Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence-Reference-Cited by-同舟云学术

Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence

Published:2021 Issue: Volume:47 Page:6325-6331
ISSN:2214-7853
Container-title:Materials Today: Proceedings
language:en
Short-container-title:Materials Today: Proceedings

Author:

Dubey Ghanshyam Prasad,Bhujade Dr. Rakesh Kumar

Publisher

Elsevier BV

Subject

General Medicine

Reference22 articles.

1. J. Jabez, B. Muthukumar, Intrusion Detection System (IDS): anomaly detection using outlier detection approach, in: Proceedings of International Conference on Intelligent Computing, Communication and Convergence (ICCC – 2015), published under Procedia Computer Science, 48, 338–346, Science Direct, ELSEVIER, 2015.

2. V. Mane, S. Pawar, Anomaly based IDS using Back Propagation Neural Network, Int. J. Comput. Applications (IJCA) [0975 - 8887], 136(10) (February 2016) 29–34.

3. A. Ossowicka, M. Pietrolaj, J. Ruminski, A survey of neural networks usage for intrusion detection systems, J. Ambient Intell. Human. Comput., SPRINGER, May 2020.

4. R. Sharma, H. Kalita, P. Borah, Analysis of Machine Learning Techniques based Intrusion Detection Systems, in: Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, published under Smart Innovation, Systems and Technologies, Volume 44, Page 485 – 493, SPRINGER 2016.

5. S. Kumar, A. Viinikainen, T. Hamalainen, Machine learning classification model for network based intrusion detection system, in: Proceedings of 11th International Conference for Internet Technology and Secured Transactions (ICITST – 2016), Page 242 – 249, IEEE 2016.

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Case Study: Understanding Internet Anomalies;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

2. Feature Engineering and Computer Vision for Cybersecurity;Advances in Information Security, Privacy, and Ethics;2024-02-23

3. Strengthening Network Security: Deep Learning Models for Intrusion Detection with Optimized Feature Subset and Effective Imbalance Handling;Computers, Materials & Continua;2024

4. APELID: Enhancing real-time intrusion detection with augmented WGAN and parallel ensemble learning;Computers & Security;2024-01

5. A multi-constraint transfer approach with additional auxiliary domains for IoT intrusion detection under unbalanced samples distribution;Applied Intelligence;2023-12-30