LogNEO: GPT-Neo Framework Achieves High Performance in Real-Time Log Anomaly Detection
Researchers have developed LogNEO, a log anomaly detection system based on GPT-Neo that uses reinforcement learning with a novel reward scheme to identify system failures. The framework achieved F1-scores of 0.927-0.984 on standard benchmarks, improving recall over prior methods while maintaining comparable precision. The system demonstrates practical viability with 45 ms latency at 15,000 events per second in production deployment.
LogNEO is a log anomaly detector built on EleutherAI's GPT-Neo (1.3B parameters) and fine-tuned using Proximal Policy Optimisation (PPO) with a position-aware reward scheme that explicitly models prediction difficulty. The reward structure assigns higher rewards for correct early predictions and stronger penalties for later errors, combined with cross-entropy regularisation. The system achieved F1-scores of 0.927 on HDFS, 0.913 on BGL, and 0.984 on Thunderbird benchmarks, improving recall by up to 6 percentage points over the prior state-of-the-art LogGPT method while maintaining comparable precision. A production microservice deployment demonstrates practical feasibility with 45 ms end-to-end latency at 15,000 events per second using Apache Kafka, Redis, and TensorRT-accelerated inference.
What's missing
The paper does not discuss computational costs (training time, GPU requirements, energy consumption) or provide detailed ablation studies isolating the contribution of the position-aware reward scheme versus other design choices. Generalization to log types outside the three benchmarks tested is not addressed.
What different sources said
- arXiv cs.AICenter
LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
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