Online Anomaly Detection With Nested Trees
نویسندگان
چکیده
منابع مشابه
Human Activity Clustering for Online Anomaly Detection
This paper aims to address the problem of profiling human activities captured in surveillance videos for the applications of online normal human activity recognition and anomaly detection. A novel framework is developed for automatic human activity modeling and online anomaly detection without any manual labeling of the training dataset. The framework consists of the following key components: 1...
متن کاملOnline Anomaly Detection Based on Monitoring Traces
In modern days, customers expect that Web services work reliably and are available around the clock. A system failure can have a significant negative impact on a company’s reputation and economical success. This makes it necessary to continuously monitor software systems in order to detect problems of arising failures early. Existing anomaly detection approaches are taking up this challenge by ...
متن کاملSecurity analysis of online centroid anomaly detection
Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). It is to be expected in such cases that learning algorithms will have to deal with manipulated data aimed at hampering decision making. Although some previous work address...
متن کاملAnomaly detection in online social networks
Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, a...
متن کاملOnline anomaly detection in unmanned vehicles
Autonomy requires robustness. The use of unmanned (autonomous) vehicles is appealing for tasks which are dangerous or dull. However, increased reliance on autonomous robots increases reliance on their robustness. Even with validated software, physical faults can cause the controlling software to perceive the environment incorrectly, and thus to make decisions that lead to task failure. We prese...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2016
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2016.2623773