In the data security context, anomaly detection is a branch of intrusion detection that can detect emerging intrusions and security attacks. A number of anomaly detection systems (ADSs) have been proposed in the literature that using various algorithms and techniques try to detect the intrusions and anomalies. This paper focuses on the ADS schemes which have applied fuzzy logic in combination with other machine learning and data mining techniques to deal with the inherent uncertainty in the intrusion detection process. For this purpose, it first presents the key knowledge about intrusion detection systems and then classifies the fuzzy ADS approaches regarding their utilized fuzzy algorithm. Afterward, it summarizes their major contributions and illuminates their advantages and limitations. Finally, concluding issues and directions for future researches in the fuzzy ADS context are highlighted.

在数据安全性上下文中，异常检测是入侵检测的一个分支，可以检测新兴入侵和安全攻击。文献中已经提出了许多异常检测系统（ADS），它们使用各种算法和技术来尝试检测入侵和异常。本文重点介绍了将模糊逻辑与其他机器学习和数据挖掘技术相结合的ADS方案，以应对入侵检测过程中固有的不确定性。为此，它首先介绍了有关入侵检测系统的关键知识，然后根据其使用的模糊算法对模糊ADS方法进行了分类。随后，总结了它们的主要贡献并阐明了它们的优点和局限性。最后，