Self-Adaptation of Systems Through Machine Learning: Detection of Environmental Risks for Information Systems Adaptation

Self-adaptive systems play a crucial role in the industrial world due to their ability to make corrections following environmental destabilization. In this paper we addressed the implementation of a machine learning approach which allows the detection of environmental hazards for the adaptation of information systems. We used internet connectivity data, which we collected from different networks, to train the model. We carried out these processes based on five machine learning algorithms, namely: XGBoost, Random Forest, SVM, Decision tree, K Neighbors. The results obtained by its algorithms in terms of test accuracy are 99.95% for XGBoost, Random Forest Decision tree, and 97.62% for SVM and 99.80% for K Neighbors. In terms of train accuracy are 100% for XGBoost, Random Forest Decision tree, and 97.74% for SVM and 99.84% for K Neighbors. In terms of F1-score are 99.95% for and 97.80% for K Neighbors. In terms of precision are 99.95% for XGBoost, Random Forest Decision tree, and 97.75% for SVM and 97.81% for K Neighbors. In terms of recall are 99.95% for XGBoost, Random Forest Decision tree, and 97.62% for SVM and 97.80% for K Neighbors. We also carried out a comparative analysis of the results obtained by each of the five algorithms, based on the following performance indicators: accuracy, F1-score, precision, Recall.