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ARTICLE

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

S. Sadhasivam1, P. Valarmathie2, K. Dinakaran3,*

1 Department of Computer Science and Engineering, K. S. R College of Engineering, Thiruchengode, India
2 Centre for Artificial Intelligence, Chennai Institute of Technology, Chennai, India
3 Department of Computer Science and Engineering, S. A. Engineering College, Chennai, India

* Corresponding Author: K. Dinakaran. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 461-479. https://doi.org/10.32604/iasc.2023.028650

Abstract

With the vast advancements in Information Technology, the emergence of Online Social Networking (OSN) has also hit its peak and captured the attention of the young generation people. The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user. However, the attackers also target this height of OSN utilization, explicitly creating the clones of the user’s account. Various clone detection mechanisms are designed based on social-network activities. For instance, monitoring the occurrence of clone edges is done to restrict the generation of clone activities. However, this assumption is unsuitable for a real-time environment and works optimally during the simulation process. This research concentrates on modeling and efficient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy. This model does not rely on assumptions. Here, an ensemble Adaptive Random Subspace is used for classifying the clone victims with k-Nearest Neighbour (k-NN) as a base classifier. The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results. When the weighted node’s threshold value is higher, the trust establishment is terminated, and the clones are ranked and sorted in the higher place for termination. Thus, the victims are alert to the clone propagation over the online social networking end, and the validation is done using the MATLAB 2020a simulation environment. The model shows a better trade-off than existing approaches like Random Forest (RF), Naïve Bayes (NB), and the standard graph model. Various performance metrics like True Positive Rate (TPR), False Alarm Rate (FAR), Recall, Precision, F-measure, and ROC and run time analysis are evaluated to show the significance of the model.

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Cite This Article

APA Style
Sadhasivam, S., Valarmathie, P., Dinakaran, K. (2023). Malicious activities prediction over online social networking using ensemble model. Intelligent Automation & Soft Computing, 36(1), 461-479. https://doi.org/10.32604/iasc.2023.028650
Vancouver Style
Sadhasivam S, Valarmathie P, Dinakaran K. Malicious activities prediction over online social networking using ensemble model. Intell Automat Soft Comput . 2023;36(1):461-479 https://doi.org/10.32604/iasc.2023.028650
IEEE Style
S. Sadhasivam, P. Valarmathie, and K. Dinakaran "Malicious Activities Prediction Over Online Social Networking Using Ensemble Model," Intell. Automat. Soft Comput. , vol. 36, no. 1, pp. 461-479. 2023. https://doi.org/10.32604/iasc.2023.028650



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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