Description

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/ incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based. In this process we propose a machine learning algorithms to detect the anomaly detection from network. Firstly, we need to form the reversed data. Then find the target variable and split the data into training set and testing set. Then it will applied into classification method. In this method the machine learning algorithms are K Nearest Kneighbor and Random Forest. Finally predict the anomaly detection and find the result based on accuracy, detection rate, false positive rate, precision, recall, and f1-measure.