Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
arXiv:2606.11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017. However, many existing studies neither supply their temporal modules with genuine sequence inputs nor evaluate under realistic, leakage-free conditions, making it unclear whether reported gains arise from true sequence-modeling capability. In this work, we reformulate CIC-IDS2017 as a temporal intrusion-detection task by constructing
The proliferation of deep learning, particularly Transformers, in cybersecurity applications necessitates a rigorous evaluation of their true efficacy under realistic conditions, especially as these models become more integrated into critical infrastructure. This paper pushes back on the hype by providing a realistic assessment of Transformer capabilities in intrusion detection.
This research provides a more grounded understanding of AI's capabilities in cybersecurity, highlighting potential overstatements in current academic benchmarks and prompting a re-evaluation of how effectively these advanced models handle genuine temporal sequences. It helps prevent misallocation of resources in developing AI-powered intrusion detection systems.
The understanding of Transformer utility in intrusion detection is refined, moving from a perception of near-perfect performance to a more nuanced view that emphasizes the need for proper temporal sequence handling and realistic evaluation. It implies that simply applying advanced models doesn't automatically translate to robust security.
- · Cybersecurity researchers
- · Organizations prioritizing robust IDS validation
- · Developers of genuinely temporal AI models
- · Developers of poorly validated AI-IDS
- · Organizations relying on superficial AI performance metrics
- · Academic studies with unrealistic benchmarks
Increased scrutiny of AI-powered intrusion detection systems and a greater demand for robust, leakage-free evaluation methodologies.
A shift in R&D focus towards building AI models that can truly capture temporal dependencies in network traffic, rather than just achieving high scores on flawed datasets.
Improved practical effectiveness of future AI-driven cybersecurity solutions, potentially leading to more resilient critical infrastructure defenses over time as fundamental issues are addressed.
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Read at arXiv cs.LG