Improving Multi-Factor Authentication Security with Deep Learning-Based User Behaviour Analysis

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions; thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely.