The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Effective Intrusion Detection System Using XGBoost
Open accessAbstract
As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains “security”, as more and more data get attached to the internet. This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others. XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results. The whole motive is to learn about the integrity of data and have a higher accuracy in the prediction of data. By doing so, the amount of mischievous data floating in a network can be minimized, making the network a secure place to share information. The more secure a network is, the fewer situations where data is hacked or modified. By changing various parameters of the model, future research can be done to get the most out of the data entering and leaving a network. The most important player in the network is data, and getting to know it more closely and precisely is half the work done. Studying data in a network and analyzing the pattern and volume of data leads to the emergence of a solid Intrusion Detection System (IDS), that keeps the network healthy and a safe place to share confidential information.
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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Revathi, S., & Malathi, A. (2013) · Cited by 413
Performance analysis of NSL-KDD dataset using ANN
Ingre, B., & Yadav, A. (2015) · 2015 International Conference on Signal Processing and Communication Engineering Systems · doi:10.1109/spaces.2015.7058223 · Cited by 374
Learning rules for anomaly detection of hostile network traffic
Mahoney, M. V., & Chan, P. K. (2003) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250987 · Cited by 201
A Hybrid Intelligent Approach for Network Intrusion Detection
Panda, M., Abraham, A., & Patra, M. R. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.827 · Cited by 169
Automatic network intrusion detection: Current techniques and open issues
Catania, C. A., & Garino, C. G. G. (2012) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2012.05.013 · Cited by 131
Semi-supervised co-training and active learning based approach for multi-view intrusion detection
Mao, C.-H., Lee, H.-M., Parikh, D., Chen, T., & Huang, S. (2009) · ACM Symposium on Applied Computing · doi:10.1145/1529282.1529735 · Cited by 49
An effective unsupervised network anomaly detection method
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2012) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1145/2345396.2345484 · Cited by 48
Comparison of classification techniques for intrusion detection dataset using WEKA
Garg, T., & Khurana, S. S. (2014) · International Conference on Recent Advances and Innovations in Engineering · doi:10.1109/icraie.2014.6909184 · Cited by 42
Semi-supervised Learning for False Alarm Reduction
Chiu, C.-Y., Lee, Y., Chang, C., Luo, W., & Huang, H.-C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14400-4_46 · Cited by 38
Cited by
Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4150 · Cited by 1,288
Duan, J., Asteris, P. G., Nguyen, H., Bui, X.-N., & Moayedi, H. (2021) · Engineering with Computers · doi:10.1007/s00366-020-01003-0 · Cited by 357
An efficient XGBoost–DNN-based classification model for network intrusion detection system
Devan, P., & Khare, N. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04708-x · Cited by 292
Towards Quantum-Enhanced Machine Learning for Network Intrusion Detection
Gouveia, A., & Correia, M. (2020) · 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca51143.2020.9306691 · Cited by 52
Deep Learning-Driven User Legitimacy Prediction Using Keystroke and Mouse Behavioural Dynamics
AbouRida, O., Nashaat, M., & Gamal El-din Saad, N. (2024) · 2024 International Conference on Computer and Applications (ICCA) · doi:10.1109/icca62237.2024.10928042 · Cited by 4
Metadata
- Authors
- Dhaliwal, S. S., Nahid, A.-A., & Abbas, R.
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2018-06-21
- Volume
- 9
- Issue
- 7
- Pages
- 149
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W2808779485
- Cited by
- 503
- References
- 11
- FWCI
- 14.47
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
