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A Review: Machine Learning Approaches for Zero-Day Attacks Recognition

Sivakumar Nagarajan1
Technical Architect, I & I Software Inc, 2571 Baglyos Circle, Suite B-32, Bethlehem, PA-18020, USA

Published Online: March-April 2024

Pages: 105-109

References

1. Ariafar, Elham, and RasoulKiani. "Intrusion detection system using an optimized framework based on datamining techniques." 2017
IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2017.
2. Belavagi, Manjula C., and BalachandraMuniyal. "Performance evaluation of supervised machine learning algorithms for intrusion
detection." Procedia Computer Science 89.2016 (2016): 117-123.
3. Chiba, Zouhair, et al. "A cooperative and hybrid network intrusion detection framework in cloud computing based on snort and optimized
back propagation neural network." Procedia Computer Science 83 (2016): 1200- 1206.
4. Fung, Carol, and Raouf Boutaba, “Intrusion Detection”, Intrusion detection networks: a key to collaborative security. Auerbach
Publications, (2017): 21-37.
5. Hachmi, Fatma, KhadoujaBoujenfa, and Mohamed Limam. "Enhancing the Accuracy of Intrusion Detection Systems by Reducing the
Rates of False Positives and False Negatives Through Multi-Objective Optimization." Journal of Network and Systems Management 27.1
(2019): 93-120.
6. Pan, SinnoJialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009):
1345-1359.
7. Saleh, Ahmed I., Fatma M. Talaat, and Labib M. Labib. "A hybrid intrusion detection system (HIDS) based on prioritized k-nearest
neighbors and optimized SVM classifiers." Artificial Intelligence Review 51.3 (2019): 403-443.

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