Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques
Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusin...
Saved in:
| Main Authors: | Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10833631/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing accuracy through ensemble based machine learning for intrusion detection and privacy preservation over the network of smart cities
by: Mudita Uppal, et al.
Published: (2025-02-01) -
Web Traffic Anomaly Detection Using Isolation Forest
by: Wilson Chua, et al.
Published: (2024-11-01) -
Research on traffic representation in network anomaly detection
by: SUN Jianwen, et al.
Published: (2025-01-01) -
Enhanced anomaly traffic detection framework using BiGAN and contrastive learning
by: Haoran Yu, et al.
Published: (2024-11-01) -
NMF-NAD:detecting network-wide traffic anomaly based on NMF
by: Xiang-lin WEI, et al.
Published: (2012-04-01)