• Ms. Sakshi Sainath Chaudhari
• Mr. Satyajit Mulay
• Ms. Vaishnavi Kasat
• Mr. Tanay Sasane
• Mr. Siddhant Navthar

In today's evolving cyber threat landscape, traditional intrusion detection methods often fall short in detecting novel and sophisticated attacks. This paper proposes a Hybrid Network Intrusion Detection System (HNIDS) that integrates both anomaly-based and signature-based detection approaches to enhance accuracy and adaptability. The signature-based component efficiently identifies known threats using predefined patterns, while the anomaly-based module detects deviations from normal behavior, enabling the discovery of previously unseen intrusions. By combining the strengths of both techniques, the hybrid system achieves improved detection rates and reduced false positives. Experimental results demonstrate the effectiveness of the proposed model in securing network environments against a wide range of cyber attacks.

Network Security, Intrusion Detection System (IDS), Anomaly Detection, Signature-based Detection, Hybrid IDS, Cybersecurity, Machine Learning, Threat Detection, False Positive Reduction, Network Traffic Analysis

"Hybrid Network Intrusion Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.g234-g243, May-2025, Available :http://www.jetir.org/papers/JETIR2505725.pdf

"Hybrid Network Intrusion Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppg234-g243, May-2025, Available at : http://www.jetir.org/papers/JETIR2505725.pdf