SLNALog: A log Anomaly Detection Scheme Based on Swift Layer Normalization Attention Mechanism for Next-Generation Power Communication Networks

Log anomaly detection is a critical first line of defense for securing next-generation power communication networks against malicious attacks.serves as the initial line of defense for safeguarding the security of the next-generation power communication networks, which can protect it from attackers invasion damage. However, in industrial settingsin the industrial Internet domain, limitedthe scarcity of computational resources on edge devices result in longin devices leads to prolonged inference times for anomaly detection models, hindering the timely detection of anomalous log activities.impeding the prompt identification of unusual activities logged within these devices. To address these challenges, we propose SLNALog, an anomaly detection workflow centered around a Swift Layer Normalization Attention module.the aforementioned issues, the SLNALog anomaly detection workflow has been proposed. Its core comprises a Swift Layer Normalization Attention module. This module leveragesis based on linear attention to optimizeand optimizes the key-value interactions found in traditional attention mechanisms, thereby reducing the computational complexity of the detection process. This optimization reduces the complexity of log anomaly detection. As a result, the model’s receptive field for log data is expanded, and the efficiency of log anomaly detection is improved. ExperimentalThe experimental results on the HDFS and BGL datasets demonstrate the superiority of our approach.indicate that it achieves higher accuracy. SLNALog achieves higher accuracy, with F1-scores increasing by 0.08 and 0.04, respectively, while reducing detection time by 5.7% and 28.3%. The model provides an effective solution to enhance the cyber security of smart grids. Furthermore, the workflow incorporates an LLM-based log template analysis module and an Adapter-based model tuning module to enhance the model’s generalization in real-world scenarios. The proposed model provides an effective solution for enhancing the cybersecurity of smart grids.