The exponential growth in wireless network faults, vulnerabilities, and attacks make the WLAN security management a challenging research area [29]. Data mining applied to intrusion detection is an active area of research. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require proces...

It is generally agreed that two key points always attract special concerns during the modelling of anomaly-based intrusion detection. One is the techniques about discerning two classes with different features, another is the construction/selection of the observed sample of normally occurring patterns for system normality characterization. In this paper, instead of focusing on the design of spec...

This chapter presents an extension of conformal prediction for anomaly detection applications. It includes the presentation and discussion of the Conformal Anomaly Detector (CAD) and the computationally more efficient Inductive Conformal Anomaly Detector (ICAD), which are general algorithms for unsupervised or semi-supervised and offline or online anomaly detection. One of the key properties of...

Anomaly detection is an important problem that has been popularly researched within diverse research areas and application domains. One of the open problems in anomaly detection is the modeling and prediction of complex sequential data, which consist of a series of temporally related behavior patterns. In this paper, a novel sequential anomaly detection method based on temporal-difference (TD) ...

Plagiarism detection has been considered as a classification problem which can be approximated with intrinsic strategies, considering self-based information from a given document, and external strategies, considering comparison techniques between a suspicious document and different sources. In this work, both intrinsic and external approaches for plagiarism detection are presented. First, the m...

Algorithms for anomaly detection in IP networks have been developed and a real-time distributed platform for anomaly detection has been implemented. These algorithms automatically and adaptively detect “soft” network faults (performance degradations) in IP networks. These algorithms are implemented as a reliable and fully distributed real-time software platform called NSAD (Network/Service Anom...

Anomaly detection in networks is detection of deviations from what is considered to be normal. Performing anomaly detection is a learning approach to detect failures and intrusions in a network, intended to capture novel attacks. Anomaly Detection With fast Incremental Clustering (ADWICE) is an efficient algorithm to detect anomaly. But, since it uses distance based clustering mechanism it suff...

Techniques for video object motion analysis, behaviour recognition and event detection are becoming increasingly important with the rapid increase in demand for and deployment of video surveillance systems. Motion trajectories provide rich spatiotemporal information about an object’s activity. This paper presents a novel technique for classification of motion activity and anomaly detection usin...

We show how the coupling of gravitinos and gauginos to fluxes modifies anomaly cancellation in M-theory on a manifold with boundary. Anomaly cancellation continues to hold, after a shift of the definition of the gauge currents by a local gauge invariant expression in the curvatures and E 8 fieldstrengths. We compute the first nontrivial correction of