Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Internet of things (IOT) possess cultural, commercial and social effect in life in the future. The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets. Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain. Machine Learning Based Ensemble Intrusion Detection (MLEID) method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport (MQTT) and Hyper-Text Transfer Proto-col (HTTP) protocols. The proposed work has two significant contributions which are a selection of features and detection of attacks. New features are chosen from Improved Ant Colony Optimization (IACO) in the feature selection, and then the detection of attacks is carried out based on a combination of their possible proper-ties. The IACO approach is focused on defining the attacker's important features against HTTP and MQTT. In the IACO algorithm, the constant factor is calculated against HTTP and MQTT based on the mean function for each element. Attack detection, the performance of several machine learning models are Distance Deci-sion Tree (DDT), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Mahala-nobis Distance Support Vector Machine (MDSVM) were compared with predicting accurate attacks on the IoT network. The outcomes of these classifiers are combined into the ensemble model. The proposed MLEID strategy has effec-tively established malicious incidents. The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors. Besides, the pro-posed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.