LogGPT: Log Anomaly Detection via GPT
Xiao Han, Shuhan Yuan, Mohamed Trabelsi

TL;DR
LogGPT introduces a GPT-based framework for log anomaly detection, employing language modeling and reinforcement learning to improve detection accuracy over existing methods.
Contribution
The paper presents LogGPT, a novel GPT-based approach with reinforcement learning finetuning specifically designed for log anomaly detection.
Findings
LogGPT outperforms state-of-the-art methods on three datasets.
Reinforcement learning finetuning enhances anomaly detection performance.
GPT-based modeling effectively captures log sequence patterns.
Abstract
Detecting system anomalies based on log data is important for ensuring the security and reliability of computer systems. Recently, deep learning models have been widely used for log anomaly detection. The core idea is to model the log sequences as natural language and adopt deep sequential models, such as LSTM or Transformer, to encode the normal patterns in log sequences via language modeling. However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection. To fill up the gap, we propose LogGPT, a novel framework that employs GPT for log anomaly detection. LogGPT is first trained to predict the next log entry based on the preceding sequence. To further enhance the performance of LogGPT, we propose a novel reinforcement learning strategy to finetune the model…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware System Performance and Reliability · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
MethodsMulti-Head Attention · Attention Is All You Need · Cosine Annealing · Linear Warmup With Cosine Annealing · Linear Layer · Attention Dropout · Dropout · Refunds@Expedia|||How do I get a full refund from Expedia? · Weight Decay · Adam
