Cross Reference Up

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.

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.


Maurus, S and Plant, C (2017). Let's See Your Digits: Anomalous-State Detection using Benford's Law. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp. 977–986. DOI:10.1145/3097983.3098101. 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
Sun, L, Ho, A, Xia, Z, Chen, J and Zhang, M (2019). Development of an Early Warning System for Network Intrusion Detection Using Benford’s Law Features. In: Meng W., Furnell S. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2019. Communications in Computer and Information Science, vol 1095. Springer, Singapore. DOI:10.1007/978-981-15-0758-8_5. View Complete Reference Online information Works that this work references Works that reference this work
Sun, L, Ho, ATS, Xia, Z, Chen, J, Huang, X and Zhang, Y (2017). Detection and Classification of Malicious Patterns In Network Traffic Using Benford’s Law. 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, pp. 864-872. DOI:10.1109/APSIPA.2017.8282154. View Complete Reference Online information Works that this work references Works that 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