The aim of this paper is to present ongoing work towards a Monitoring-as-a-Service anomaly detection framework in a hybrid or public cloud. The goal of our framework is twofold. First it closes the gap between incidents at different layers of cloud-sourced workflows, namely we focus both on...
The usage of openstack cloud environment is also increasing both in academics and industry as it provides open source cloud services to run the application both for research and for production environment. One of the challenges in cloud environment is that the detection and prediction of the ...
Anomaly detection on large-scale, complex and dynamic data is an essential service that is vital to enable smart functionality in most systems. Increased reliance on cloud computing infrastructures to process such data pose critical challenges with regard to security and privacy. This paper introduces...
Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this wo...
In this section, we introduce an innovative cloud-based model for performing scalable anomaly detection in a privacy preserving manner. A distributed data clustering approach for anomaly detection is adopted for use on encrypted data by leveraging cloud computing resources. The framework can perform ent...
Thapa P, Arjunan T (2024) AI-enhanced cybersecurity: machine learning for anomaly detection in cloud computing. Q J Emerg Technol Innov 9(1):25–37 Google Scholar Yi J, Tian Y (2024) Insider threat detection model enhancement using hybrid algorithms between unsupervised and supervised learning...
Anomaly Detection指在不属于该分类的数据集中,而Novelty是检测可能属于该分类但却没见过(Unseen)也就是Novel的数据集,而OOD(out-of-distribution)则是多分类中不同目标的分布,这些任务在接下来的论文中,也经常有人进行相应的研究。 03 异常检测相关工作与方向...
Anomalies are the unusual patterns that do not conform to the usual patterns of data [1,2,3,4]. Anomaly detection is the detection of deviation or uncertainty in data. Doing so in the early stages can save the time and resources spent during the processing and decision taken after processin...
is still an open issue. In this paper, we present a comprehensive formalization of cardinality-based feature models with potentially unbounded feature multiplicities. We apply a combination of ILP and SMT solvers to automate consistency checking and anomaly detection, including novel anomalies, e.g.,...
A platform that can do this must be able to track cloud expenditure at a granular level and identify anomalies in real time. Continuous cost monitoring and anomaly detection is especially important in the dynamic environment of cloud computing....