Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood and adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. (C) 2004 Published by Elsevier Inc.

An equivalent waveguide approach to designing of reflect arrays with the use of variable-size microstrip patches

An equivalent unit cell waveguide approach (WGA) is described to study the behavior of a multilayer reflect array of variable-size patches/dipoles, The approach considers normal incidence of a plane wave on an infinite periodic array of identical radiating elements and introduces an equivalent unit cell waveguide to obtain the reflection coefficient. A field matching technique and method of moments (MoM) is used to determine fields in different layers of the equivalent waveguide. Good agreements for the phase of the reflection coefficient between the proposed model and those published in selected literatures are obtained. (C) 2002 Wiley Periodicals, Inc.

Designing a 161-element Ku-band microstrip reflectarray of variable size patches using an equivalent unit cell waveguide approach

An equivalent unit cell waveguide approach (WGA) to designing 4 multilayer microstrip reflectarray of variable size patches is presented. In this approach, a normal incidence of a plane wave on an infinite periodic array of radiating elements is considered to obtain reflection coefficient phase curves for the reflectarray's elements. It is shown that this problem is equivalent to the problem of reflection of the dominant TEM mode in a waveguide with patches interleaved by layers of dielectric. This waveguide problem is solved using a field matching technique and a method of moments (MoM). Based on this solution, a fast computer algorithm is developed to generate reflection coefficient phase curves for a multilayer microstrip patch reflectarray. The validity of the developed algorithm is tested against alternative approaches and Agilent High Frequency Structure Simulator (HFSS). Having confirmed the validity of the WGA approach, a small offset feed two-layer microstrip patch array is designed and developed. This reflectarray is tested experimentally and shows good performance.

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Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Burrows-wheeler transform in secondary memory

Master’s Thesis in Computer Engineering

A framework for efficient model transformations

The reported productivity gains while using models and model transformations to develop entire systems, after almost a decade of experience applying model-driven approaches for system development, are already undeniable benefits of this approach. However, the slowness of higher-level, rule based model transformation languages hinders the applicability of this approach to industrial scales. Lower-level, and efficient, languages can be used but productivity and easy maintenance seize to exist. The abstraction penalty problem is not new, it also exists for high-level, object oriented languages but everyone is using them now. Why is not everyone using rule based model transformation languages then? In this thesis, we propose a framework, comprised of a language and its respective environment, designed to tackle the most performance critical operation of high-level model transformation languages: the pattern matching. This framework shows that it is possible to mitigate the performance penalty while still using high-level model transformation languages.

BiYacc: Roll your parser and reflective printer into one

In: A. Cunha, E. Kindler (eds.): Proceedings of the Fourth International Workshop on Bidirectional Transformations (Bx 2015), L’Aquila, Italy, July 24, 2015, published at http://ceur-ws.org

In vivo neuropathology of cortical changes in elderly persons with schizophrenia.

BACKGROUND: Elderly schizophrenia patients frequently develop cognitive impairment of unclear etiology. Magnetic resonance imaging (MRI) studies revealed brain structural abnormalities, but the pattern of cortical gray matter (GM) volume and its relationship with cognitive and behavioral symptoms are unknown. METHODS: Magnetic resonance scans were taken from elderly schizophrenia patients (n = 20, age 67 +/- 6 SD, Mini-Mental State Examination [MMSE] 23 +/- 4), Alzheimer's disease (AD) patients (n = 20, age 73 +/- 9, MMSE 22 +/- 4), and healthy elders (n = 20, age 73 +/- 8, MMSE 29 +/- 1). Patients were assessed with a comprehensive neuropsychological and behavioral battery. Cortical pattern matching and a region-of-interest analysis, based on Brodmann areas (BAs), were used to map three-dimensional (3-D) profiles of differences in patterns of gray matter volume among groups. RESULTS: Schizophrenia patients had 10% and 11% lower total left and right GM volume than healthy elders (p < .001) and 7% and 5% more than AD patients (p = .06 and ns). Regions that had both significantly less gray matter than control subjects and gray matter volume as low as AD mapped to the cingulate gyrus and orbitofrontal cortex (BA 30, 23, 24, 32, 25, 11). The strongest correlate of gray matter volume in elderly schizophrenia patients, although nonsignificant, was the positive symptom subscale of the Positive and Negative Syndrome Scale, mapping to the right anterior cingulate area (r = .42, p = .06). CONCLUSIONS: The orbitofrontal/cingulate region had low gray matter volume in elderly schizophrenia patients. Neither cognitive impairment nor psychiatric symptoms were significantly associated with structural differences, even if positive symptoms tended to be associated with increased gray matter volume in this area.

