Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems

Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.