• Vaishali Desai
• Nafisa Ansari

Purpose: In network communications, networks are one of the most vulnerable systems where security is a major issue. The aim is to study ANIDS under which genetic- neural network architecture is proposed. Methodology: This is a Descriptive type of research that took 8 months, and 12 research papers are studied. The observational tool is used. Findings: The variables included in the study were selected based on the literature. As per the literature review, machine learning techniques are used to identify attacks by combining different algorithms as there is no more research done on combining Genetic and Artificial neural networks. Contribution: There are many available misuse-based detection systems. However, most IDS lack the capability to detect previously unknown attacks. The anomaly intrusion detection system is the subset of intrusion detection systems which effectively finds both known as well as zero-day attacks. Anomaly IDS face problems such as high rate of false alarm. To overcome the problem of high false alarm rate we have proposed anomaly NIDS which uses genetic algorithm and artificial neural networks to detect intrusion and also classify the detected attacks into proper types. When there is an increase in false prediction rate the genetic-neural network algorithm is run automatically and the newer attacks are categorized into respective types such as probing, DOS, U2R, R2L. The results of this study offer guidance to the network administrator to act upon and on how to best secure their assets against attacks.

IDS, Anomaly Detection system (AIDS), Artificial Neural Network (ANN), Genetic Algorithm, ML

"STUDY OF ANOMALY BASED NIDS: MACHINE LEARNING TECHNIQUES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.219-224, June-2023, Available :http://www.jetir.org/papers/JETIRFY06029.pdf

"STUDY OF ANOMALY BASED NIDS: MACHINE LEARNING TECHNIQUES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. pp219-224, June-2023, Available at : http://www.jetir.org/papers/JETIRFY06029.pdf