A Generalized and Robust Nonlinear Approach based on Machine Learning for Intrusion Detection-Reference-Cited by-同舟云学术

A Generalized and Robust Nonlinear Approach based on Machine Learning for Intrusion Detection

Published:2024-07-11 Issue:1 Volume:38 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Rahman Jakiur¹,Singh Jaskaran²,Nayak Soumen¹,Jena Biswajit¹,Mohanty Lopamudra³,Singh Narpinder⁴,Laird John R.⁵,Singh Rajesh⁶,Garg Deepak⁷,Khanna Narendra N.⁸,Fouda Mostafa M.⁹,Saba Luca¹⁰,Suri Jasjit S.²⁹¹¹¹²

Affiliation:

1. Department of CSE, SOA Deemed to be University, Bhubaneswar, Odisha, India

2. Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, California, USA

3. Department of Computer Science, ABES Engineering College, Ghaziabad, India

4. Department of Food Science, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

5. Heart and Vascular Institute, Adventist Health St. Helena, St Helena, California, USA

6. Division of Research and Innovation, Uttaranchal Institute of Technology, Dehradun, India

7. School of Computer Science and Artificial Intelligence, SR University, Warangal, India

8. Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India

9. Department of Electrical and Computer Engineering, Idaho State University, Pocatello, Idaho, USA

10. Department of Neurology, University of Cagliari, Cagliari, Italy

11. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

12. Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2024.2376983

Reference92 articles.

1. Abrar, I., Z. Ayub, F. Masoodi, and A. M. Bamhdi. 2020. A machine learning approach for intrusion detection system on NSL-KDD dataset. In 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India. IEEE.

2. A Review of the Advances in Cyber Security Benchmark Datasets for Evaluating Data-Driven Based Intrusion Detection Systems

3. Almseidin, M., M. Alzubi, S. Kovacs, and M. Alkasassbeh. 2017. Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia. IEEE.

4. Alrowaily, M., F. Alenezi, and Z. Lu. 2019. Effectiveness of machine learning based intrusion detection systems. Security, Privacy, and Anonymity in Computation, Communication, and Storage: 12th International Conference, SpaCCS 2019, Atlanta, GA, USA. Springer.

5. Amor, N. B., S. Benferhat, and Z. Elouedi. 2004. Naive Bayes vs decision trees in intrusion detection systems. In Proceedings of the 2004 ACM symposium on Applied computing, Nicosia, Cyprus.