Identity Management System (IDMS) refers to how users or individuals are identified and authorized to use organizational systems and services. Since traditional identity management and authentication systems rely heavily on a trusted central authority, they cannot mitigate the effects of single points of failure. As a decentralized and distributed public ledger in a peer-to- peer (P2P) network, Blockchain (BC) technology has garnered a considerable amount of attention in the field of IDMS in recent years. Through Self-Sovereign Identity (SSI), users can have full authority over their digital identity. Successful implementation of a BC-based IDMS can significantly increase the degree of privacy and security of a user’s SSI. However, the integration of BC-based IMDS to provide a user with SSI is still an unorganized area of research in its early stages of development. This article presents an extensive literature review of state-of-the-art academic publications as well as commercial market offerings regarding the applicability of BC-based SSI solutions. It also provides a detailed preliminary regarding the building blocks of blockchain technology and a progressive roadmap of IDMS solutions. In order to develop an effective BC-based IDMS solution that focuses on securing a user’s SSI, this article outline five essential components of a BC-based IDMS: authentication, integrity, privacy, trust, and simplicity. Furthermore, we perform a security analysis that outlines several types of adversarial threats that can cause potential damage to the BC-based IDMS. We identify and discuss associated issues and challenges by analyzing several notable BC-based IDMS solutions in academic literature. We also highlight potential research gaps and provide future research scope.

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented.

Existing surveys of research into Software-Defined Networking (SDN) make only minimal mention of multicasting. We present a survey of existing multicast routing protocols in IP multicast, and a survey of existing and proposed multicast routing algorithms in SDN multicast. Each of these protocols/algorithms is designed to optimize a slightly different metric, under specific constraints, so we contrast the approaches based on the optimization goal. We then present a survey of existing and proposed approaches to security for IP multicast and SDN multicast. This reveals a striking lack of breadth in the research in the topic of SDN multicast security.

Software-Defined Networking (SDN) has revolutionized the networking landscape by offering programmable control and optimization of network resources. However, SDN architecture’s inherent flexibility and centralized control expose it to new security risks. In this paper, we have presented a comprehensive study focused on the security implications associated with the control plane of SDN, which serves as a critical layer responsible for its network orchestration. We have addressed some pressing security concerns in SDN deployments by examining control plane vulnerabilities and explicit attacks. Through extensive analysis, we have investigated various control plane attacks. By meticulously exploring each attack vector, we have shed light on its mechanisms, potential impact and countermeasures. Furthermore, we have emphasized the interdependencies between the control plane, application plane, and data plane, highlighting how compromises in the control plane can propagate and impact the entire network infrastructure. Our research contributes to a deeper understanding of the specific vulnerabilities within SDN, focusing on the control plane as the primary target. By providing insights into the security landscape of SDN, network administrators, researchers, and security practitioners can develop proactive defense strategies and fortify the security posture of SDN deployments. We have underscored the importance of integrating robust security mechanisms to safeguard the control plane and maintain the overall security of SDN architectures. Our comprehensive analysis of control plane attacks in SDN elucidates the evolving security challenges posed by the programmability and centralization of network control. By addressing these vulnerabilities, we have tried to pave the way for future researchers to develop effective security solutions and ensure SDN networks’ resilience and integrity.

With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.