• Akash Kalme
• Pratham Doke
• Nikita Gokhale
• Samruddhi Kadam
• Preeti Jain

The rapid evolution of the internet and communication technologies has led to a significant expansion in network size and the associated data volume. Consequently, this surge has given rise to new and sophisticated forms of cyber attacks, which present considerable challenges for maintaining the security and integrity of networks. In this context, the presence of intruders seeking to launch various malicious attacks within networks cannot be underestimated. To counteract these threats, Intrusion Detection Systems (IDS) play a pivotal role by scrutinizing network traffic to ensure the confidentiality, integrity, and availability of data. However, despite extensive research efforts, IDS still struggles with the need to enhance detection accuracy while reducing false alarm rates and addressing emerging forms of intrusions. Recently, the application of machine learning (ML) and deep learning (DL) techniques has emerged as a promising avenue to strengthen network-based IDS (NIDS) systems, aiming to detect intrusions efficiently. Machine Learning enhances NIDS by improving accuracy, adaptability to new threats, reducing false positives, and enabling the detection of complex and subtle anomalies. It also automates threat detection, reduces manual rule maintenance, and provides real-time monitoring, ultimately enhancing an organization's overall security posture.

Deep Learning, Machine Learning, Network Anomaly Detection, Network Intrusion Detection System, Network Security

"A Survey on Network Intrusion Detection System using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e116-e119, November-2023, Available :http://www.jetir.org/papers/JETIR2311418.pdf

"A Survey on Network Intrusion Detection System using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe116-e119, November-2023, Available at : http://www.jetir.org/papers/JETIR2311418.pdf