Anomaly-based intrusion detection systems using machine learning algorithm
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Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The increasing number of cyber-attacks highlights the need for improved intrusion detection
systems (IDS) that can detect and classify attacks accurately and efficiently. Machine
learning (ML) techniques have been shown to be effective in this regard, as they can learn
from data and identify patterns that may indicate an intrusion or anomaly.
In this research, we want to find a highly accurate and error-tolerant classifier for identifying
aberrant traffic in a network intrusion detection dataset. We will evaluate the effectiveness
of seven different machine learning techniques on a given dataset by using various
evaluation metrics such as accuracy, precision, recall, and F1-score. These measurements
allow us to evaluate the effectiveness of various algorithms and offer insightful information
about the advantages and disadvantages of each approach.
Our ultimate goal is to select the best-performing classifier that can accurately detect
anomalous traffic with minimal error. By identifying an effective classifier for NIDS, our
goal is to aid in the creation of stronger, more dependable systems that can defend against
cyberattacks and guarantee the safety of digital networks.
Açıklama
Anahtar Kelimeler
Instruction Detection System (IDS), Network IDS, Machine Learning, Cyber Security
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Al-Juboori, K. A. A. (2023). Anomaly-based intrusion detection systems using machine learning algorithm. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