Mechanisms of Symptomatic Spinal Cord Ischemia After TEVAR: Insights From the European Registry of Endovascular Aortic Repair Complications (EuREC).

Abstract Purpose: To test the hypothesis that simultaneous closure of at least 2 independent vascular territories supplying the spinal cord and/or prolonged hypotension may be associated with symptomatic spinal cord ischemia (SCI) after thoracic endovascular aortic repair (TEVAR). Methods: A pattern matching algorithm was used to develop a risk model for symptomatic SCI using a prospective 63-patient single-center cohort to test the positive predictive value (PPV) of prolonged intraoperative hypotension and/or simultaneous closure of at least 2 of 4 the vascular territories supplying the spinal cord (left subclavian, intercostal, lumbar, and hypogastric arteries). This risk model was then applied to data extracted from the multicenter European Registry on Endovascular Aortic Repair Complications (EuREC). Between 2002 and 2010, the 19 centers participating in EuREC reported 38 (1.7%) cases of symptomatic spinal cord ischemia among the 2235 patients in the database. Results: In the single-center cohort, direct correlations were seen between the occurrence of symptomatic SCI and both prolonged intraoperative hypotension (PPV 1.00, 95% CI 0.22 to 1.00, p = 0.04) and simultaneous closure of at least 2 independent spinal cord vascular territories (PPV 0.67, 95% CI 0.24 to 0.91, p = 0.005). Previous closure of a single vascular territory was not associated with an increased risk of symptomatic spinal cord ischemia (PPV 0.07, 95% CI 0.01 to 0.16, p = 0.56). The combination of prolonged hypotension and simultaneous closure of at least 2 territories exhibited the strongest association (PPV 0.75, 95% CI 0.38 to 0.75, p<0.0001). Applying the model to the entire EuREC cohort found an almost perfect agreement between the predicted and observed risk factors (kappa 0.77, 95% CI 0.65 to 0.90). Conclusion: Extensive coverage of intercostal arteries alone by a thoracic stent-graft is not associated with symptomatic SCI; however, simultaneous closure of at least 2 vascular territories supplying the spinal cord is highly relevant, especially in combination with prolonged intraoperative hypotension. As such, these results further emphasize the need to preserve the left subclavian artery during TEVAR.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Pankkitoiminnan riskienhallinta ja riskienhallintaraportointi Puolassa

