This work is cited by the following items of the Benford Online Bibliography:
Alipour, A and Alipour, S (2019). Application of Benford’s Law in Analyzing Geotechnical Data. Civil Engineering Infrastructures Journal 52(2), pp. 323 – 334. DOI:10.22059/ceij.2019.272005.1534. | ||||
Asadi, AN (2015). An approach for detecting anomalies by assessing the inter-arrival time of UDP packets and flows using Benford's law. 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, pp. 257-262. DOI:10.1109/KBEI.2015.7436057. | ||||
Blondeau Da Silva, S (2020). Limits of Benford’s Law in Experimental Field. International Journal of Applied Mathematics 33(4), pp. 685-695. DOI:10.12732/ijam.v33i4.12. | ||||
Chi, D (2020). First Digit Phenomenon in Number Generation Under Uncertainty: Through the Lens of Benford’s Law. Master's thesis, School of Psychology, University of Sydney. | ||||
Corazza, M, Ellero, A and Zorzi, A (2018). The importance of being “one” (or Benford’s law). Lettera Matematica 6(1), pp. 33–39. DOI:10.1007/s40329-018-0218-4. | ||||
Ducharme, RG, Kaci, S and Vovor-Dassu ,C (2020). Smooths Tests of Goodness-of-fit for the Newcomb-Benford distribution. Preprint: arXiv:2003.00520v1 [math.ST]. FRE | ||||
Gauvrit, N, Houillon, J-C and Delahaye, J-P (2017). Generalized Benford’s Law as a Lie Detector. Advances in Cognitive Psychology 13(2), pp. 121-127. DOI:10.5709/acp-0212-x. | ||||
Gottwalt, F, Waller, A and Liu, W (2016). Natural Laws as a Baseline for Network Anomaly Detection. In: Proceedings of 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 370-377. DOI:10.1109/TrustCom.2016.0086. | ||||
Horton, J, Kumar, DK and Wood, A (2020). Detecting academic fraud using Benford law: The case of Professor James Hunton. Research Policy 49(8), 104084 . DOI:10.1016/j.respol.2020.104084. | ||||
Iorliam, A (2019). Combination of Natural Laws (Benford’s Law and Zipf’s Law) for Fake News Detection. In: Cybersecurity in Nigeria. SpringerBriefs in Cybersecurity. Springer, Cham. DOI:10.1007/978-3-030-15210-9_3. | ||||
Iorliam, A, Tirunagari, S, Ho, ATS, Li, S, Waller, A and Poh, N (2017). "Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law. arXiv:1609.04214v2 [cs.CR], last accessed February 6, 2017. | ||||
Joksimović, D, Knežević, G, Pavlović, V, Ljubić, M and Surovy, V (2017). Some Aspects of the Application of Benford’s Law in the Analysis of the Data Set Anomalies. In: Knowledge Discovery in Cyberspace: Statistical Analysis and Predictive Modeling. New York: Nova Science Publishers, pp. 85–120. ISSN/ISBN:978-1-53610-566-7. | ||||
Li, F, Han, S, Zhang, H, Ding, J, Zhang, J and Wu, J (2019). Application of Benford’s law in Data Analysis. Journal of Physics: Conference Series 1168, pp. 032133. DOI:10.1088/1742-6596/1168/3/032133. | ||||
Miller, SJ (ed.) (2015). Benford's Law: Theory and Applications. Princeton University Press: Princeton and Oxford. ISSN/ISBN:978-0-691-14761-1. | ||||
Nakatoh, T, Suzuki, T, Kamimasu. Y and Hirokawa. S (2020). Detection of Unnatural Parts of Statistical Data. Information Engineering Express 6(2), pp. 20 – 36. | ||||
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. | ||||
Suzuki, T, Kamimasu, T, Nakatoh, T and Hirokawa, S (2018). Identification of Unnatural Subsets in Statistical Data. 7th International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 74-80. DOI:10.1109/IIAI-AAI.2018.00024. |