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Cyberattacks continue to become more frequent
and sophisticated, posing serious threats to network security.
The paper proposes a multi-stage machine learning approach
for improving cyberattack detection and classification
utilizing the CICIDS 2017 dataset, a comprehensive intrusion
detection reference. The suggested technique is divided into
three stages: anomaly detection using the One-Class Support
Vector Machine (OC-SVM), attack categorization with
CatBoost and LightGBM, and false-positive reduction to
improve Zero-Day attack detection. The use of Principal
Component Analysis (PCA) for dimensionality reduction and
balanced data sampling guarantees that the model is robust
and efficient. Results demonstrate that the CatBoost
algorithm surpasses LightGBM in key measures, with an F1-
weighted score of 0.97 and a Zero-Day recall rate of 0.91. A
solution for real-world cybersecurity applications that
advances the state of the art in machine learning-based
intrusion detection systems.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Bulut Karadağ
Gökhan Kesin
Sümeyye Alpay
İsmet Köroğlu
İsmail Fırat Çelik
Gökhan Görmüş
Aslıhan Çandır
Fatih Alagöz