Machine Learning for Securing Traffic in Computer Networks

نویسندگان

چکیده

Computer network attacks are among the most significant and common threats against computer-wired wireless communications. Intrusion detection technology is used to secure computer networks by monitoring traffic identifying attacks. In this paper, we investigate evaluate application of four machine learning classification algorithms for that target networks: DDoS, Brute Force Web, SQL Injection attacks, in addition Benign Traffic. A public dataset 80 features was build models using Random Forest, Logistic Regression, CN2, Neural Networks. The constructed were evaluated based on 10-fold cross-validation Classification Accuracy (CA), Area under Curve (AUC), F1, Recall, Specificity, Sensitivity metrics Confusion Matrix, Calibration, Lift, ROC plots. Forest model achieved 98% CA score 99% AUC score, while regression 90% score.

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ژورنال

عنوان ژورنال: International Journal of Advanced Computer Science and Applications

سال: 2022

ISSN: ['2158-107X', '2156-5570']

DOI: https://doi.org/10.14569/ijacsa.2022.0131252