Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets
Optimizing the detection of intrusions is becoming more crucial due to the continuously rising rates and ferocity of cyber threats and attacks. One of the popular methods to optimize the accuracy of intrusion detection systems (IDSs) is by employing machine learning (ML) techniques. However, there a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2021-01-01
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| Series: | Security and Communication Networks |
| Online Access: | http://dx.doi.org/10.1155/2021/8836057 |