Spam filtering for network traffic security on a multi-core environment

This paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.

Distinguishing DDoS attacks from flash crowds using probability metrics

Both Flash crowds and DDoS (Distributed Denial-of-Service) attacks have very similar properties in terms of internet traffic, however Flash crowds are legitimate flows and DDoS attacks are illegitimate flows, and DDoS attacks have been a serious threat to internet security and stability. In this paper we propose a set of novel methods using probability metrics to distinguish DDoS attacks from Flash crowds effectively, and our simulations show that the proposed methods work well. In particular, these mathods can not only distinguish DDoS attacks from Flash crowds clearly, but also can distinguish the anomaly flow being DDoS attacks flow or being Flash crowd flow from Normal network flow effectively. Furthermore, we show our proposed hybrid probability metrics can greatly reduce both false positive and false negative rates in detection.

Identifying co-regulating microrna groups

Background: Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes.
Results: Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 × 10^-2). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 × 10^-2) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA. Conclusions: This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Detection of cross-channel anomalies from multiple data channels

We identify and formulate a novel problem: crosschannel anomaly detection from multiple data channels. Cross channel anomalies are common amongst the individual channel anomalies, and are often portent of significant events. Using spectral approaches, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single channel anomalies. Our mathematical analysis shows that our method is likely to reduce the false alarm rate. We demonstrate our method in two applications: document understanding with multiple text corpora, and detection of repeated anomalies in video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large scale data stream analysis.

Renal access coordinators' impact on hemodialysis patient outcomes and associated service delivery: a systematic review

Background
Renal access coordinators contribute specifically to dialysis access care for people with chronic and end stage renal disease. Since the introduction of renal access coordinators into Australia in the early 2000s, there have been anecdotal examples of associated improvements in patient outcomes and service delivery; however scant published quantitative evidence exists. Thus, the impact of the implementation of renal access coordinators has not undergone a rigorous review to date.

Objective
The objective of this systematic review was to critically appraise and synthesize the best available evidence related to the impact of renal access coordinators on dialysis patient outcomes and associated service delivery.

INCLUSION CRITERIA

Types of participants
This review considered studies that included renal access coordinators (noting variations of the titles) and adult hemodialysis patients (aged 18 years and over).

Types of intervention(s)
This review considered studies that evaluated the effectiveness of the renal access coordinator. This role typically consists of clinical and administration duties such as providing pre dialysis access coordination, access surveillance patient education and nurse education.

Types of studies
The types of studies considered within this review included experimental and epidemiological study designs. Thus randomized controlled trials (RCT), non-randomized controlled trials, and quasi-experimental, before and after studies, prospective and retrospective cohort studies were considered as were case control studies, analytical cross sectional studies and descriptive cross sectional studies.

Types of outcomes
Patient outcomes considered included: days to first vascular access complication (such as stenosis or thrombosis) and/or primary intervention (such as angioplasty or surgical intervention); percentage of central line insertions (negative); rate of arteriovenous fistula (AVF)/arteriovenous graft (AVG)/central venous catheter (CVC) at start of dialysis (incidence); prevalent rate of AVF/AVG/CVC; time to occlusion of AVF and time from referral to surgery. Service outcomes included: knowledge/up skilling of renal nurses; cannulation skills, ultrasound skills, knowledge of anatomy and physiology and other access related knowledge.

Search strategy
The search strategy aimed to locate published and unpublished studies, utilizing a three-step searching approach. Studies published in English from 1990 to October 2013 were considered for inclusion in this review.

Methodological quality
The studies were assessed by two independent reviewers using the appropriate standardized critical appraisal instruments from the Joanna Briggs Institute.

Data collection
Data were extracted from papers included in the review using the standardised data extraction tool from the Joanna Briggs Institute, namely JBI Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI).

Data synthesis
This review aimed to conduct meta-analyses of the findings: however, because of the limitations of the data found, this was not possible and so the findings are presented in a narrative format.

Results
Five studies were identified for inclusion in the review. No RCTs were found, therefore four of the five studies were pre-post intervention cohort studies and one was a prospective quality assurance report. Data were heterogeneous and thus did not allow for meta-analysis. All studies included multidisciplinary teams with variable emphasis on the renal access coordinator role. The pre post intervention cohort studies measured incident and/or prevalent AVF, AVG and CVC rates in the hemodialysis population and the quality assurance report measured the difference in patency rates between AVF and AVG. All discussed the role of central coordination as a contributor to the success of vascular access care.

Conclusions
This review found insufficient data to make firm conclusions about the impact that renal access coordinators have on patient outcomes. The results of this review suggest an association between renal access coordinators and improved patient outcomes. These improved patient outcomes were apparent in an increase in incident and prevalent AVFs, and a decrease in the incidence and prevalence of CVCs. Both associations are correlated with a reduction in infection rates, length of hospital stay and healthcare costs.

