A Predictive Neural Network Architecture for Early Detection of Low-Rate Cyberattacks

2026-06-17

AI Summary

This study addresses the critical challenge posed by low-rate denial-of-service (LDoS) attacks to Internet of Things (IoT) security, owing to their stealthy nature and prolonged duration, which enable them to evade conventional detection mechanisms. To counter this threat, the authors propose the IDQS framework, which innovatively integrates a recursive trend prediction neural network based on RTP-QoS with a pairwise decision model (PDM). By forecasting time-series quality-of-service (QoS) variations and quantifying deviations between actual and predicted values, IDQS enables lightweight, highly sensitive early detection of LDoS attacks. Experimental evaluations on the SDN-SlowRate-DDoS and CIC-IDS2017 datasets demonstrate detection accuracies exceeding 79% and 91%, respectively, across most scenarios, along with high recall, low false-positive rates, and an end-to-end inference latency of only 0.28 seconds—making it well-suited for resource-constrained IoT environments.

Abstract

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.

Problem

• Low-Rate Denial of Service
• Intrusion Detection
• IoT Networks
• Quality of Service
• Cyberattacks

Innovation

• Predictive Neural Network
• Low-Rate DoS Detection
• QoS Prediction
• Pairwise Decision Model
• Lightweight Intrusion Detection