A 2025 Survey on Machine Learning-Driven Network Intrusion Detection Systems: Trends, Challenges, and Future Directions
Tanya Gupta
, Dr. Sunita Soni , Dr. Shweta Kharya
Network Intrusion Detection System (NIDS), Cybersecurity, Machine Learning, Deep Learning, Artificial Intelligence, Anomaly Detection, Federated Learning, Intrusion Prevention Systems
Cybersecurity remains a critical concern in 2025 as networks expand and cyber threats grow more complex. Network Intrusion Detection Systems (NIDS) are fundamental in monitoring traffic and identifying malicious activities. Traditional detection methods often fall short in recognizing novel threats, which has led to a shift toward intelligent systems powered by Machine Learning (ML). This survey explores the application of ML in NIDS, highlighting recent developments, benchmark datasets, evaluation.
"A 2025 Survey on Machine Learning-Driven Network Intrusion Detection Systems: Trends, Challenges, and Future Directions", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a41-a44, May-2025, Available :https://ijsdr.org/papers/IJSDR2505007.pdf
Volume 10
Issue 5,
May-2025
Pages : a41-a44
Paper Reg. ID: IJSDR_302408
Published Paper Id: IJSDR2505007
Downloads: 0001332
Research Area: Science and Technology
Country: Dhamtari , Chhattisgarh , India
DOI: https://doi.org/10.56975/ijsdr.v10i5.302408
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
