Abstract: In this paper, a new method called Anomaly Discover is provided for detecting anomalies in communities with mixed attributes (binary, numerical and categorical attributes). Our strategy tries to identify unusual users in Online Social Networks (OSN) communities and score them according to how far they deviate from typical users. Our ranking is based on both users’ attributes and network structure. Moreover, for effective anomaly detection, the context-selection process is performed for choosing relevant attributes that demonstrate a strong contrast between normal and abnormal users. So the anomaly score is defined as the degree of divergence in the network structure as well as a context-specific subset of attributes. To assess the efficacy of our model, we used real and artificial networks. We then compared the outcomes to those of two state-of-art models. The outcomes show that our model performs well since it outperforms other models and can pick up anomalies that competing models miss.

Hedia Zardi and Hajar Alrajhi, “Anomaly Discover: A New Community-based Approach for Detecting Anomalies in Social Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01404101

@article{Zardi2023,
title = {Anomaly Discover: A New Community-based Approach for Detecting Anomalies in Social Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01404101},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01404101},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Hedia Zardi and Hajar Alrajhi}
}