Determination of Oxalate in Grass by FIA with a Spectrophotometric Detection System Using a Two Enzyme Reactor

A new methodology for soluble oxalic acid determination in grass samples was developed using a two enzyme reactor in an FIA system. The reactor consisted of 3 U of oxalate oxidase and 100 U of peroxidase immobilized on Sorghum vulgare seeds activated with glutaraldehyde. The carbon dioxide was monitored spectrophotometrically, after reacting with an acid-base indicator (Bromocresol Purple) after it permeated through a PTFE membrane. A linear response range was observed between 0.25 and 1.00mmol l-1 of oxalic acid; the data was fit by the equation A=-0.8(±1.5)+ 57.2(±2.5)[oxalate], with a correlation coefficient of 0.9971 and a relative standard deviation of 2% for n=5. The variance for a 0.25 mmol l-1 oxalic acid standard solution was lower than 4% for 11 measurements. The FIA system allows analysis of 20 samples per hour without prior treatment. The proposed method showed a good correlation with that of the Sigma Kit.

Statistical model applied to NetFlow for network intrusion detection

The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application. © 2010 Springer-Verlag Berlin Heidelberg.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

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

Laboratorial training of examiners for using a visual caries detection system in epidemiological surveys

Background: In epidemiological surveys, a good reliability among the examiners regarding the caries detection method is essential. However, training and calibrating those examiners is an arduous task because it involves several patients who are examined many times. To facilitate this step, we aimed to propose a laboratory methodology to simulate the examinations performed to detect caries lesions using the International Caries Detection and Assessment System (ICDAS) in epidemiological surveys. Methods: A benchmark examiner conducted all training sessions. A total of 67 exfoliated primary teeth, varying from sound to extensive cavitated, were set in seven arch models to simulate complete mouths in primary dentition. Sixteen examiners (graduate students) evaluated all surfaces of the teeth under illumination using buccal mirrors and ball-ended probe in two occasions, using only coronal primary caries scores of the ICDAS. As reference standard, two different examiners assessed the proximal surfaces by direct visual inspection, classifying them in sound, with non-cavitated or with cavitated lesions. After, teeth were sectioned in the bucco-lingual direction, and the examiners assessed the sections in stereomicroscope, classifying the occlusal and smooth surfaces according to lesion depth. Inter-examiner reproducibility was evaluated using weighted kappa. Sensitivities and specificities were calculated at two thresholds: all lesions and advanced lesions (cavitated lesions in proximal surfaces and lesions reaching the dentine in occlusal and smooth surfaces). Conclusion: The methodology purposed for training and calibration of several examiners designated for epidemiological surveys of dental caries in preschool children using the ICDAS is feasible, permitting the assessment of reliability and accuracy of the examiners previously to the survey´s development.

Progettazione e implementazione di un sistema dinamico per la gestione del packet filtering

Un livello di sicurezza che prevede l’autenticazione e autorizzazione di un utente e che permette di tenere traccia di tutte le operazioni effettuate, non esclude una rete dall’essere soggetta a incidenti informatici, che possono derivare da tentativi di accesso agli host tramite innalzamento illecito di privilegi o dai classici programmi malevoli come virus, trojan e worm. Un rimedio per identificare eventuali minacce prevede l’utilizzo di un dispositivo IDS (Intrusion Detection System) con il compito di analizzare il traffico e confrontarlo con una serie d’impronte che fanno riferimento a scenari d’intrusioni conosciute. Anche con elevate capacità di elaborazione dell’hardware, le risorse potrebbero non essere sufficienti a garantire un corretto funzionamento del servizio sull’intero traffico che attraversa una rete. L'obiettivo di questa tesi consiste nella creazione di un’applicazione con lo scopo di eseguire un’analisi preventiva, in modo da alleggerire la mole di dati da sottoporre all’IDS nella fase di scansione vera e propria del traffico. Per fare questo vengono sfruttate le statistiche calcolate su dei dati forniti direttamente dagli apparati di rete, cercando di identificare del traffico che utilizza dei protocolli noti e quindi giudicabile non pericoloso con una buona probabilità.

Detection system for Escherichia coli-specific virulence genes: absence of virulence determinants in B and C strains

We describe a rational approach to simultaneously test Escherichia coli strains for the presence of known virulence genes in a reverse dot blot procedure. Specific segments of virulence genes of E. coli designed to have similar hybridization parameters were subcloned on plasmids and subsequently amplified by PCR as unlabeled probes in amounts sufficient to be bound to nylon membranes. Various pathogenic isolates and laboratory strains of E. coli were probed for the presence of virulence genes by labeling the genomic DNA of these strains with digoxigenin and then hybridizing them to the prepared nylon membranes. These hybridization results demonstrated that besides the E. coli K-12 safety strain derivatives, E. coli B and C strains are also devoid of genes encoding any of the investigated virulence factors. In contrast, pathogenic E. coli control strains, used to evaluate the method, showed typical hybridization patterns. The described probes and their easy application on a single filter were shown to provide a useful tool for the safety assessment of E. coli strains to be used as hosts in biotechnological processes. This approach might also be used for the identification and characterization of clinically significant E. coli isolates from human and animal species.

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.