Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree

Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a comprehensive review of intrusion detection techniques, and commonly used datasets for evaluation. It discusses eva...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.80348-80391
Main Authors: Azam, Zahedi, Islam, Md. Motaharul, Huda, Mohammad Nurul
Format: Article
Language:English
Subjects:
Anomalies
Computer crime
Computer hacking
Cybersecurity
Datasets
DDoS attacks
Decision analysis
decision tree
Decision trees
Deep learning
inductive learning
Intrusion
Intrusion detection
Intrusion detection system
Intrusion detection systems
Machine learning
Phishing
supervised and unsupervised learning
Surveys
Taxonomy
Unsupervised learning
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Summary:Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a comprehensive review of intrusion detection techniques, and commonly used datasets for evaluation. It discusses evasion techniques employed by attackers and the challenges in combating them to enhance network security. Researchers strive to improve IDS by accurately detecting intruders, reducing false positives, and identifying new threats. Machine learning (ML) and deep learning (DL) techniques are adopted in IDS systems, showing potential in efficiently detecting intruders across networks. The paper explores the latest trends and advancements in ML and DL-based network intrusion detection systems (NIDS), including methodology, evaluation metrics, and dataset selection. It emphasizes research obstacles and proposes a future research model to address weaknesses in the methodologies. The decision tree, known for its speed and user-friendliness, is proposed as a model for detecting result anomalies, combining findings from a comparative survey. This research aims to provide insights into building an effective decision tree-based detection framework.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3296444