Providing a method to anomaly detection in blockchain patterns using a combination of genetic algorithm and machine learning

Blockchain models are vulnerable to various types of attacks due to their vulnerability. Due to many network traffic features of blockchain patterns, machine learning models are time-consuming to identify attacks. In this paper, a new method for detecting anomalies in blockchain patterns is presented. The proposed method for network intrusion detection is to use machine learning technique and also to reduce the number of features in order to reduce the time of intrusion detection in blockchain patterns. At first in the proposed method is reduced the number of features using genetic algorithm, then it is made using classifications based on machine learning. The proposed method is tested on the NSL-KDD dataset. Accuracy, recall and correctness criteria are used to evaluate the proposed method. The accuracy of the proposed method is above ۹۷% in the best test mode, and its validity can be confirmed based on comparison with other references.