Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification
Dealing with imbalanced data is one of the main challenges in machine/deep learning algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. In this paper, a model is proposed to generate text log messages using a SeqGAN network. Then features are extracted using an Autoencoder and anomaly detection is done using a GRU network. The proposed model is evaluated with two imbalanced log data sets, namely BGL and Openstack. Results are presented which show that oversampling and balancing data increases the accuracy of anomaly detection and classification.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionClassificationGeneral ClassificationGenerative Adversarial NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily …
Anomaly DetectionData AugmentationGenerative Adversarial NetworkUnsupervised Anomaly DetectionHybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis
Cyberattacks from within an organization's trusted entities are known as insider threats. Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confide…
Adversarial RobustnessAnomaly DetectionGenerative Adversarial NetworkSynthetic Data GenerationLogLLaMA: Transformer-based log anomaly detection with LLaMA
Log anomaly detection refers to the task that distinguishes the anomalous log messages from normal log messages. Transformer-based large language models (LLMs) are becoming popular for log anomaly detection because of th…
Anomaly DetectionReinforcement Learning (RL)Heartbeat Anomaly Detection using Adversarial Oversampling
Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to reduce this fact. Different machine lear…
Anomaly DetectionDiagnosticGeneral ClassificationSOS: Score-based Oversampling for Tabular Data
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs…
Style Transfer