Abstract

Security of computers, networks and their communication protocols are vital in smart grid technology operation and its management. This paper discusses a proposed Bayesian Neural Networks for time-series TCP/IP packets intrusion detection and threats classification in a grid network. This architecture termed SGtechNet detects invariants with maximized detection accuracy by applying a robust method that approximates the variation in posterior weights of neural networks with variational inference to minimize the divergence between prior and true network posterior distributions. Spatiotemporal feature engineering and uncertainty estimation in Bayesian modeling, were leveraged to learn novel attack features and classify attacks accordingly. This architecture reduced the size of the proposed model to 25 % of the size of a pioneer model (AlexNet), hence, facilitating the inference time compared to the baseline. SGtechNet was tested on NSL-KDD datasets using two deep learning algorithms: CNN-LSTM and GRU, on two classification categories (binary and multiclass) with Accuracy, Precision, Recall, and F1-Score as the performance metrics. GRU algorithm comparatively performed moderately well on both classification categories, unlike CNN-LSTM that performed convincingly only on one test category. Comparing the result of the SGtechNet model against a comparator model showed outstanding performance in both model size, computational speed, and marginal improvement in terms of accuracy. Chi-Square Test analysis determined that the degree at which the training accuracy differed with validation accuracy was statistically insignificant.