A Review on Ai based Data Authentication by Monitoring Behavioural Pattern

In this study, we do an experiment to examine the viability of a continually authenticating approach based on the monitoring of users' activities to confirm their identities using particular user profiles that are modeled using AI techniques. To carry out the experiment, a unique application was created to collect user data in a supervised situation in which certain tasks must be finished in advance. After anonymization, this dataset will be made accessible to the public. Furthermore, a publicly available dataset was utilized for benchmarking, enabling our methods to be verified in an unguided environment. These data were processed to identify several important properties that might be utilized for training three distinct AI methods: Multi-Layer Perceptrons, Support Vector Machines, and a deep learning network. These methods proved to be successful in both situations and were able to effectively authenticate users. To detect imposters when an authenticated session is hijacked in a real-world setting, a continuous authentication method was designed and tested utilizing weighted sliding windows, and a rejection test was finally carried out

Trust and identity management within online social networks

Online social networking is one of the largest Internet activities, with almost one third of all daily Internet users visiting these websites. Characteristics of this environment are issues relating to trust, user privacy and anonymity. Service providers are focused primarily on acquiring users, and little attention is given to the effective management of these users within the social networking environment. This study serves to evaluate if proper identity management processes and controls are needed to protect users and their informational privacy, while establishing a higher degree of user trust for other users and the system. Design Science is followed as the primary methodology, with the final outcome being a proposed artefact. Through a detailed experiment, an evaluation of the controls and processes exhibited by Facebook and MySpace was conducted. The areas of evaluation were identified through the Vulnerability Mitigation and Assessment (VAM) methodology. The findings of this experiment, together with the secondary data reviewed, form the proposed artefact, which is a set of controls aimed at increasing trust and privacy through the effective implementation of these controls and identity management processes.Thesis (MA) -- Faculty of Management and Commerce, 201

Trust and identity management within online social networks

Online social networking is one of the largest Internet activities, with almost one third of all daily Internet users visiting these websites. Characteristics of this environment are issues relating to trust, user privacy and anonymity. Service providers are focused primarily on acquiring users, and little attention is given to the effective management of these users within the social networking environment. This study serves to evaluate if proper identity management processes and controls are needed to protect users and their informational privacy, while establishing a higher degree of user trust for other users and the system. Design Science is followed as the primary methodology, with the final outcome being a proposed artefact. Through a detailed experiment, an evaluation of the controls and processes exhibited by Facebook and MySpace was conducted. The areas of evaluation were identified through the Vulnerability Mitigation and Assessment (VAM) methodology. The findings of this experiment, together with the secondary data reviewed, form the proposed artefact, which is a set of controls aimed at increasing trust and privacy through the effective implementation of these controls and identity management processes.Thesis (MA) -- Faculty of Management and Commerce, 201