RESEARCH PAPER SECURITY
Machine Learning Approaches for Real-Time Cyber Threat Detection in Enterprise Networks
IEEE Transactions on Information Forensics and Security
Comprehensive research on implementing machine learning algorithms for detecting advanced persistent threats in enterprise network environments.
Abstract
This paper presents a novel approach to real-time cyber threat detection using ensemble machine learning techniques. We propose a hybrid model combining deep neural networks with traditional anomaly detection methods to identify sophisticated attack patterns in network traffic. Our experimental results demonstrate a 97.3% detection accuracy with a false positive rate of less than 0.5%. The system was tested on a dataset of 10 million network flows collected from enterprise environments over 12 months.
Authors
Your Name Dr. Jane Smith Prof. Michael Johnson
Tags
#machine-learning
#cybersecurity
#threat-detection
#network-security
#artificial-intelligence
Citations: 45 DOI: 10.1109/TIFS.2023.1234567
Read Publication