We examine the Weyl anomaly for a four-dimensional z = 3 Lifshitz scalar coupled to Hořava’s theory of anisotropic gravity. We find a one-loop break-down of scale-invariance at second order in the gravitational background.

in this paper, an anomaly detection method in cluster-based mobile ad hoc networks with ad hoc on demand distance vector (aodv) routing protocol is proposed. in the method, the required features for describing the normal behavior of aodv are defined via step by step analysis of aodv and independent of any attack. in order to learn the normal behavior of aodv, a fuzzy averaging method is used fo...

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...

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...

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...

This thesis deals with the problem of anomaly detection for sequence data. Anomaly detection has been a widely researched problem in several application domains such as system health management, intrusion detection, healthcare, bioinformatics, fraud detection, and mechanical fault detection. Traditional anomaly detection techniques analyze each data instance (as a univariate or multivariate rec...

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 defence. IDS identifies patterns of known intrusions (misuse detection) or differentiates anomalous network data from normal data (anomaly detection). In this paper, a novel Intr...