Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

Cloud computing has become a significant technology trend Experts believe cloud computing is currently reshaping information technology and the IT marketplace The advantages of using cloud computing include cost savings, speed to market, access to greater computing resources, high availability, and scalability Handbook of Cloud Computing includes contributions from world experts in the field of cloud computing from academia, research laboratories and private industry This book presents the systems, tools, and services of the leading providers of cloud computing; including Google, Yahoo, Amazon, IBM, and Microsoft The basic concepts of cloud computing and cloud computing applications are also introduced Current and future technologies applied in cloud computing are also discussed Case studies, examples, and exercises are provided throughout Handbook of Cloud Computing is intended for advanced-level students and researchers in computer science and electrical engineering as a reference book This handbook is also beneficial to computer and system infrastructure designers, developers, business managers, entrepreneurs and investors within the cloud computing related industry

Handbook of Cloud Computing

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Internet of Things: A survey on machine learning-based intrusion detection approaches

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user’s concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.

https://journals.sagepub.com/doi/pdf/10.1155/2014/190903

Data Security and Privacy in Cloud Computing

As a typical application of Internet of Things (IoT) in the field of transportation, Internet of Vehicles (IoV) aims at achieving an integrated intelligent transportation system to enhance traffics efficiency, avoid accidents, ensure road safety, and improve driving experiences by using new IoT technologies. Different from other Internet, it is characterized by dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation. Due to these characteristics, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks, etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.

Attacks and countermeasures in the internet of vehicles

Internet of Vehicles (IoV) is a typical application of Internet of things in the field of transportation, which aims at achieving an integrated intelligent transportation system to enhance traffics, to avoid accidents, to ensure road safety, and to improve driving experiences. Due to its characteristics of dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks and etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.

Security and Privacy in the Internet of Vehicles

A new method, which assumes with a function form of , is applied to solve the Dodd-Bullough equation through travelling-wave transformation. A new family of explicit travelling wave solutions is derived. The proposed method works efficiently to be applied to solve other forms of Dodd-Bullough equations.

/pdf/new-travelling-wave-solutions-for-dodd-bullough-equation-4431gmq66c.pdf

New Travelling-Wave Solutions for Dodd-Bullough Equation

The fingerprint positioning based on received signal strength has a widespread development in many applications since the technology does not depend on additional hardware deployment. However, the site survey process which is executed before fingerprint positioning is time consuming and labor intensive. Sampling cell size directly affects the localization precision and the survey workload. We propose a sampling cell size optimization method to release the survey workload while holding the precision. We adopted the Gaussian process model to estimate the received signal strength based on some samples and used the inherent maximum Euclidean error of fingerprint vectors to compute the optimal cell size. We conduct some experiments to validate the efficiency of the mechanism. And the results show that our method can largely reduce the workload of the site survey process.

Yongping Xiong

Papers

Data Security and Privacy in Cloud Computing

Attacks and countermeasures in the internet of vehicles

Security and Privacy in the Internet of Vehicles

New Travelling-Wave Solutions for Dodd-Bullough Equation

Optimization of Sampling Cell Size for Fingerprint Positioning