The vulnerability of Transformer-based NIDS to backdoor

The gap

Evidence profile

Stated in the synthesized section of the source papers, classified as general, drawn from work published between 2023 and 2026, spanning 5 journals. Those papers have been cited 151 times in total.

Supporting evidence — 5 representative gaps

• Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023) · IEEE Communications Surveys & Tutorials · doi

  No prior work evaluates the adversarial robustness of Transformer-based architectures specifically designed for intrusion detection systems. While Transformers have been applied to NIDS with reported high accuracy, and adversarial attacks against NIDS are well-documented, the intersection—how Transformer-based NIDS withstand adversarial perturbations—remains unexplored.

  generalstated in synthesizedevidence 5/5
  Keywords: prior work evaluates adversarial robustness transformer-based architectures specifically
• SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection (2023) · Computers & Security · cited 34× · doi

  Adversarial attack methods for NIDS lack domain-specific constraints that ensure realism in network traffic; most attacks are adapted from computer vision without accounting for the communication protocol and network flow constraints that define valid adversarial examples in intrusion detection.

  generalstated in synthesizedevidence 5/5
  Keywords: adversarial attack methods nids lack domain-specific constraints ensure
• A Transformer-based network intrusion detection approach for cloud security (2024) · Journal of Cloud Computing Advances Systems and Applications · cited 117× · doi

  Defense mechanisms against adversarial attacks on Transformer-based NIDS have not been evaluated; existing defenses (adversarial training, certified robustness) are studied for general DNNs or vision models, but their effectiveness and computational trade-offs for Transformer architectures in tabular intrusion detection remain unknown.

  generalstated in synthesizedevidence 5/5
  Keywords: defense mechanisms against adversarial attacks transformer-based nids have
• Advancing Backdoor Attack Detection in Transformer Models using Feature Squeezing and Statistical Anomaly Filtering Techniques (2026) · International Journal of Artificial Intelligence Research · doi

  The vulnerability of Transformer-based NIDS to backdoor attacks and poisoning during training has not been characterized, despite recent work on backdoor detection in Transformers and the known susceptibility of NIDS to adversarial manipulation during both training and testing phases.

  generalstated in synthesizedevidence 5/5
  Keywords: vulnerability transformer-based nids backdoor attacks poisoning during training
• Deep Learning-Based Anomaly Detection in Network Intrusion Detection Systems: A Comparative Evaluation (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  No benchmark evaluation exists comparing the adversarial robustness of different deep learning architectures (VAEs, VAE-GANs, AAEs, Transformers) for NIDS under standardized adversarial attack scenarios, limiting practitioners' ability to select architecturally robust models for deployment.

  generalstated in synthesizedevidence 5/5
  Keywords: benchmark evaluation exists comparing adversarial robustness different deep

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