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 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.

Automated summary

Cyber-attacks present increasing challenges in accurately detecting intrusions, compromising data confidentiality, integrity, and availability. This review provides an overview of recent IDS taxonomy, intrusion detection techniques, and commonly used evaluation datasets. It discusses evasion techniques used by attackers and challenges faced in countering them to improve network security. Researchers are adopting machine learning (ML) and deep learning (DL) techniques in IDS systems, showing promise in efficient detection of intrusions across networks. The review explores the latest trends and advancements in ML and DL-based network intrusion detection systems (NIDS), including methodology, evaluation metrics, and dataset selection. It highlights research obstacles and proposes a future research model to address weaknesses in methodologies. The decision tree model is suggested for detecting result anomalies by combining findings from a comparative survey, aiming to provide insights into building an effective decision tree-based detection framework.