Transmission Deviation based Windowed Training for Intrusion Detection on Streaming Data |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-1 |
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| Year of Publication : 2024 | ||
| Authors : A. Sagaya Priya, S. Britto Ramesh Kumar | ||
| DOI : 10.14445/22312803/IJCTT-V72I1P107 | ||
How to Cite?
A. Sagaya Priya, S. Britto Ramesh Kumar, "Transmission Deviation based Windowed Training for Intrusion Detection on Streaming Data," International Journal of Computer Trends and Technology, vol. 72, no. 1, pp. 40-47, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I1P107
Abstract
New and increased cyber-attacks have been launched frequently on network systems due to the large number of highly sensitive data transmitted in these systems. Hence, it becomes mandatory to improve the intrusion detection systems' capability and handle the high variations in data distributions that are common on systems experiencing concept drift. The proposed Transmission Deviation based Windowed Model (TDWM) for intrusion detection on streaming network data is a novel approach that addresses the need for improved intrusion detection systems in the face of high variations in data distributions. The TDWM model considers imbalance levels and is designed to handle varied imbalance levels effectively, ensuring unbiased training. Two training models have been designed, each level capable of handling varied imbalance levels. Retraining of models is triggered based on the drift levels, ensuring that the model never becomes obsolete. Experiments were performed on three different intrusion detection datasets containing varied imbalances and varied drift levels. Experimental results and comparisons indicate the model exhibits high accuracy levels of >97% over all three datasets. Such high performance on varied datasets indicates the model's capability to handle data with varied distributions and its ability to be deployed in real time.
Keywords
Network intrusion detection, Ensemble modeling, Boosting, Stacking, Time window, Online training.
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