Detection of false data injection in point-of-sale systems during credit card transactions using tuned deep learning models and oversampling techniques

Background False data injection attack (FDIA) occurs during credit card transactions at point-of-sale systems (POS). FDI increases in POS due to free WIFI access at sales counters. FDIA occurs through novel hardware attacks such as side channel, readout-bypass, and control flow attacks. The false data injection in POS leads to a data breach and financial loss to the customer and the seller/credit card owner. Method To solve the above problem, we have developed architecture-tuned deep learning models such as random search (RS), artificial neural network (ANN), Bayesian optimized (BO), convolutional neural network (CNN), long short term memory (LSTM), Hyperband (HB), Autoencoder (AE). Moreover, tuned architecture model access is increased through oversampling methods such as random oversampling (ROS), synthetic minority oversampling (SMOTE), adaptive synthetic sampling (ADASYN), synthetic minority oversampling for nominal and continuous features (SMOTENC), and Borderline SMOTE (BL-SMOTE). BO-CNNLSTM model with SMOTE detects FDIA attack quickly and correctly to reduce overfitting of data through optimizing the number of hidden units of the LSTM model. Results Hence, the proposed BO-CNNLSTM model achieves an accuracy of about 98%, a precision of about 94%, a recall of about 96%, and an F1-score of about 96%.