Keylogging has become a crucial attacking strategy adopted by cyber criminals to fetch keystrokes of legitimate users during their interaction with computer systems. The data gathered serves as a powerful weapon for attackers to exploit. Existing techniques for detecting or preventing keylogging operations rely on mundane systems or traditional machine learning algorithms that lack deep intelligence to identify and mitigate activities of keyloggers accurately due to the volume of data that is generated from keystrokes with simple interactions from the users. This study developed a deep learning-based predictive model capable of preventing keylogging attacks in human-computer interaction using hyperband hyperparameter tuning and a hybrid genetic algorithm on stochastic gradient descent with Adam-based LSTM optimization on a UNSW-NB keylogging dataset with nine families of attacks to build the deep learning model called HH-GASA-OLSTM. From the experiments, the proposed predictive model on an 80:20 validation ratio gives the best result on six performance metrics, which are loss function of 0.0187, accuracy of 99.45%, precision of 0.9930, recall of 0.9945, F1-Score of 0.9938, MCC of 0.9889, RMSE of 0.0054, MAE of 0.0054 and MSE of 0.00003. These results are suitable for the accurate detection and prevention of keylogging attacks in HCI. The study therefore recommends further studies; the implementation of other hyperparameter tuning approaches with other parameter optimization techniques using other deep learning architectures, such as radial basis function network and deep belief network, for improved model optimization.