Machine Learning Defence Mechanism for Securing the Cloud Environment

A computer paradigm known as ”cloud computing” offers end users on-demand, scalable, and measurable services. Today’s businesses rely heavily on computer technology for a variety of reasons, including cost savings, infrastructure, development platforms, data processing, data analytics, etc. The end users can access the cloud service providers’ (CSP) services from any location at any time using a web application. The protection of the cloud infrastructure is of the highest  significance, and several studies using a variety of technologies have been conducted to develop more effective defenses against cloud threats. In recent years, machine learning technology has shown to be more effective in securing the cloud environment. In recent years, machine learning technology has shown to be more effective in securing the cloud environment. To create models that can automate the process of identifying cloud threats with better accuracy than any other technology, machine learning algorithms are  trained  on  a  variety  of  real-world  datasets. In this study, various recent research publications that used machine learning as a defense mechanism against cloud threats are reviewed

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Security for 5G Mobile Wireless Networks

The advanced features of 5G mobile wireless network systems yield new security requirements and challenges. This paper presents a comprehensive survey on security of 5G wireless network systems compared to the traditional cellular networks. The paper starts with a review on 5G wireless networks particularities as well as on the new requirements and motivations of 5G wireless security. The potential attacks and security services with the consideration of new service requirements and new use cases in 5G wireless networks are then summarized. The recent development and the existing schemes for the 5G wireless security are presented based on the corresponding security services including authentication, availability, data confidentiality, key management and privacy. The paper further discusses the new security features involving different technologies applied to 5G such as heterogeneous networks, device-to-device communications, massive multiple-input multiple-output, software defined networks and Internet of Things. Motivated by these security research and development activities, we propose a new 5G wireless security architecture, based on which the analysis of identity management and flexible authentication is provided. As a case study, we explore a handover procedure as well as a signaling load scheme to show the advantage of the proposed security architecture. The challenges and future directions of 5G wireless security are finally summarized

A Holistic Analysis of Internet of Things (IoT) Security : Principles, Practices, and New Perspectives

Peer reviewedPublisher PD

Collaborative Intrusion Detection in Federated Cloud Environments using Dempster-Shafer Theory of Evidence

Moving services to the Cloud environment is a trend that has been increasing in recent years, with a constant increase in sophistication and complexity of such services. Today, even critical infrastructure operators are considering moving their services and data to the Cloud. As Cloud computing grows in popularity, new models are deployed to further the associated benefits. Federated Clouds are one such concept, which are an alternative for companies reluctant to move their data out of house to a Cloud Service Providers (CSP) due to security and confidentiality concerns. Lack of collaboration among different components within a Cloud federation, or among CSPs, for detection or prevention of attacks is an issue. For protecting these services and data, as Cloud environments and Cloud federations are large scale, it is essential that any potential solution should scale alongside the environment adapt to the underlying infrastructure without any issues or performance implications. This thesis presents a novel architecture for collaborative intrusion detection specifically for CSPs within a Cloud federation. Our approach offers a proactive model for Cloud intrusion detection based on the distribution of responsibilities, whereby the responsibility for managing the elements of the Cloud is distributed among several monitoring nodes and brokering, utilising our Service-based collaborative intrusion detection – “Security as a Service” methodology. For collaborative intrusion detection, the Dempster-Shafer (D-S) theory of evidence is applied, executing as a fusion node with the role of collecting and fusing the information provided by the monitoring entities, taking the final decision regarding a possible attack. This type of detection and prevention helps increase resilience to attacks in the Cloud. The main novel contribution of this project is that it provides the means by which DDoS attacks are detected within a Cloud federation, so as to enable an early propagated response to block the attack. This inter-domain cooperation will offer holistic security, and add to the defence in depth. However, while the utilisation of D-S seems promising, there is an issue regarding conflicting evidences which is addressed with an extended two stage D-S fusion process. The evidence from the research strongly suggests that fusion algorithms can play a key role in autonomous decision making schemes, however our experimentation highlights areas upon which improvements are needed before fully applying to federated environments

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page

Blending big data analytics : review on challenges and a recent study

With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table