Time Series Based Network Intrusion Detection using MTF-Aided Transformer
This work addresses network intrusion detection for Software-Defined Networks, offering an incremental improvement in handling sparse data scenarios.
The paper tackles time series classification for network intrusion detection in Software-Defined Networks by proposing a Markov Transition Field-aided Transformer model, which outperforms baseline models in data-constrained environments and achieves competitive training and inference times.
This paper introduces a novel approach to time series classification using a Markov Transition Field (MTF)-aided Transformer model, specifically designed for Software-Defined Networks (SDNs). The proposed model integrates the temporal dependency modeling strengths of MTFs with the sophisticated pattern recognition capabilities of Transformer architectures. We evaluate the model's performance using the InSDN dataset, demonstrating that our model outperforms baseline classification models, particularly in data-constrained environments commonly encountered in SDN applications. We also highlight the relationship between the MTF and Transformer components, which leads to better performance, even with limited data. Furthermore, our approach achieves competitive training and inference times, making it an efficient solution for real-world SDN applications. These findings establish the potential of MTF-aided Transformers to address the challenges of time series classification in SDNs, offering a promising path for reliable and scalable analysis in scenarios with sparse data.