@article{bibcite_16236,
  author = {Mert Nakip},
  title = {A predictive neural network architecture for early detection of low-rate cyberattacks},
  abstract = {<p>Low-Rate Denial of Service (LDoS) attacks pose a significant challenge to IoT networks due to their subtle and prolonged nature, often evading traditional intrusion detection systems. This paper presents IDQS (Intrusion Detection via QoS Prediction), a lightweight and proactive framework for early LDoS attack detection. IDQS integrates two new key components: (i) RTP-QoS, a Recurrent Trend Predictive Neural Network that learns and forecasts future Quality of Service (QoS) based on historical traffic patterns, and (ii) PDM, a Pairwise Decision Model that evaluates discrepancies between predicted and actual QoS to identify potential attacks. Evaluated on the public SDN-SlowRate-DDoS and CIC-IDS2017 datasets, IDQS respectively achieves over 79\% and 91\% detection accuracy across most attack scenarios with high recall and low false negatives, while maintaining an end-to-end inference time of just 0.28 seconds. The results demonstrate the effectiveness and efficiency of IDQS for real-time deployment in resource-constrained IoT environments.</p>},
  year = {2026},
  journal = {Knowledge-Based Systems},
  chapter = {115995},
  url = {https://www.sciencedirect.com/science/article/pii/S0950705126007215},
  doi = {10.1016/j.knosys.2026.115995},
}