Detecting Cyber Attacks with High-Frequency Features using Machine Learning Algorithms
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Budapest Tech
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In computer networks, intrusion detection systems are used to detect cyber-attacks and anomalies. Feature selection is important for intrusion detection systems to scan the network quickly and accurately. On the other hand, analyzes performed using data with many attributes cause significant resource and time loss. In this study, unlike the literature studies, the frequency effects of the features in the data set are analyzed in detecting cyber-attacks on computer networks. Firstly, the frequencies of the features in the NSL-KDD data set were determined. Then, the effect of high-frequency features in detecting cyber-attacks has been examined with the widely used machine learning algorithms of Random Forest, J48, Naive Bayes, and Multi-Layer Perceptron. The performance of each algorithm is evaluated by considering Precision, False Positive Rate, Accuracy, and True Positive Rate statistics. Detection performances of different types of cyberattacks in the NSL-KDD dataset were analyzed with machine learning algorithms. Precision, Receiver Operator Characteristic, F1 score, recall, and accuracy statistics were chosen as success criteria of machine learning algorithms in attack detection. The results showed that features with high frequency are effective in detecting attacks.
Açıklama
Anahtar Kelimeler
Attribute selection, Cyberattacks, Machine Learning, IDS, NSL-KDD, Anomaly detection
Kaynak
Acta Polytechnica Hungarica
WoS Q Değeri
Q3
Scopus Q Değeri
Cilt
19
Sayı
7
