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Reseach Article
Survey of Intrusion Detection of Imbalanced Network based on Machine Learning Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 23 |
| Year of Publication: 2023 |
| Authors: Rachita Kulshrestha, Chetan Gupta, Ritu Shrivastava |
10.5120/ijca2023922980
|
Rachita Kulshrestha, Chetan Gupta, Ritu Shrivastava . Survey of Intrusion Detection of Imbalanced Network based on Machine Learning Algorithm. International Journal of Computer Applications. 185, 23 ( Jul 2023), 27-32. DOI=10.5120/ijca2023922980
Abstract
Intrusion detection is the process of analyzing the network packets to identify if the packet is legitimate or anomalous. The major challenges involved in this domain includes the huge volume of data for training and the fast and streaming data that is to be provided for the prediction process. The accuracy and timely detection should be ensured by Network Intrusion Detection System (NIDS). For intrusion detection in balance and imbalance network traffic, machine learning and deep learning methods can be used. In this paper a survey of different intrusion detection systems based on machine learning and deep learning methods is performed. The proposed system adds on ensemble learning approach to improve accuracy. A review on various intrusion detection system (IDS) using the techniques in machine learning is been put forwarded.
References
- Kezhou Ren, Maohuan Wang, Yifan Zeng and Yingchao Zhang, “An Unmanned Network Intrusion Detection Model Based on Deep Reinforcement Learning”, IEEE International Conference on Unmanned Systems (ICUS), IEEE 2022.
- R. Ahsan, W. Shi, X. Ma, and W. L. Croft, “A comparative analysis of CGAN-based oversampling for anomaly detection,” IET Cyberphysical Systems: Theory & Applications, vol. 7, no. 1, pp. 40–50, Mar. 2022.
- S. Dong, Y. Xia, and T. Peng, “Network Abnormal Traffic Detection Model Based on Semi-Supervised Deep Reinforcement Learning,” IEEE Transactions On Network And Service Management, vol. 18, no. 4, pp. 4197–4212, Dec. 2021.
- Lan Liu, Pengcheng Wang , Jun Lin, and Langzhou Liu, “Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning”, IEEE Access 2020.
- A. Raghavan, F. D. Troia, and M. Stamp, ``Hidden Markov models with random restarts versus boosting for malware detection,'' J. Comput. Virol. Hacking Techn., vol. 15, no. 2, pp. 97107, Jun. 2019.
- Zhiyou Zhang and Peishang Pan “A hybrid intrusion detection method based on improved fuzzy C-Means and SVM”, IEEE International Conference on Communication Information System and Computer Engineer (CISCE), pp. no. 210-214, Haikou, China 2019.
- Afreen Bhumgara and Anand Pitale, “Detection of Network Intrusion Using Hybrid Intelligent System”, IEEE International Conferences on Advances in Information Technology, pp. no. 167-172, Chikmagalur, India 2019.
- Ritumbhira Uikey and Dr. Manari Cyanchandani “Survey on Classification Techniques Applied to Intrusion Detection System and its Comparative Analysis”, IEEE 4th International Conference on Communication $ Electronics System (ICCES), pp. no. 459-466, Coimbatore, India 2019.
- Aditya Phadke, Mohit Kulkarni, Pranav Bhawalkar and Rashmi Bhattad “A Review of Machine Learning Methodologies for Network Intrusion Detection”, IEEE 3rd National Conference on Computing Methodologies and Communication (ICCMC), pp. no. 703-709, Erode, India 2019.
- S. Sivantham, R.Abirami and R.Gowsalya “Comapring in Anomaly Based Intrusion Detection System for Networks”, IEEE International conference on Vision towards Emerging Trends in Communication and Networking (ViTECon), pp. no. 289-293, Coimbatore, India 2019.
- Azar Abid Salih and Maiwan Bahjat Abdulrazaq “Combining Best Features selection Using Three Classifiers in Intrusion Detection System”, IEEE International Conference on Advanced science and Engineering (ICOASE), pp. no. 453-459, Zakho - Duhok, Iraq 2019.
- Lukman Hakim and Rahilla Fatma Novriandi “Influence Analysis of Feature Selection to Network Intrusion Detection System Performance Using NSL-KDD Dataset”, IEEE International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), pp. no. 330-336, Jember, Indonesia 2019.
- T. Sree Kala and A. Christy, “An Intrusion Detection System Using Opposition Based Particle Swayam Optimization Algorithm and PNN”, IEEE International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, pp. no. 564-569, Coimbatore, India 2019.
- Xiaoyan Wang and Hanwen Wang “A High Performance Intrusion Detection Method Based on Combining Supervised and Unsupervised Learning”, IEEE Smart World, Ubiquitous Intelligence $ Computing Advanced $ Trusted Computing, Scalable Computing, Internet of People and Smart City Innovations, pp. no. 889-897, Guangzhou, China 2018.
- P. Singh and M. Venkatesan, “Hybrid Approach for Intrusion Detection System”, IEEE International Conference on Current Trends Towards Converging Technologies (ICCTCT), pp. no. 654-659, Coimbatore, India 2018.
- M. Tavallaee, E. Bagheri, W, Lu and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 dataset”, IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. no. 892-899, Ottawa, India 2018.
- Karuna S. Bhosale and Assoc. prof. Maria, “Data Mining Based Advanced Algorithm for Intrusion Detection in Communication Networks”, IEEE International Conference on Computational Techniques, Electronics & Mechanical System (CTEMS), Belgaum, India 2018.
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