Assessing the security of IoT-based smart environments such as smart homes and smart cities is becoming fundamentally essential to implementing the correct control measures and effectively reducing security threats and risks brought about by deploying IoT-based smart technologies. The problem, however, is in finding security standards and assessment frameworks that best meets the security requirements as well as comprehensively assesses and exposes the security posture of IoT-based smart environments. To explore this gap, this paper presents a review of existing security standards and assessment frameworks which also includes several NIST special publications on security techniques highlighting their primary areas of focus to uncover those that can potentially address some of the security needs of IoT-based smart environments. Cumulatively a total of 80 ISO/IEC security standards, 32 ETSI standards and 37 different conventional security assessment frameworks which included 7 NIST special publications on security techniques were reviewed. To present an all-inclusive and up-to-date state-of-the-art research, the review process considered both published security standards and assessment frameworks as well as those under development. The findings show that most of the conventional security standards and assessment frameworks do not directly address the security needs of IoT-based smart environments but have the potential to be adapted into IoT-based smart environments. With this insight into the state-of-the-art research on security standards and assessment frameworks, this study helps advance the IoT field by opening new research directions as well as opportunities for developing new security standards and assessment frameworks that will address future IoT-based smart environments security concerns. This paper also discusses open problems and challenges related to IoT-based smart environments security issues. As a new contribution, a taxonomy of challenges for IoT-based smart environment security concerns drawn from the extensive literature examined during this study is proposed in this paper which also maps the identified challenges to potential proposed solutions.

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

It is predicted that, the number of connected Internet of Things (IoT) devices will rise to 38.6 billion by 2025 and an estimated 50 billion by 2030. The increased deployment of IoT devices into diverse areas of our life has provided us with significant benefits such as improved quality of life and task automation. However, each time a new IoT device is deployed, new and unique security threats emerge or are introduced into the environment under which the device must operate. Instantaneous detection and mitigation of every security threat introduced by different IoT devices deployed can be very challenging. This is because many of the IoT devices are manufactured with no consideration of their security implications. In this paper therefore, we review existing literature and present IoT threat detection research advances with a focus on the various IoT security challenges as well as the current developments towards combating cyber security threats in IoT networks. However, this paper also highlights several future research directions in the IoT domain.

More than ever before, the world is nowadays experiencing increased cyber-attacks in all areas of our daily lives. This situation has made combating cybercrimes a daily struggle for both individuals and organisations. Furthermore, this struggle has been aggravated by the fact that today's cybercriminals have gone a step ahead and are able to employ complicated cyber-attack techniques. Some of those techniques are minuscule and inconspicuous in nature and often camouflage in the facade of authentic requests and commands. In order to combat this menace, especially after a security incident has happened, cyber security professionals as well as digital forensic investigators are always forced to sift through large and complex pools of data also known as Big Data in an effort to unveil Potential Digital Evidence (PDE) that can be used to support litigations. Gathered PDE can then be used to help investigators arrive at particular conclusions and/or decisions. In the case of cyber forensics, what makes the process even tough for investigators is the fact that Big Data often comes from multiple sources and has different file formats. Forensic investigators often have less time and budget to handle the increased demands when it comes to the analysis of these large amounts of complex data for forensic purposes. It is for this reason that the authors in this paper have realised that Deep Learning (DL), which is a subset of Artificial Intelligence (AI), has very distinct use-cases in the domain of cyber forensics, and even if many people might argue that it's not an unrivalled solution, it can help enhance the fight against cybercrime. This paper therefore proposes a generic framework for diverging DL cognitive computing techniques into Cyber Forensics (CF) hereafter referred to as the DLCF Framework. DL uses some machine learning techniques to solve problems through the use of neural networks that simulate human decision-making. Based on these grounds, DL holds the potential to dramatically change the domain of CF in a variety of ways as well as provide solutions to forensic investigators. Such solutions can range from, reducing bias in forensic investigations to challenging what evidence is considered admissible in a court of law or any civil hearing and many more.