• V SULOCHANA MCA., PH.D.,
• B VALARMATHI

With the rapid evolution of the Internet, the landscape of cyber-attacks is constantly changing, leading to a rather pessimistic outlook on cyber security. This article delves into the realm of network analysis for intrusion detection, specifically focusing on the implementation of Machine Learning (ML) and Deep Learning (DL) techniques. A comprehensive tutorial description is provided for each ML/DL method, accompanied by an examination of relevant research papers. These papers were meticulously indexed, read, and summarized based on their temporal or thermal correlations. Given the paramount importance of data in ML/DL methods, the article also sheds light on commonly utilized network datasets within this domain. Furthermore, it addresses the challenges associated with employing ML/DL for cyber security and offers valuable suggestions for future research directions. Notably, the KDD data set emerges as a well-established benchmark in the field of Intrusion Detection techniques. Extensive efforts are being made to enhance intrusion detection strategies, with equal emphasis placed on the quality of data used for training and testing the detection model. This project undertakes a comprehensive analysis of the KDD data set, specifically focusing on four distinct attribute classes: Basic, Content, Traffic, and Host. To categorize these attributes, the Modified Random Forest (MRF) approach is employed. Keywords: Intrusion Detection, Feature Selection, Machine Leaning

IIntrusion Detection, Machine Learning, Deep Learning, Network Analysis, Cyber Security, KDD dataset, Feature Selection, Modified Random Forest, Data Quality

"INTRUSION DETECTION SYSTEM USING VOTING BASED NEURAL NETWORK ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a770-a777, April-2024, Available :http://www.jetir.org/papers/JETIR2404097.pdf

"INTRUSION DETECTION SYSTEM USING VOTING BASED NEURAL NETWORK ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa770-a777, April-2024, Available at : http://www.jetir.org/papers/JETIR2404097.pdf