Hybrid Anomaly Detection Method Using Community Detection in Graph and Feature Selection

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MIRZAEI M.; Mehabadi A.

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Paper Information

Journal: JOURNAL OF ELECTRONIC AND CYBER DEFENCE Year:2020 | Volume:8 | Issue:1 Page(s): 17-24

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Information Journal Paper

Title

Hybrid Anomaly Detection Method Using Community Detection in Graph and Feature Selection

Author(s)

MIRZAEI M. | Mehabadi A. | Issue Writer Certificate

Keywords

Anomaly detectionQ1

Social networksQ1

Data miningQ1

Graph miningQ1

Abstract

Anomaly detection is an important issue in a wide range of applications, such as security, health and intrusion detection in Social networks. Most of the developed methods only use graph structural or content information to detect anomalies. Due to the integrated structure of many networks, such as Social networks, applying these methods faces limitations and this has led to the development of hybrid methods. In this paper, a proposed hybrid method for Anomaly detection is presented based on community detection in graph and feature selection which exploits anomalies as incompatible members in communities and uses an algorithm based on the detection and combination of similar communities. The experimental results of the proposed method on two datasets with real anomalies demonstrate its capability in the detection of anomalous nodes which is comparable to the latest scientific methods.

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APA: Copy

MIRZAEI, M., & Mehabadi, A.. (2020). Hybrid Anomaly Detection Method Using Community Detection in Graph and Feature Selection. JOURNAL OF ELECTRONIC AND CYBER DEFENCE, 8(1 ), 17-24. SID. https://sid.ir/paper/361828/en

Vancouver: Copy

MIRZAEI M., Mehabadi A.. Hybrid Anomaly Detection Method Using Community Detection in Graph and Feature Selection. JOURNAL OF ELECTRONIC AND CYBER DEFENCE[Internet]. 2020;8(1 ):17-24. Available from: https://sid.ir/paper/361828/en

IEEE: Copy

M. MIRZAEI, and A. Mehabadi, “Hybrid Anomaly Detection Method Using Community Detection in Graph and Feature Selection,” JOURNAL OF ELECTRONIC AND CYBER DEFENCE, vol. 8, no. 1 , pp. 17–24, 2020, [Online]. Available: https://sid.ir/paper/361828/en

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