notesum.ai

Published at November 13

LogLLM: Log-based Anomaly Detection Using Large Language Models

cs.SE

cs.AI

Released Date: November 13, 2024

Authors: Wei Guan¹, Jian Cao¹, Shiyou Qian¹, Jianqi Gao¹

Aff.: ¹Department of Computer Science and Engineering, SJTU, Shanghai, China

Arxiv: http://arxiv.org/abs/2411.08561v1

Refer to caption

	Log parser	HDFS			BGL			Liberty			Thunderbird			Avg. F₁
	Log parser	Prec.	Rec.	F₁	Prec.	Rec.	F₁	Prec.	Rec.	F₁	Prec.	Rec.	F₁	Avg. F₁
DeepLog	$\usym{2714}$	0.835	0.994	0.908	0.166	0.988	0.285	0.751	0.855	0.800	0.017	0.963	0.033	0.506
LogAnomaly	$\usym{2714}$	0.886	0.893	0.966	0.176	0.985	0.299	0.684	0.876	0.768	0.025	0.963	0.050	0.521
PLELog	$\usym{2714}$	0.893	0.979	0.934	0.595	0.880	0.710	0.795	0.874	0.832	0.826	0.704	0.760	0.809
FastLogAD	$\usym{2714}$	0.721	0.893	0.798	0.167	1.000	0.287	0.151	0.999	0.263	0.008	0.931	0.017	0.341
LogBERT	$\usym{2714}$	0.989	0.614	0.758	0.165	0.989	0.283	0.909	0.615	0.734	0.143	0.500	0.222	0.499
LogRobust	$\usym{2714}$	0.961	1.000	0.980	0.696	0.968	0.810	0.695	0.979	0.813	0.318	1.000	0.482	0.771
CNN	$\usym{2714}$	0.966	1.000	0.982	0.698	0.965	0.810	0.580	0.914	0.709	0.900	0.670	0.766	0.817
NeuralLog	$\usym{2717}$	0.971	0.988	0.979	0.792	0.884	0.835	0.875	0.926	0.900	0.794	0.931	0.857	0.893
RAPID	$\usym{2717}$	1.000	0.859	0.924	0.874	0.399	0.548	0.911	0.611	0.732	0.200	0.207	0.203	0.602
LogLLM	$\usym{2717}$	0.994	1.000	0.997	0.861	0.979	0.916	0.992	0.926	0.958	0.966	0.966	0.966	0.959