LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
arXiv:2606.08153v1 Announce Type: new Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure. We present LogNEO, a log anomaly detector built on EleutherAI's GPT-Neo (1.3B parameters) and fine-tuned with a novel partial-credit, exponentially decaying position-aware reward scheme combined with cross-entropy regularisation via Proximal Policy Optimisation (PPO). The position-aware reward explicitly models prediction difficulty: early positions receive higher rewards for correct predictions, while later positions in
The increasing complexity and scale of modern computing infrastructure necessitate advanced real-time anomaly detection solutions to maintain reliability and security.
This development indicates a growing trend towards applying sophisticated large language models and reinforcement learning techniques to critical system monitoring and cybersecurity.
The use of GPT-Neo with a novel reward scheme offers a more accurate and efficient approach to identifying log anomalies, potentially enhancing system resilience and threat detection.
- · Cybersecurity sector
- · Cloud infrastructure providers
- · AI developers specializing in RL
- · Large-scale system operators
- · Traditional rule-based anomaly detection systems
- · Security teams reliant on manual log analysis
Improved detection of system failures and security breaches in critical infrastructure.
Reduced operational downtime and financial losses due to more proactive problem identification.
Accelerated adoption of advanced AI/ML in IT operations and security, potentially leading to more autonomous systems management.
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Read at arXiv cs.LG