We present a detailed survey of the Zero Trust (ZT) security paradigm which has a growing number of advocates in the critical infrastructure risk management space. The article employs a descriptive approach to present the fundamental tenets of ZT and provides a review of numerous potential options available for successful realization of this paradigm. We describe the role of authentication and access control in Zero Trust Architectures (ZTA) and present an in-depth discussion of state-of-the-art techniques for authentication and access control in different scenarios. Furthermore, we comprehensively discuss the conventional approaches to encryption, micro-segmentation, and security automation available for instantiating a ZTA. The article also details various challenges associated with contemporary authentication mechanisms, access control schemes, trust and risk computation techniques, micro-segmentation approaches, and Software-Defined Perimeter, that can impact the implementation of ZT in its true sense. Based upon our analysis, we finally pinpoint the potential future research directions for successful realization of ZT in critical infrastructures.

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI’s potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

The Internet of Things (IoT) vision is increasingly being realized to facilitate convenient and efficient human living. To conduct effective IoT research using the most appropriate tools and techniques, we discuss recent research trends in the IoT area along with current challenges faced by the IoT research community. Several existing and emerging IoT research areas such as lightweight energy-efficient protocol development, object cognition and intelligence, as well as the critical need for robust security and privacy mechanisms will continue to be significant fields of research for IoT. IoT research can be a challenging process spanning both virtual and physical domains through the use of simulators and testbeds to develop and validate the initial proof-of-concepts and subsequent prototypes. To support researchers in planning IoT research activities, we present a comparative analysis of existing simulation tools categorized based on the scope of coverage of the IoT architecture layers. We compare existing large-scale IoT testbeds that have been adopted by researchers for examining the physical IoT prototypes. Finally, we discuss several open challenges of current IoT simulators and testbeds that need to be addressed by the IoT research community to conduct large-scale, robust and effective IoT simulation, and prototype evaluations.

Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.