Tutkimuksessa tarkastellaan julkisesti noteerattujen pankkien riskienhallintaraportoinnin nykykäytäntöä Puolassa. Tutkimus jakaantuu kahteen osaan: Tutkimuksen ensimmäisessä osassa esitellään pankkitoimintaa, pankkitoiminnan riskejä ja riskienhallintaa. Pankkitoiminnan riskejä ovat luotto- ja markkinariskit, lisäksi puhutaan operatiivisista ja ympäristöriskeistä. Tutkimuksen toisessa osassa selvitetään ja kuvataan sitä, millaista on tutkimuksen kohdeyritysten riskienhallinta ja riskienhallintaraportointi, tutkimuksen tarkoituksena on myös verrata pankkien riskienhallintaraportointia keskenään. Tutkimuksen kohteena on 13 Varsovan pörssissä listattua pankkia. Tutkimusaineistona käytetään näiden pankkien vuoden 2005 vuosikertomuksia. Kysymyksessä on laadullinen tapaustutkimus, jolle on tyypillistä kuvaileva ja selittävä tutkimusote. Aineiston analyysimenetelmänä on käytetty pattern matchingiä, jonka avulla tutkitaan aineistosta löytyviä riskienhallintaraportoinnin osatekijöitä/indikaattoreita ja verrataan niitä oletettuihin malleihin. Tutkittujen riskienhallintaraporttien perusteella voidaan todeta, että pankkitoiminnan ydinriskeistä: luotto-, korko-, valuutta- ja likviditeettiriskeistä raportoidaan hyvin. Sen sijaan puutteita löytyy operatiivisten ja ympäristöriskien raportoinnista. Suurin osa pankeista raportoi operatiivisista riskeistä, mutta raportointi on pintapuolista ja analysointi puuttuu. Ympäristöriskeistä raportointi ei ole yleistä. Raportoinnin laajuus ja informatiivisuus vaihtelevat pankkien kesken: Suuret, kansainväliset pankkikonsernit raportoivat riskeistään laajasti ja informatiivisesti, kun taas pienemmillä, kansallisilla pankeilla raportointi jää usean pankin kohdalla suppeaksi. Syitä raportoinnin eroille on monia: Yksi syistä on IFRS-standardien vakiintumaton käyttö pienimmillä, kansallisilla pankeilla verrattuna kansainvälisiin pankkikonserneihin. Kansainvälisillä pankkikonserneilla on paremmat valmiudet raportoida riskienhallinnastaan verrattuna pienimpiin pankkeihin, jotka julkaisivat tilinpäätöksensä ensimmäistä kertaa IFRS-standardien mukaisesti vuonna 2005. Yhtenä selittävänä tekijänäraportoinnin eroille voidaan myös mainita omistuspohja: sijoittajainformaation merkitys on korostunut erityisesti organisaatioissa, joissa on laaja, kansainvälinen omistuspohja. Sen sijaan valtio-omisteisessa yrityksessä sijoittajainformaation merkitys on vähäisempi. Myös yrityskulttuuri vaikuttaa siihen, missä laajuudessa, ja mitä tietoa yritys antaa julkisuuteen. Pankit ovat myös tarkkoja maineestaan, mitä tietoa voidaan julkaista ja mitä vaikutuksia tiedon julkaisemisellaon yrityskuvaan. Sen sijaan pankin koolla ei välttämättä ole vaikutusta riskienhallintaraportoinnin laajuuteen ja informatiivisuuteen.

Design and implementation of machine vision system for the mobile assembly robot

The review of intelligent machines shows that the demand for new ways of helping people in perception of the real world is becoming higher and higher every year. This thesis provides information about design and implementation of machine vision for mobile assembly robot. The work has been done as a part of LUT project in Laboratory of Intelligent Machines. The aim of this work is to create a working vision system. The qualitative and quantitative research were done to complete this task. In the first part, the author presents the theoretical background of such things as digital camera work principles, wireless transmission basics, creation of live stream, methods used for pattern recognition. Formulas, dependencies and previous research related to the topic are shown. In the second part, the equipment used for the project is described. There is information about the brands, models, capabilities and also requirements needed for implementation. Although, the author gives a description of LabVIEW software, its add-ons and OpenCV which are used in the project. Furthermore, one can find results in further section of considered thesis. They mainly represented by screenshots from cameras, working station and photos of the system. The key result of this thesis is vision system created for the needs of mobile assembly robot. Therefore, it is possible to see graphically what was done on examples. Future research in this field includes optimization of the pattern recognition algorithm. This will give less response time for recognizing objects. Presented by author system can be used also for further activities which include artificial intelligence usage.