Unveiling Hidden Anomalies: Leveraging SMAC-LSTM for Enhanced Software Log Analysis

Software logs are essential records generated during the functioning of software systems, aiding in the identification of irregularities and prevention of system failures. Recently, deep learning models have garnered significant interest among researchers due to their efficacy in detecting anomalies within software logs. This research paper constructs a novel dataset, consisting of three parts: two datasets derived from our software system, along with a publicly available dataset obtained from the LogHub platform. The extensive logs within the dataset undergo preprocessing to extract meaningful features. Furthermore, this study introduces a novel model named SMAC-LSTM, designed specifically for detecting anomalies in software logs. Sequential Model-based Algorithm Configuration (SMAC) is a suitable method for hyperparameter optimization and automated deep learning. SMAC-LSTM involves determining the optimal hyperparameter values for the LSTM model using the SMAC. Additionally, SMAC-LSTM combines the temporal dependency capturing ability of Long Short-Term Memory (LSTM) with a context-dependent mechanism achieved through a Bayesian optimization algorithm based on random forests. This fusion enhances the model's ability to detect subtle anomalies in time series data, which are frequently disregarded by con-ventional LSTM models. The thorough evaluation demonstrates the superior performance of SMAC-LSTM models compared to traditional deep learning models, showcasing significant enhance-ments in precision (98.63%), and recall (92.31%), with an F1-Score of 95.36%, outperforming all other models. These results underscore the potential of SMAC-LSTM in the realm of software log anomaly detection.