Affiliations: School of Information Technology and Engineering, Vellore Institute of Technology-Vellore, Tamil Nadu, India
Correspondence:
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Corresponding author. C. Vanmathi, School of Information Technology and Engineering, Vellore Institute of Technology-Vellore, Tamil Nadu-632014, India. E-mail: [email protected].
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.
Keywords: Intrusion detection system, Improved class imbalance processing, bi-directional gated recurrent unit (Bi-GRU), convolutional neural network (CNN), self-improved blue monkey optimization (SI-BMO), cyber-physical systems (CPS)
