.png){kind=icon}
返回
GRAND: GAN-based software runtime anomaly detection method using trace information
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2023.10.036.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Software runtime anomaly detection can detect manifestations (known as anomalies) caused by faults in complex systems before they lead to failure. Whereas most existing methods use external performance indicators, this study uses internal execution traces to reveal failures not only related to software performance issues but also functional errors. A neural network model called GRAND, which combines a variational autoencoder and a generative adversarial network, is proposed to mine anomalies in the execution trace. Cassandra, a widely used database system, was used as a representation to conduct the empirical study. The dataset was collected under a well-designed operational profile that contained 5180 time series, each containing more than ten million data points. GRAND achieved a higher detection performance than the other two SOTA baseline models, with a 99% F1-score compared with 93% and 87%. Ablation studies show that the workload information used in GRAND can determine whether the current internal status is consistent with the task, thus achieving a 16% improvement in the F1-score. The attention mechanism used for data fusion can achieve a 32% improvement in the F1-score.
Keyword:
Software anomaly detection
Internal execution trace
Unsupervised learning
VAE
GAN
期刊
IF:
6.3
论文数:
8.2K
被引数:
3.0W
机构
引用论文
Log message anomaly detection with fuzzy C-means and MLP基于模糊C均值和MLP的日志消息异常检测.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Which algorithm can detect unknown attacks? Comparison of supervise d, unsupervise d and meta-learning algorithms for intrusion detectionCOMPUTERS & SECURITY
OAAI
COMPUTERS & SECURITYIF5.4
OAAI分享收藏分享收藏A comparative evaluation of outlier detection algorithms: Experiments and analysesPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容