Cross Reference Up

Mbona, I and Eloff, JHP (2022). Feature selection using Benford’s law to support detection of malicious social media bots. Information Sciences 582, pp. 369-381.

This work is cited by the following items of the Benford Online Bibliography:

Note that this list may be incomplete, and is currently being updated. Please check again at a later date.

Bhosale, S (2021). Identifying Bots on Twitter with Benford’s Law. Masters project, Dept. of Computer Science, San Jose State University. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Bhosale, S and Di Troia, F (2022). Twitter Bots’ Detection with Benford’s Law and Machine Learning. In Proceedings of Silicon Valley Cybersecurity Conference. SVCC 2022. Communications in Computer and Information Science, vol 1683, Bathen, L., Saldamli, G., Sun, X., Austin, T.H., Nelson, A.J. (eds). Springer, Cham. DOI:10.1007/978-3-031-24049-2_3. View Complete Reference No online information available Works that this work references No Bibliography works reference this work
Dutta, A, Choudhury, MR and De, AK (2022). A Unified Approach to Fraudulent Detection. International Journal of Applied Engineering Research 17(2), pp. 110-124. ISSN/ISBN:0973-4562. View Complete Reference Online information Works that this work references Works that reference this work
Mbona, I and Eloff, JHP (2023). Classifying social media bots as malicious or benign using semi-supervised machine learning. Journal of Cybersecurity 9(1), p.tyac015 . DOI:10.1093/cybsec/tyac015. View Complete Reference Online information Works that this work references No Bibliography works reference this work