ◽  
Noura Alrajeh ◽  
Murad A. Rassam ◽  
Bander Ali Saleh Al-rimy
Keyword(s):  
Cloud Computing ◽  
Cost Effective ◽  
Future Research ◽  
Data Loss ◽  
Cloud Infrastructure ◽  
Computing Systems ◽  
Security Issues ◽  
Effective Manner ◽  
Comprehensive Survey ◽  
New Challenges

Cloud computing is currently becoming a well-known buzzword in which business titans, such as Microsoft, Amazon, and Google, among others, are at the forefront in developing and providing sophisticated cloud computing systems to their users in a cost-effective manner. Security is the biggest concern for cloud computing and is a major obstacle to users adopting cloud computing systems. Maintaining the security of cloud computing is important, especially for the infrastructure. Several research works have been conducted in the cloud infrastructure security area; however, some gaps have not been completely addressed, while new challenges continue to arise. This paper presents a comprehensive survey of the security issues at different cloud infrastructure levels (e.g., application, network, host, and data). It investigates the most prominent issues that may affect the cloud computing business model with regard to infrastructure. It further discusses the current solutions proposed in the literature to mitigate the different security issues at each level. To assist in solving the issues, the challenges that are still unsolved are summarized. Based on the exploration of the current challenges, some cloud features such as flexibility, elasticity and the multi-tenancy are found to pose new challenges at each infrastructure level. More specifically, the multi-tenancy is found to have the most impact at all infrastructure levels, as it can lead to several security problems such as unavailability, abuse, data loss and privacy breach. This survey concludes by giving some recommendations for future research.

Attack Detection in Cloud Networks Based on Artificial Intelligence Approaches

2019 ◽  
pp. 63-84
Author(s):  
Zuleyha Yiner ◽  
Nurefsan Sertbas ◽  
Safak Durukan-Odabasi ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Learning Algorithms ◽  
General Information ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Network Access ◽  
Cloud Infrastructure ◽  
Security Issues

Cloud computing that aims to provide convenient, on-demand, network access to shared software and hardware resources has security as the greatest challenge. Data security is the main security concern followed by intrusion detection and prevention in cloud infrastructure. In this chapter, general information about cloud computing and its security issues are discussed. In order to prevent or avoid many attacks, a number of machine learning algorithms approaches are proposed. However, these approaches do not provide efficient results for identifying unknown types of attacks. Deep learning enables to learning features that are more complex, and thanks to the collection of big data as a training data, deep learning achieves more successful results. Many deep learning algorithms are proposed for attack detection. Deep networks architecture is divided into two categories, and descriptions for each architecture and its related attack detection studies are discussed in the following section of chapter.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

Attack Detection in Cloud Networks Based on Artificial Intelligence Approaches

2020 ◽  
pp. 377-394
Author(s):  
Zuleyha Yiner ◽  
Nurefsan Sertbas ◽  
Safak Durukan-Odabasi ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Learning Algorithms ◽  
General Information ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Network Access ◽  
Cloud Infrastructure ◽  
Security Issues

Cloud computing that aims to provide convenient, on-demand, network access to shared software and hardware resources has security as the greatest challenge. Data security is the main security concern followed by intrusion detection and prevention in cloud infrastructure. In this chapter, general information about cloud computing and its security issues are discussed. In order to prevent or avoid many attacks, a number of machine learning algorithms approaches are proposed. However, these approaches do not provide efficient results for identifying unknown types of attacks. Deep learning enables to learning features that are more complex, and thanks to the collection of big data as a training data, deep learning achieves more successful results. Many deep learning algorithms are proposed for attack detection. Deep networks architecture is divided into two categories, and descriptions for each architecture and its related attack detection studies are discussed in the following section of chapter.

Artificial Intelligence and Deep Learning-based Solutions to Enhance Cyber Security

2021 ◽  
Author(s):  
Lakshit Malhotra ◽  
Bharat Bhushan ◽  
Rahul Veer Singh
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cyber Security

A Comprehensive Survey on Interoperability for IIoT: Taxonomy, Standards, and Future Directions

2023 ◽  
Vol 55 (1) ◽  
pp. 1-35
Author(s):  
Abhishek Hazra ◽  
Mainak Adhikari ◽  
Tarachand Amgoth ◽  
Satish Narayana Srirama
Keyword(s):  
Industrial Revolution ◽  
Industrial Applications ◽  
Future Research ◽  
The Internet ◽  
Large Numbers ◽  
Industrial Iot ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
Real Time Information ◽  
The Internet Of Things

In the era of Industry 4.0, the Internet-of-Things (IoT) performs the driving position analogous to the initial industrial metamorphosis. IoT affords the potential to couple machine-to-machine intercommunication and real-time information-gathering within the industry domain. Hence, the enactment of IoT in the industry magnifies effective optimization, authority, and data-driven judgment. However, this field undergoes several interoperable issues, including large numbers of heterogeneous IoT gadgets, tools, software, sensing, and processing components, joining through the Internet, despite the deficiency of communication protocols and standards. Recently, various interoperable protocols, platforms, standards, and technologies are enhanced and altered according to the specifications of the applicability in industrial applications. However, there are no recent survey papers that primarily examine various interoperability issues that Industrial IoT (IIoT) faces. In this review, we investigate the conventional and recent developments of relevant state-of-the-art IIoT technologies, frameworks, and solutions for facilitating interoperability between different IIoT components. We also discuss several interoperable IIoT standards, protocols, and models for digitizing the industrial revolution. Finally, we conclude this survey with an inherent discussion of open challenges and directions for future research.

Cloud Computing and Cybersecurity Issues Facing Local Enterprises

2019 ◽  
pp. 1777-1799
Author(s):  
Emre Erturk
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Cloud Security ◽  
Security Issues ◽  
Contemporary Research ◽  
Security Practices ◽  
It Resources ◽  
Security Incidents

This chapter sets out to explore new trends in cyber and cloud security, and their implications for businesses. First, the terminology and assumptions related to cloud computing are stated. Next, the chapter reports on contemporary research around the awareness of security issues, and the security processes within the cloud computing realm. Cyber security poses a different challenge to local small and medium sized organizations, which may seem to have less at stake financially. However, they are more vulnerable, due to fewer resources dedicated toward prevention. A series of serious security incidents may even keep them out of business. Furthermore, security needs to be understood and handled differently in a cloud based environment. Therefore, the chapter identifies unique security practices and recommendations for these businesses to run their IT resources safely in the cloud.

When Wearable Computing Meets Smart Cities

2019 ◽  
pp. 1356-1376 ◽  
Author(s):  
João Soares de Oliveira Neto ◽  
André Luis Meneses Silva ◽  
Fábio Nakano ◽  
José J. Pérez-Álcazar ◽  
Sergio T. Kofuji
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Urban Space ◽  
Universal Design ◽  
Smart Cities ◽  
Wearable Computing ◽  
Future Research ◽  
Successful Implementation ◽  
Persons With Disabilities

In this chapter, wearables are presented as assistive technology to support persons with disabilities (PwD) to face the urban space in an autonomous and independently way. In the Inclusive Smart City (ISC), everyone has to be able to access visual and audible information that so far are available just for people that can perfectly see and listen. Several concepts and technologies – such as Accessibility and Universal Design, Pervasive Computing, Wearable Computing, Internet of Things, Artificial Intelligence, and Cloud Computing – are associated to achieve this aim. Also, this chapter discusses some examples of use of wearables in the context of Smart Cities, states the importance of these devices to the successful implementation of Inclusive Smart Cities, as well as presenting challenges and future research opportunities in the field of wearables in ISC.

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