Detection of respiratory arousals using photoplethysmography (PPG) signal in sleep apnea patients

Respiratory events during sleep induce cortical arousals and manifest changes in autonomic markers in sleep disorder breathing (SDB). Finger photoplethysmography (PPG) has been shown to be a reliable method of determining sympathetic activation. We hypothesize that changes in PPG signals are sufficient to predict the occurrence of respiratory-event-related cortical arousal. In this study, we develop a respiratory arousal detection model in SDB subjects by using PPG features. PPG signals from 10 SDB subjects (9 male, 1 female) with age range 43-75 years were used in this study. Time domain features of PPG signals, such as 1) PWA--pulse wave amplitude, 2) PPI--peak-to-peak interval, and 3) Area--area under peak, were used to detect arousal events. In this study, PWA and Area have shown better performance (higher accuracy and lower false rate) compared to PPI features. After investigating possible groupings of these features, combination of PWA and Area (PWA + Area) was shown to provide better accuracy with a lower false detection rate in arousal detection. PPG-based arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic with an area under the curve of 0.91. For the decision threshold (PC(thresh) = 25%) chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. The results showed an accuracy of 84.68%, 85.15%, 86.93%, and 50.79% with a false rate of 21.80%, 55.41%, 64.78%, and 50.79% at PC(thresh) = 25% or PPI, PWA, Area , and PWA + Area features, respectively. This indicates that combining PWA and Area features reduced the false positive rate without much affecting the sensitivity of the arousal detection system. In conclusion, the PPG-based respiratory arousal detection model is a simple and promising alternative to the conventional electroencephalogram (EEG)-based respiratory arousal detection system.

Using response action with Intelligent Intrusion detection and prevention System against web application malware

Findings: After evaluating the new system, a better result was generated in line with detection efficiency and the false alarm rate. This demonstrates the value of direct response action in an intrusion detection system.

Muscle atrophy, pain, and damage in bed rest reduced by resistive (vibration) exercise

The purpose of this study was to investigate the effectiveness of a short-duration (5-6 min, 3 d·wk) resistive exercise program with (RVE) or without (RE) whole-body vibration in reducing muscle atrophy in the lower limb during prolonged inactivity when compared with that in an inactive control group. METHODS: As part of the second Berlin BedRest Study, 24 male subjects underwent 60 d of head-down tilt bed rest. Using magnetic resonance imaging, muscle volumes of the individual muscles of the lower limb were calculated before and at various intervals during and after bed rest. Pain levels and markers of muscle damage were also evaluated during and after bed rest. Adjustment of P values to guard against false positives was performed via the false discovery rate method. RESULTS: On the "intent-to-treat" analysis, RE reduced atrophy of the medial and lateral gastrocnemius, soleus, vasti, tibialis posterior, flexor hallucis longus, and flexor digitorum longus (P ≤ 0.045 vs control group) and RVE reduced atrophy of the medial and lateral gastrocnemius and tibialis posterior (P ≤ 0.044). Pain intensity reports after bed rest were lower in RE at the foot (P ≤ 0.033) and whole lower limb (P = 0.01) and in RVE at the thigh (P ≤ 0.041), lower leg (P ≤ 0.01), and whole lower limb (P ≤ 0.036). Increases in sarcomere-specific creatine kinase after bed rest were less in RE (P = 0.020) and RVE (P = 0.020). No differences between RE and RVE were observed. CONCLUSIONS: In conclusion, a short-duration RVE or RE can be effective in reducing the effect of prolonged bed rest on lower extremity muscle volume loss during bed rest and muscle damage and pain after bed rest. Copyright © 2014 by the American College of Sports Medicine.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Comparação dos testes de virusneutralização contra os genótipos 1 e 2 do vírus da diarreia viral bovina (BVDV-1 e BVDV-2) em bovinos de rebanhos naturalmente infectados

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Virulence properties and antimicrobial susceptibility of Shiga toxin-producing Escherichia coli strains isolated from healthy cattle from Parana State, Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

Associação glicemia de jejum e fatores de risco como teste para rastreamento do diabete gestacional

OBJETIVOS: comparar dois testes de rastreamento do diabete gestacional. MÉTODOS: estudo prospectivo no qual foram avaliadas 356 gestantes, sem diagnóstico prévio do diabete melito, submetidas, de modo independente, a dois testes de rastreamento: associação glicemia de jejum e fator de risco (GJ+FR) e teste oral simplificado de tolerância à glicose (TTG50g). A comparação entre os métodos foi realizada pelos índices de sensibilidade (S), especificidade (E) e valores preditivos positivo (VPP) e negativo (VPN), resultados falsos, positivos (FP) e negativos (FN) e pela diferença dos resultados observados e esperados, avaliada pelo teste do Qui-quadrado (p<0,05). RESULTADOS: a associação GJ+FR determinou a confirmação diagnóstica em maior número de gestantes (187; 52,5%) que o TTG50g (49; 13,8%). Esta diferença foi significativa (p<0,05). A associação GJ+FR apresentou sensibilidade de 83,7% e valor preditivo negativo (VPN) de 95,3% em relação ao TTG50g. CONCLUSÕES: os índices elevados de sensibilidade e VPN da associação GJ+FR em relação ao TTG50g, sua simplicidade, praticidade, baixo custo e fácil replicação permitem sua indicação no rastreamento do diabete gestacional.