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The cosmic evolution of the Internet of things (IoTs) in par with its realization in all spheres of life undertakings, mandates continuous research pursuits in IoT and its associated components. While the rapid evolution of IoTs has facilitated monumental opportunities for humanity, it has also acted as a catalyst precipitating diverse security issues. Cybercrimes have been on the rise as criminals and hackers continue to take advantage of IoT's security loopholes and vulnerabilities. The enormity of the attacks has not only been damaging to the quality of life, but it poses a disservice and an unquantifiable risk to human safety. Thus, a timely and comprehensive review, analysis and investigation of the security of IoTs is crucial. Through a systematic literature review of over 200 articles, we set out the latest findings and trends to provide new insights into the security of IoTs, taking cognizant of its social, economic, technical and legal implications, which will be beneficial to researchers, manufacturers, individuals, organizations, and governments. Although many studies reviewing the state of IoT exist in the literature, no studies shape the area of its security well. Hence, there is currently no study that provides an in-depth survey of the emerging security concerns of IoT from diverse perspectives and in tandem with the current condition of the global world today. Compared to other related reviews on the security of IoTs, this survey encompasses much more technical angles to the security of IoT. It begins with the review of the concept of IoTs, the assessments of its industrial development trends, revolutionary paradigms and updated security of the IoT. Key challenges in the security of blockchain technology, a recent spike in distributed denial of service (DDoS) attacks due to the COVID-19 pandemic are sensitive areas that have remained untouched by previous review works. Additionally, politics and security of electoral votes, forensic issues in the IoT era and much more are some of the new depths missing in the literature of IoT security. Thus, a huge divide in the total adoption and actualization of IoT in diverse areas of human endeavour. This review formalizes the IoT concept, illuminating deep insights into possible solutions to the heterogeneous nature of IoT's security challenges, emerging issues, gaps, opportunities, foresight, and recommendations.

Investigating the online social network profiles of victims, suspects, and witnesses are now part of almost every legal investigation, either it involves a criminal offense, financial fraud, or domestic lawsuit. However, investigating online social networks (OSN) is a technically complicated process that becomes more challenging due to the legal issues of privacy and authentication. Completely manual investigative methods are not feasible for OSN investigations due to the immense size and heterogeneity of social networks. However, the existing models for digital forensic investigation are not supporting automated or semi-automated forensic investigation processes. Furthermore, they are not addressing the fundamental differences and specific requirements of online social networks. The model presented in this work incorporates the robust features of standard investigation models and proposes a digital forensic investigation process model that explicitly addresses the necessities of OSN investigations. This work is addressing the issues of automating the forensic collection and analysis processes, defining crime scene boundaries, and outlining reasonable iterative collection procedures in online social network forensic investigation. This work is evaluated using a case study and is compared with existing practices and standards.

Currently, examining social media networks is an integral part of most investigations. However, getting a clear view of the events relevant to the incident from a large set of data, such as social media, is a challenging task. Automation of the forensic and analysis process is the only solution to manage large data sets and get useful information. However, automation in digital forensics is a technical issue with legal implications. The legal system accepts only those automated processes that are reproducible, explainable, and rigorously testable. Therefore, automated forensic processes must be based on formal theories, which are rare in digital forensics. This article explains a theoretical and formal knowledge model for forensic automation on online social networks. This model consists of an event-based knowledge model, which provides theoretical concepts that can assist in the construction and interpretation of the events related to the incident under investigation. The proposed model is implemented through an ontology to provide semantically rich and formal representation to the concepts. This article also describes the feasibility of legally acceptable automated analysis operators, based on a formal theory, for online social network forensics.