Anomaly Detection using combination of Autoencoder and Isolation Forest

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Abstract
The process of identifying abnormal objects or patterns that deviate from the typical behavior in a dataset or other observations is known as Anomaly Detection. It is an essential technique in many fields, such as cyber security, finance, transportation, and fraud detection. This paper combines an autoencoder and an isolation forest algorithm to enhance anomaly detection. The autoencoder is a neural network trained to reconstruct the input data, while the isolation forest is a tree-based algorithm that can identify outliers in the data. By combining these two methods, the autoencoder can learn a compact representation of the data, and the isolation forest can then be applied to the reconstructed data to identify anomalies. This combination effectively enhances the anomaly detection process in high-dimensional data when compared to utilizing the individual algorithms.- Title
- Anomaly Detection using combination of Autoencoder and Isolation Forest
- Author
- Almansoori, Mahmood
- Telek, Miklós
- Date of issue
- 2023
- Access level
- Open access
- Copyright owner
- Szerző
- Conference title
- 1st Workshop on Intelligent Infocommunication Networks, Systems and Services (WI2NS2)
- Conference place
- Budapest
- Conference date
- 2023.02.07
- Language
- en
- Page
- 25 - 30
- Subject
- Anomaly detection, autoencoder, isolation forest algorithm
- Version
- Post print
- Identifiers
- DOI: 10.3311/WINS2023-005
- Title of the container document
- 1st Workshop on Intelligent Infocommunication Networks, Systems and Services
- ISBN, e-ISBN
- 978-963-421-902-6
- Document type
- Konferenciaközlemény
- Document genre
- Konferenciacikk
- University
- Budapest University of Technology and Economics
- Faculty
- Faculty of Electrical Engineering and Informatics