Hybrid intelligent technique for intrusion detection in cyber physical systems with improved feature set

Abstract

Machine learning techniques commonly used for intrusion detection systems (IDSs face challenges due to inappropriate features and class imbalance. A novel IDS comprises four stages: Pre-processing, Feature Extraction, Feature Selection, and Detection. Initial pre-processing balances input data using an improved technique. Features (statistical, entropy, correlation, information gain) are extracted, and optimal ones selected using Improved chi-square. Intrusion detection is performed by a hybrid model combining Bi-GRU and CNN classifiers, with optimized weight parameters using SI-BMO. The outputs from both classifiers are averaged for the result. The SI-BMO-based IDS is compared with conventional techniques Blue Monkey Optimization (BMO), Grasshopper Optimization Algorithm (GOA), Deer Hunting Optimization (DHO), Poor Rich Optimization (PRO), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) for performance evaluation.