One-class support vector classifiers: A survey
S Alam, SK Sonbhadra, S Agarwal… - Knowledge-Based …, 2020 - Elsevier
Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …
diversified applicability in data mining and pattern recognition problems. Concerning to …
Improving security using SVM-based anomaly detection: issues and challenges
Security is one of the main requirements of the current computer systems, and recently it
gains much importance as the number and severity of malicious attacks increase …
gains much importance as the number and severity of malicious attacks increase …
Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics
In the near future, objects have to connect with each other which can result in gathering
private sensitive data and cause various security threats and cyber crimes. To prevent cyber …
private sensitive data and cause various security threats and cyber crimes. To prevent cyber …
Hybrid approach to document anomaly detection: an application to facilitate RPA in title insurance
Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is
no exception. Robotic process automation (RPA) is taking over manual tasks in TI business …
no exception. Robotic process automation (RPA) is taking over manual tasks in TI business …
Reconstruction-based anomaly detection for multivariate time series using contrastive generative adversarial networks
The majority of existing anomaly detection methods for multivariate time series are based on
Transformers and Autoencoders owing to their superior capabilities. However, these …
Transformers and Autoencoders owing to their superior capabilities. However, these …
Ai for devsecops: A landscape and future opportunities
DevOps has emerged as one of the most rapidly evolving software development paradigms.
With the growing concerns surrounding security in software systems, the DevSecOps …
With the growing concerns surrounding security in software systems, the DevSecOps …
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework
In recent years, a variety of research areas have contributed to a set of related problems with
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …
A space-embedding strategy for anomaly detection in multivariate time series
Anomaly detection of time series has always been a hot topic in academia and industry.
However, many existing multivariant time series methods suffer from common challenges …
However, many existing multivariant time series methods suffer from common challenges …
A systematic literature review on automated log abstraction techniques
Context: Logs are often the first and only information available to software engineers to
understand and debug their systems. Automated log-analysis techniques help software …
understand and debug their systems. Automated log-analysis techniques help software …
Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems
Anomaly detection defines as a problem of finding those data samples, which do not follow
the patterns of the majority of data points. Among the variety of methods and algorithms …
the patterns of the majority of data points. Among the variety of methods and algorithms …