As cyber-physical systems become more integrated into our daily lives, it is essential to ensure their security and reliability. Intrusion detection is an important aspect of cyber security for these systems, as it helps to identify and prevent cyber attacks that can cause damage to physical systems. Rider Deep LSTM is a variant of the Long Short-Term Memory (LSTM) model that can be used for intrusion detection in cyber-physical systems. This model uses deep neural network architecture to learn patterns in time-series data and classify whether or not an intrusion has occurred. In this paper, we explore the use of Rider Deep LSTM for intrusion detection in cyber-physical systems and evaluate its performance using a dataset of normal and anomalous behavior. Our results show that Rider Deep LSTM is a powerful tool for intrusion detection in cyber-physical systems, and can help to improve their security and reliability.

Intrusion detection, Deep Recurrent Neural Network, cyber security systems, water wave optimization algorithm, rider optimization approach.