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Beschreibung
Intrusion detection systems (IDS) are important elements in network defenses to help protect against increasingly sophisticated cyber attacks. This project objective presents a novel anomaly detection technique that can be used to detect previously unknown attacks on a network by identifying attack features. This effects-based feature identification method uniquely combines k-means clustering; NaiveBayes feature selection and C4.5 decision tree classification for finding cyber attacks with a high degree of accuracy and it used KDD99CUP dataset as input. Basically it detects whether the attacks are there or not, like IPSWEEP, NEPTUNE, SMURF.
Details
| Verlag | LAP LAMBERT Academic Publishing |
| Ersterscheinung | 20. November 2020 |
| Maße | 22 cm x 15 cm x 0.4 cm |
| Gewicht | 96 Gramm |
| Format | Softcover |
| ISBN-13 | 9786203042290 |
| Seiten | 52 |
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