A hybrid model of anomaly detection using inverse weight clustering and machine learning in cloud environments

Today, due to highly advanced attacks and intrusions, it has become very difficult to detect IoT attacks in cloud environments. Other problems with cloud systems include low error detection accuracy, false positive rates, and long computation times. In the proposed method, we present a hybrid intrusion detection model including a clustering algorithm and a machine-based random forest classification for fog and cloud environments. Also, to control the network traffic in the physical layer and also to detect anomalies between IoT devices, calculations will be performed on the fog and the edges of the cloud, so that after preprocessing, the incoming traffic to the fog and cloud will be checked and if necessary They are directed to an anomaly detection module. A random forest-based learning classification was used to identify the type of each attack. General data and cloud data have been used for research. The overall accuracy of the proposed intrusion detection system was 98.03 and the mean false positive rate was 17% and the anomaly detection rate was 96.30, which is considerable compared to previous methods.