论文标题
基于AI的日志分析仪：一种实用的方法
AI based Log Analyser: A Practical Approach
论文作者
论文摘要
对数的分析是针对系统和网络弹性的故障或网络事件检测，研究和技术取证分析进行的重要活动。 AI算法在日志分析中的潜在应用可以增强这种复杂而费力的任务。但是，这种解决方案的限制是日志源的异质性，并且仅限于训练分类器的标签。当此类标签可用时，需要更新分类器。这项基于实践的研究旨在通过使用变压器结构来训练只有正常日志条目的新模型来应对这些挑战。通过多种形式的扰动进行对数增强，以作为特征学习的自我监督培训的一种形式。该模型通过使用有限的标签样本组合的形式进行了进一步的填充，以模仿现实世界中的情况，并具有标签的可用性。我们模型构造的实验结果通过比较评估测量结果为未来的实际应用铺平了道路。
The analysis of logs is a vital activity undertaken for fault or cyber incident detection, investigation and technical forensics analysis for system and cyber resilience. The potential application of AI algorithms for Log analysis could augment such complex and laborious tasks. However, such solution has its constraints the heterogeneity of log sources and limited to no labels for training a classifier. When such labels become available, the need for the classifier to be updated. This practice-based research seeks to address these challenges with the use of Transformer construct to train a new model with only normal log entries. Log augmentation through multiple forms of perturbation is applied as a form of self-supervised training for feature learning. The model is further finetuned using a form of reinforcement learning with a limited set of label samples to mimic real-world situation with the availability of labels. The experimental results of our model construct show promise with comparative evaluation measurements paving the way for future practical applications.
