Abstract
Over the last decade, there has been significant interest in detecting hybrid anomalies in Online Social Networks (OSN). However, there remain several questions regarding the evaluation of these systems and the nature of anomaly. In order to answer these questions, further research must be conducted on anomaly definitions and development for social networks. This is achieved through datasets that represent a balanced testing environment and contains anomalies with selected characteristics. In this paper, we propose an artificial injection agent as a security solution for evaluating detecting users’ abnormal behavior in OSN. The proposed agent creates synthetic anomaly and injects it into the data to utilize the detection accuracy in a Bayesian Network Classifier. We evaluated the effectiveness of the proposed technique concerning the detection of the injected anomalies. Using our approach, we were able to detect the injected anomalies with a success rate of 94% and no false alarm rates.
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Aljably, R., Al-Rodhaan, M., Tian, Y. (2022). Detecting Hybrid Anomalies Using an Unsupervised Approach in Online Social Networks. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_50
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DOI: https://doi.org/10.1007/978-981-19-0852-1_50
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