# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 7 Conclusions  
This paper proposes a log sequence anomaly detection framework based on evidential deep learning, named LogEDL. This method goes beyond traditional log sequence anomaly detection approaches by introducing a new task that leverages uncertainty to identify anomalies. Specifically, LogEDL not only prioritizes precise classification but also quantifies \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**HDFS**} & \multicolumn{3}{c}{**BGL**} & \multicolumn{3}{c}{**Thunderbird**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline LogBERT & 87.02 & 78.10 & 82.32 & 89.40 & 92.32 & 90.83 & 96.75 & 96.52 & 96.64 \\ LogEDL (w. ENL) & 89.14 & 84.59 & 86.81 & 97.43 & 92.54 & 94.92 & 96.70 & 96.96 & 96.83 \\ LogEDL (w. EMSE) & 89.93 & 84.62 & 87.19 & 97.46 & 92.34 & 94.83 & 96.99 & 97.80 & 97.39 \\ LogEDL (w. ECE) & **90.06** & **92.80** & **91.41** & **99.84** & **97.26** & **98.53** & **97.80** & **98.08** & **97.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments on different evidence loss functions. the uncertainty of prediction outcomes, thereby augmenting the model's robustness and accuracy in the context of anomalous log detection tasks. This functionality is similar to open-set recognition patterns. The experimental results show that compared to state-of-the-art anomaly detection methods, LogEDL can maximally learn various contextual patterns of normal log sequences, thereby achieving advanced performance in anomaly detection tasks. In the future, we plan to further extend LogEDL to large-scale and diverse log datasets to establish a foundational model for log sequence analysis. The conceptualization and design of the study were led by [PERSON], [PERSON], [PERSON] and [PERSON]. Experiments were carried out by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Data were analyzed by [PERSON], [PERSON] and [PERSON]. The framework was developed by [PERSON], [PERSON] and [PERSON]. The manuscript was primarily written by [PERSON] and [PERSON], with substantial contributions from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Provided critical feedback and helped shape the research, analysis, and manuscript. [PERSON] and [PERSON] made significant contributions to the preparation, execution, and analysis of this study, which justifies a shared first-authorship. As [PERSON] initiated the study (together with [PERSON], [PERSON] and [PERSON]), he is listed first among the shared first authors. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant 62076035 and Grant U21B2022. Not applicable. The research data of this paper is available by contacting the corresponding author upon reasonable requests. The authors would like to thank the anonymous reviewers for their invaluable comments. Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company China Mobile Communications Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: