EISM-CPS: An Enhanced Intelligent Security Methodology for Cyber-Physical Systems through Hyper-Parameter Optimization.

The increased usage of cyber-physical systems (CPS) has gained the focus of cybercriminals, particularly with the involvement of the internet, provoking an increased attack surface. The increased usage of these systems generates heavy data flows, which must be analyzed to ensure security. In particular, machine learning (ML) and deep learning (DL) algorithms have shown feasibility and promising results to fulfill the security requirement through the adoption of intelligence. However, the performance of these models strongly depends on the model structure, hyper-parameters, dataset, and application. So, the developers only possess control over defining the model structure and its hyper-parameters for diversified applications. Generally, not all models perform well in default hyper-parameter settings. Their specification is a challenging and complex task and requires significant expertise. This problem can be mitigated by utilizing hyper-parameter optimization (HPO) techniques, which intend to automatically find efficient learning model hyper-parameters in specific applications or datasets. This paper proposes an enhanced intelligent security mechanism for CPS by utilizing HPO. Specifically, exhaustive HPO techniques have been considered for performance evaluation and evaluation of computational requirements to analyze their capabilities to build an effective intelligent security model to cope with security infringements in CPS. Moreover, we analyze the capabilities of various HPO techniques, normalization, and feature selection. To ensure the HPO, we evaluated the effectiveness of a DL-based artificial neural network (ANN) on a standard CPS dataset under manual hyper-parameter settings and exhaustive HPO techniques, such as random search, directed grid search, and Bayesian optimization. We utilized the min-max algorithm for normalization and SelectKBest for feature selection. The HPO techniques performed better than the manual hyper-parameter settings. They achieved an accuracy, precision, recall, and F1 score of more than 98%. The results highlight the importance of HPO for performance enhancement and reduction of computational requirements, human efforts, and expertise. [ABSTRACT FROM AUTHOR]