• Dr.P K Venkateswar Lal
• Ch. Siva Kamakshi

Cyber attacks increase the difficulty of gaining access; increases risks to data confidentiality, integrity and availability. This review article presents the latest IDS classification, a comprehensive review of intrusion detection techniques, and commonly used evaluation data. It discusses the evasion techniques used by attackers and the challenges of combat strategies to improve cybersecurity. Researchers are trying to improve IDS by accurately identifying attackers, reducing false alarms, and identifying new threats. IDS systems use machine learning (ML) and deep learning (DL) technologies and have proven their ability to detect network intruders. This paper reviews the latest topics and developments in ML and DL- based network intrusion detection systems (NIDS), including methods, metrics, and data selection. It highlights research limitations and suggests future research directions to address methodological weaknesses. Decision trees, known for their speed and ease of use, are recommended as a model for controlling uncertainty in the results, together with the results of comparative studies. This study aims to provide insight into the creation ofa good decision tree-based search engine.

Decision tree, Intrusion detection system, Machine learning, Deep learning, Detection accuracy, Support ve ctor machine, Unsuper vised Learning, Types of Attacks

"Comparative analysis of intrusion detection system and machine learning based model analysis using decision tree", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.384-391, June-2024, Available :http://www.jetir.org/papers/JETIRGL06065.pdf

"Comparative analysis of intrusion detection system and machine learning based model analysis using decision tree", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp384-391, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06065.pdf