A 2025 Survey on Machine Learning-Driven Network Intrusion Detection Systems: Trends, Challenges, and Future Directions
Year: 2025
Type: article
Abstract: 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.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410519422