Towards fuzzy anomaly detection-based security: a comprehensive review

Mohammad Masdari () and Hemn Khezri ()
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Mohammad Masdari: Islamic Azad University
Hemn Khezri: Afagh Higher Education Institute

Fuzzy Optimization and Decision Making, 2021, vol. 20, issue 1, No 1, 49 pages

Abstract: Abstract 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.

Keywords: Anomaly detection; ANFIS; Fuzzy logic; FCM; Feature selection; Neuro-fuzzy (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10700-020-09332-x

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