Identity authentication model from continuous keystroke pattern using CSO and LSTM network.

The identification of a user's authenticity in a continuous form will have a wide range of appreciation since the one-time authentication system is admissible for compromise after logging in. In this research work, an optimisation-based deep learning network model namely cuckoo search optimisation-based long short-term memory (CSO-LSTM) is proposed to effectively learn the keystroke pattern of the user. The CSO algorithm is used to optimise the weight parameters of the long short-term memory (LSTM) network using the evolution process. As the network weight parameters get optimised, the learning mechanism will acquire a better prediction rate than existing techniques. Two datasets were utilised to evaluate the performance of the proposed model namely Clarkson II and Buffalo. The performance evaluation of the proposed model is evaluated with different count of neurons and varied lengths of keystrokes for scalability of model as the size of the dataset increases.