Enhancing intrusion detection systems through dimensionality reduction: A comparative study of machine learning techniques for cyber security
Our research aims to improve automated intrusion detection by developing a highly accurate classifier with minimal false alarms. The motivation behind our work is to tackle the challenges of high dimensionality in intrusion detection and enhance the classification performance of classifiers, ultimat...
Saved in:
| Main Authors: | Faisal Nabi, Xujuan Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-01-01
|
| Series: | Cyber Security and Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772918423000206 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models
by: Fatima Alwahedi, et al.
Published: (2024-01-01) -
The Improved Network Intrusion Detection Techniques Using the Feature Engineering Approach with Boosting Classifiers
by: Hari Mohan Rai, et al.
Published: (2024-12-01) -
Revolutionizing smart grid security: a holistic cyber defence strategy
by: Bhushankumar Nemade, et al.
Published: (2024-12-01) -
Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques
by: Hanaa Attou, et al.
Published: (2023-09-01) -
Overview of anomaly detection techniques for industrial Internet of things
by: Haili SUN, et al.
Published: (2022-03-01)