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.
This work is cited by the following items of the Benford Online Bibliography:
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Mbona, I and Eloff, JHP (2022). Detecting Zero-day intrusion attacks using semi-supervised machine learning approaches. IEEE Access. DOI:10.1109/ACCESS.2022.3187116.
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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.
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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.
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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.
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Tirunagari, S, Abasolo, D, Iorliam, A, Ho, ATS and Poh, N (2017). Using Benford’s Law to Detect Anomalies in Electroencephalogram: An Application to Detecting Alzheimer’s Disease. In: Proceedings of 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Manchester, 2017, pp. 1-6. DOI:10.1109/CIBCB.2017.8058547.
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