NEW COMPARATIVE ANALYSIS BASED CYBER SECURITY USING DEEP REINFORCEMENT LEARNING

Abstract

A new intelligent integration between an IoT platform and deep learning neural network (DNN) algorithm for the online update computer numerical control (CNC) machines. Number of organizations rely greatly on technology and most are changing their process into intelligent or smart solutions.  These networks are wireless self-configuring is preexisting infrastructure, and have a large unpredictable node movement security becomes one of the most crucial concerns that need to be addressed. Cyber security is a pivotal intrusion detection systems (IDS) is effectively detecting and preventing cyber-attacks on IoT devices. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQN), adapted to the intrusion detection context. Feed Forward Deep Neural Networks have been developed using across various parameter permutations at differing rates of data poisoning to develop a robust deep learning architecture. We deploy backdoor command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate machine learning based anomaly detection system can perform well in detecting these attacks. Diverse scenarios are presented to verify the effectiveness of the developed system where it can disconnect immediately to secure the system automatically when detecting the cyber-attack and switch to the backup to continue the runtime operation. This survey will guide researchers and industry experts in adopting Deep Learning techniques in IoT security and intrusion detection.