面向企业数据安全场景基于孤立森林算法的数据访问异常检测与告警收敛方法

Data Access Anomaly Detection and Alarm Aggregation Method Based on Isolation Forest Algorithm for Enterprise Data Security Scenarios

摘要: 随着数字经济的快速发展，数据安全成为企业关注的焦点。本文提出了一种基于孤立森林算法的异常行为告警收敛方法，旨在提升企业数据安全合规及业务安全。通过用户实体行为分析(UEBA)技术建立用户访问行为基线，并监测实时行为与基线的偏差，以识别异常行为。然而，UEBA技术在异常告警收敛方面存在不足，导致告警数量庞大，难以维护。为此，本文引入孤立森林算法，对UEBA检出的异常样本进行进一步分析和收敛，以提高告警质量，减少告警数量，实现可维护的异常告警。文章详细介绍了基于UEBA的数据访问异常行为检测技术，存在的问题及困难，以及孤立森林算法的原理和优势。此外，还探讨了孤立森林算法在数据访问异常行为检测场景的告警收敛应用，包括数据准备、模型训练、异常分析、样本收敛和结果解释等步骤。最终，本文总结了利用孤立森林算法进行异常分析和告警收敛的有效性，并对未来的发展方向进行了展望。

Abstract: With the rapid development of the digital economy, data security has become a focal point for enterprises. This paper proposes an anomaly alert convergence method based on the Isolation Forest algorithm to enhance enterprise data security compliance and business security. By establishing a baseline of user access behavior through User and Entity Behavior Analytics (UEBA) technology and monitoring real-time behavior deviations from the baseline, abnormal behaviors are identified. However, UEBA technology has shortcomings in alert convergence, leading to a large number of alerts that are difficult to maintain. To address this, this paper introduces the Isolation Forest algorithm to further analyze and converge the abnormal samples detected by UEBA, thereby improving alert quality, reducing the number of alerts, and achieving maintainable anomaly alerts. The article provides a detailed introduction to the data access anomaly behavior detection technology based on UEBA, the existing problems and difficulties, as well as the principles and advantages of the Isolation Forest algorithm. Additionally, it discusses the application of the Isolation Forest algorithm in alert convergence for data access anomaly behavior detection scenarios, including steps such as data preparation, model training, anomaly analysis, sample convergence, and result interpretation. Ultimately, the paper summarizes the effectiveness of using the Isolation Forest algorithm for anomaly analysis and alert convergence and provides an outlook on future development directions.