Effective anomaly detection in sensor networks data streams

This paper addresses a major challenge in data mining applications where the full information about the underlying processes, such as sensor networks or large online database, cannot be practically obtained due to physical limitations such as low bandwidth or memory, storage, or computing power. Motivated by the recent theory on direct information sampling called compressed sensing (CS), we propose a framework for detecting anomalies from these largescale data mining applications where the full information is not practically possible to obtain. Exploiting the fact that the intrinsic dimension of the data in these applications are typically small relative to the raw dimension and the fact that compressed sensing is capable of capturing most information with few measurements, our work show that spectral methods that used for volume anomaly detection can be directly applied to the CS data with guarantee on performance. Our theoretical contributions are supported by extensive experimental results on large datasets which show satisfactory performance.

Enhancing audio-haptic enabled novel dental training platform performance

Existing haptic and non-haptic dental simulators do not eliminate the problem of hand instability while using the haptic devices for training purpose. This paper reports an audio-haptic dental training platform, which uses a Hand Stability System to reduce the effect of nervousness and hand instability for trainee dental students. Maintaining the ease of implementation, application customizability and the cost factor, the proposed platform increases the training efficiency by enhancing the immersive haptic experience with hand stability. This haptic platform includes multiple angle viewing techniques, audio feedback and session recording for after action review. Trials using this preliminary platform reduced the effect of human nervousness and hand instability due to the customized design.

Body mass index and dental caries in children and adolescents : a systematic review of literature published 2004 to 2011

The objective
The authors undertook an updated systematic review of the relationship between body mass index and dental caries in children and adolescents.
Method
The authors searched Medline, ISI, Cochrane, Scopus, Global Health and CINAHL databases and conducted lateral searches from reference lists for papers published from 2004 to 2011, inclusive. All empirical papers that tested associations between body mass index and dental caries in child and adolescent populations (aged 0 to 18 years) were included.
Results
Dental caries is associated with both high and low body mass index.
Conclusion
A non-linear association between body mass index and dental caries may account for inconsistent findings in previous research. We recommend future research investigate the nature of the association between body mass index and dental caries in samples that include a full range of body mass index scores, and explore how factors such as socioeconomic status mediate the association between body mass index and dental caries.

Private Health Insurance Status and Utilisation of Dental Services in Australia

This article focuses on the relationship between private insurance status and dental service utilisation in Australia using data between 1995 and 2001. This article employs joint maximum likelihood to estimate models of time since last dental visit treating private ancillary health insurance (PAHI) as endogenous. The sensitivity of results to the choice between two different but related types of instrumental variables is examined. We find robust evidence in both 1995 and 2001 that individuals with a PAHI policy make significantly more frequent dental consultations relative to those without such coverage. A comparison of the 1995 and 2001 results, however, suggests that there has been an increasing role of PAHI in terms of the frequency of dental consultations over time. This seems intuitive given the trends in the price of unsubsidised private dental consultations. In terms of policy, our results suggest that while government measures to increase private health insurance coverage in Australia have been successful to a significant degree, that success may have come at some cost in terms of socio-economic inequality as the privately insured are provided much better access to care and financial protection.

A hierarchical PCA-based anomaly detection model

A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.

Time correlated anomaly detection based on inferences

Anomaly detection techniques are used to find the presence of anomalous activities in a network by comparing traffic data activities against a "normal" baseline. Although it has several advantages which include detection of "zero-day" attacks, the question surrounding absolute definition of systems deviations from its "normal" behaviour is important to reduce the number of false positives in the system. This study proposes a novel multi-agent network-based framework known as Statistical model for Correlation and Detection (SCoDe), an anomaly detection framework that looks for timecorrelated anomalies by leveraging statistical properties of a large network, monitoring the rate of events occurrence based on their intensity. SCoDe is an instantaneous learning-based anomaly detector, practically shifting away from the conventional technique of having a training phase prior to detection. It does acquire its training using the improved extension of Exponential Weighted Moving Average (EWMA) which is proposed in this study. SCoDe does not require any previous knowledge of the network traffic, or network administrators chosen reference window as normal but effectively builds upon the statistical properties from different attributes of the network traffic, to correlate undesirable deviations in order to identify abnormal patterns. The approach is generic as it can be easily modified to fit particular types of problems, with a predefined attribute, and it is highly robust because of the proposed statistical approach. The proposed framework was targeted to detect attacks that increase the number of activities on the network server, examples which include Distributed Denial of Service (DDoS) and, flood and flash-crowd events. This paper provides a mathematical foundation for SCoDe, describing the specific implementation and testing of the approach based on a network log file generated from the cyber range simulation experiment of the industrial partner of this project.