Semi-supervised log-based anomaly detection via probabilistic label estimation
With the growth of software systems, logs have become an important data to aid system
maintenance. Log-based anomaly detection is one of the most important methods for such …
maintenance. Log-based anomaly detection is one of the most important methods for such …
Dist-pu: Positive-unlabeled learning from a label distribution perspective
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive
examples with many unlabeled ones. Compared with ordinary semi-supervised learning …
examples with many unlabeled ones. Compared with ordinary semi-supervised learning …
Rosas: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield
drastically improved performance compared to unsupervised models. However, they still …
drastically improved performance compared to unsupervised models. However, they still …
Evaluating the predictive performance of positive-unlabelled classifiers: a brief critical review and practical recommendations for improvement
JD Saunders, AA Freitas - ACM SIGKDD Explorations Newsletter, 2022 - dl.acm.org
Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn
classifiers from data consisting of labelled positive and unlabelled instances. Whilst much …
classifiers from data consisting of labelled positive and unlabelled instances. Whilst much …
Positive-unlabeled learning with label distribution alignment
Positive-Unlabeled (PU) data arise frequently in a wide range of fields such as medical
diagnosis, anomaly analysis and personalized advertising. The absence of any known …
diagnosis, anomaly analysis and personalized advertising. The absence of any known …
Black-box adversarial attacks on XSS attack detection model
Q Wang, H Yang, G Wu, KKR Choo, Z Zhang… - Computers & …, 2022 - Elsevier
Cross-site scripting (XSS) has been extensively studied, although mitigating such attacks in
web applications remains challenging. While there is an increasing number of XSS attack …
web applications remains challenging. While there is an increasing number of XSS attack …
Instance-dependent pu learning by bayesian optimal relabeling
When learning from positive and unlabelled data, it is a strong assumption that the positive
observations are randomly sampled from the distribution of $ X $ conditional on $ Y= 1 …
observations are randomly sampled from the distribution of $ X $ conditional on $ Y= 1 …
Data-driven edge intelligence for robust network anomaly detection
The advancement of networking platforms for assured online services requires robust and
effective network intelligence systems against anomalous events and malicious threats. With …
effective network intelligence systems against anomalous events and malicious threats. With …
PUMAD: PU metric learning for anomaly detection
Anomaly detection task, which identifies abnormal patterns in data, has been widely applied
to various domains. Most recent work on anomaly detection have focused on an accurate …
to various domains. Most recent work on anomaly detection have focused on an accurate …
Learning from positive and unlabeled data with arbitrary positive shift
Z Hammoudeh, D Lowd - Advances in Neural Information …, 2020 - proceedings.neurips.cc
Positive-unlabeled (PU) learning trains a binary classifier using only positive and unlabeled
data. A common simplifying assumption is that the positive data is representative of the …
data. A common simplifying assumption is that the positive data is representative of the …