FDF-HybridFS: Towards design of a failure detection framework using hybrid feature selection method for IP core networks that connect 5G core in NFV-based test environment

With the advancement of recent technologies, high-speed and secure internet connectivity is required for 5thgeneration mobile networks. A massive number of smart devices connected to the networks generate a large amount of data. Consequently, increasing traffic has generated more strain on networks and devices. Therefore, to handle the huge amount of data generated by the various devices and to detect network and device failures, automation is expected to play an important role. Hence, these requirements have accelerated the necessity of automation. This study presents a novel hybrid feature-selection technique for detecting network and device failure in IP core networks that connect 5 G core networks for internet connectivity. This approach first performs feature ranking using the MI (Mutual information), CC (Correlation coefficient), and GI (Gini index) to get the three different feature sets. Subsequently, in order to acquire a single optimized feature set, features are combined using a union operation. This optimized feature set is fed to the wrapper-based Boruta feature selection algorithm. In addition, to handle the data imbalance problem, we have applied the SMOTETomek method. Finally, obtained optimized feature set is fed to the three most popular ensemble approaches, Random forest, LGBM (Light gradient boosting machine), and xGBoost for the detection of failure. The proposed detection system's performance is evaluated using the KDDI dataset provided by KDDI Corporation. The effectiveness of the proposed FDF-HybridFS is tested and compared with several recent state-of-the-art methods cited in the related work in terms of detection rate, precision, accuracy, and F1 score. An analysis of the proposed failure detection framework's performance on the KDDI dataset shows that it outperforms state-of-the-art methods in terms of both detection rate and detection accuracy.