In the era of big data and Internet of things, massive sensor data are gathered with Internet of things. Quantity of data captured by sensor networks are considered to contain highly useful and valuable information. However, for a variety of reasons, received sensor data often appear abnormal. Therefore, effective anomaly detection methods are required to guarantee the quality of data collected...

Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses...

Abstract Abnormal climate event is that some meteorological conditions are extreme in a certain time interval. The existing methods for detecting abnormal events utilize supervised learning models to learn the patterns, but they cannot detect untrained patterns. To overcome this problem, we construct dynamic graph by discovering correlation among series and propose novel embedding model based o...

In the real world, a large number of multivariate time series data are generated by Internet Things systems, which composed many connected sensing devices. Therefore, it is impractical to consider only single univariate for decision-making. High-dimensional decrease performance traditional anomaly detection methods. Moreover, previously developed methods capture temporal correlations instead sp...

Program anomaly detection analyzes normal program behaviors and discovers aberrant executions caused by attacks, misconfigurations, program bugs, and unusual usage patterns. The merit of program anomaly detection is its independence from attack signatures, which enables proactive defense against new and unknown attacks. In this paper, we formalize the general program anomaly detection problem a...

Assuring secure and reliable operation of networks has become a priority research area these days because of ever growing dependency on network technology. Intrusion detection systems (IDS) are used as the last line of defense. Intrusion Detection System identifies patterns of known intrusions (misuse detection) or differentiates anomalous network data from normal data (anomaly detection). In t...

System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly. We propose a novel concept for anomaly detection, called relative anomaly detection. It is tailored to be robust towards...

Novel machine learning techniques for anomaly intrusion detection" (2004). ABSTRACT This paper explores the methodology of using kernels and Support Vector Machine (SVM) for intrusion detection. A new insight into two well known anomaly detection algorithms-STIDE and Markov Chain anomaly detectors, is achieved using kernel theory. We introduce two new classes of kernels used for intrusion detec...