Despite being the most widely used authentication mechanism, password-based authentication is not very secure, being easily guessed or brute-forced. To address this, many systems which especially value security adopt Multi-Factor Authentication (MFA), in which multiple different authentication mechanisms are used concurrently. JitHDA (Just-in-time human dynamics based authentication engine) is a new authentication mechanism which can add another option to MFA capabilities. JitHDA observes human behaviour and human dynamics to gather up to date information on the user from which authentication questions can be dynamically generated. This paper proposes a system that implements JitHDA, which we call Autonomous Inquiry-based Authentication Chatbot (AIAC). AIAC uses anomalous events gathered from a user’s recent activity to create personalized questions for the user to answer, and is designed to improve its own capabilities over time using neural networks trained on data gathered during authentication sessions. Due to using the user’s recent activity, they will be easy for the authentic user to answer and hard for a fraudulent user to guess, and as the user’s recent history updates between authentication sessions new questions will be dynamically generated to replace old ones. We intend to show in this paper that AIAC is a viable implementation of JitHDA.

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.