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Jasak, Z and Banjanovic-Mehmedovic, L (2008). Detecting Anomalies by Benford's Law. In Proceedings of IEEE International Symposium on Signal Processing and Information Technology, 2008. ISSPIT 2008, pp. 453-458 .

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Costa, JI (2012). Desenvolvimento de metodologias contabilométricas aplicadas a auditoria contábil digital: uma proposta de análise da lei de Newcomb-Benford para os Tribunais de Contas. Thesis, Universidade Federal de Pernambuco, Recife, Brasil. POR View Complete Reference Online information Works that this work references Works that reference this work
İlkdoğan, S (2020). İç denetimin hileye yaklaşımında Benford Kanunu’nun uygulanması [Application of Benford's Law in internal audit's approach to fraud]. Masters Thesis, Balikesir University Institute of Social Sciences, Balikesir. TUR View Complete Reference Online information Works that this work references Works that reference this work
Jasak, Z (2010). Benfordov zakon i reinforcement učenje (Benford's Law and reinforcment learning) . MSc Thesis, University of Tuzla, Bosnia. SRP View Complete Reference Online information Works that this work references Works that reference this work
Jasak, Z (2017). Sum invariance testing and some new properties of Benford's law. Doctorial Dissertation, University of Tuzla, Bosnia and Herzegovina. View Complete Reference Online information Works that this work references Works that reference this work
Sethi, K, Kumar, R, Prajapati, N and Bera, P (2020). A Lightweight Intrusion Detection System using Benford's Law and Network Flow Size Difference. Proceedings of 2020 International Conference on COMmunication Systems NETworkS (COMSNETS). DOI:10.1109/COMSNETS48256.2020.9027422. View Complete Reference Online information Works that this work references Works that reference this work
Vishnu, U (2021). Deepfake Detection using Benford’s Law and Distribution Variance Statistic. International Research Journal of Engineering and Technology(IRJET) 08(10), pp. 712-719. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Wiryadinata, D, Sugiharto, A and Tarno (2023). The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors. Journal of Information Systems Engineering & Business Intelligence 9(2), pp. 239-252. DOI:10.20473/jisebi.9.2.239-252. View Complete Reference Online information Works that this work references No Bibliography works reference this work