RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System
Hebei University · Arizona State University
Abstract
Due to the rapid growth in network traffic and increasing security threats, Intrusion Detection Systems (IDS) have become increasingly critical in the field of cyber security for providing secure communications against cyber adversaries. However, there exist many challenges for designing a robust, efficient and accurate IDS, especially when dealing with high-dimensional anomaly data with unforeseen and unpredictable attacks. In this paper, we propose a Robust Transformer-based Intrusion Detection System (RTIDS) reconstructing feature representations to make a trade-off between dimensionality reduction and feature retention in imbalanced datasets. The proposed method utilizes positional embedding technique to…
Citation impact
- FWCI
- 33.91
- Percentile
- 100%
- References
- 52
Authors
4Topics & keywords
- Computer science
- Intrusion detection system
- Artificial intelligence
- Recurrent neural network
- Data mining
- Artificial neural network
- Machine learning
- Anomaly detection