Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure

Methodology and potential of image analysis and unconventional use of GIS tools in determining grain size distribution and fractal dimension : a case study of fault rocks in the Western Tatra Mts. (Western Carpathians, Poland)

A methodology of textural analyses based on image analysis is proposed and tested based on study of fault rock samples from the Tatra Mts., Poland. The procedure encompasses: (1) SEM-BSE imagery of thin sections; (2) image classification using the maximum likelihood method, performed with GIS software; (3) statistical analysis and fractal dimension (self-similarity) analysis. The results of this method are comparable to those obtained with methods involving specialized software. The proposed analytical procedure particularly improves qualitative observations with quantitative data on grain shape and size distribution. The potential of the method is shown, as an auxiliary tool in determining the nature of deformation processes: the role of high-temperature dynamic recrystallization processes is recorded using grain shape indicators, whilst the switch from ductile to brittle conditions is reflected by the grain size distribution pattern

Preliminary results on automatic quantification of histological studies in allergic asthma

Proceedings of: The first international workshop on Microscopic Image Analysis with Applications in Biology, MIAAB 2006, was held in Copenhagen, Denmark, on 5th of October 2006 as an associated workshop of MICCAI 2006, the 9th Conference held by the international society of Medical Image Computing and Computer-Assisted Intervention.The evaluation of new therapies to treat allergic asthma makes frequent use of histological studies. Some of these studies are based on the microscope observation of stained paraffin lung sections to quantify cellular infiltration, an effect directly related to allergic processes. To our knowledge, there is no software tool for doing this quantification automatically nowadays. This paper presents a method for the quantification of cellular infiltrate of lung tissue images in a mouse model of allergic asthma. Each image is divided into regions of equal size that are classified by means of a segmentation algorithm based on texture analysis. The classification uses three discriminant functions, built from parameters derived from the histogram and the co-occurrence matrix and calculated by performing an initial stepwise discriminant analysis on 79 samples from a training set. Results provide a correct classification of 96.8 % on an independent test set of 251 samples labeled manually.Publicad

A Rapid Electrochemical Impedance Spectroscopy and Sensor-Based Method for Monitoring Freeze-Damage in Tangerines

[EN] This study focuses on the analysis and early detection of freeze-damage in tangerines using a specific double-needle sensor and Electrochemical Impedance Spectroscopy (EIS). Freeze damage may appear in citrus fruits both in the field and in postharvest processes resulting in quality loss and a difficult commercialization of the fruit. EIS has been used to test a set of homogeneous tangerine samples both fresh and later frozen to analyze electrochemical and biological differences. A double-needle electrode associated to a specifically designed electronic device and software has been designed and used to send an AC electric sinusoidal signal 1 V in amplitude and frequency range [100Hz to 1MHz] to the analyzed samples and then receive the electrochemical impedance response. EIS measurements lead to distinct values of both impedance module and phase of fresh and frozen samples over a wide frequency range. Statistical treatment of the received data set by Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) shows a clear classification of the samples depending on the experienced freeze phenomenon, with high sensitivity (1.00), specificity (>= 0.95) and confidence level (95%). Later Artificial Neural Networks (ANN) analysis based on 20-3-1 architecture has allowed to create a mathematical prediction model able to correctly classify 100% of the analyzed samples (CCR =100% for training, validation and test phases, and overall classification), being fast, easy, robust and reliable, and an interesting alternative method to the traditional laboratory analyses.This work was supported by the Spanish Government/FEDER funds [Ministerio de Economia y Empresa (MINECO)/Fondo Europeo de Desarrollo Regional (FEDER)] under Grant RTI2018-100910-B-C43 and in part by the Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana under Grant GV/2018/090.Albelda Aparisi, P.; Fortes Sánchez, E.; Contat-Rodrigo, L.; Masot Peris, R.; Laguarda-Miro, N. (2021). A Rapid Electrochemical Impedance Spectroscopy and Sensor-Based Method for Monitoring Freeze-Damage in Tangerines. IEEE Sensors Journal. 21(10):12009-12018. https://doi.org/10.1109/JSEN.2021.3065846S1200912018211

Segmentation and intensity estimation for microarray images with saturated pixels

<p>Abstract</p> <p>Background</p> <p>Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶- 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.</p> <p>Results</p> <p>We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.</p> <p>Conclusions</p> <p>The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.</p

Classifying Indian Classical Dances By Motion Posture Patterns

Dance is a classic form of human motion which is usually performed as a reaction of expression to music. The Indian classical dances, for instance, require multiple complicated movements that relates to body motion postures and hand gestures with high similarities. Past studies showed interests using various methods to classify dances. The most common method used is the Hidden Markov Models (HMM), apart from using the correlation matrix method and hierarchical cluster analysis. Nevertheless, less effort has been placed in analysing the Indian dance by using the data mining approach. Therefore, the objectives in this work are to (i) distinguish different types of Indian classical dances, (ii) classify the type of dance based on motion posture patterns and (iii) determine the effects of attributes on the classification accuracy. This study involves five types of Indian classical dances (Kathak, Bharatanatyam, Kuchipudi, Manipuri and Odissi) motion postures. The data mining approaches were used to classify the motion posture patterns by type of dances. A total of 15 dance videos were collected from the public available domain for body joints tracking processes using the Kinovea software. Data mining analysis was performed in three stages: data pre�processing, data classification and knowledge discovery using the WEKA software. RandomForest algorithm returned the highest classification accuracy (99.2616%). On attribute configuration, y-coordinates of left wrist (LW(y)) was identified as the most significant attribute to differentiate the Indian classical dance classes

Reliability of adaptive multivariate software sensors for sewer water quality monitoring

This study investigates the use of a multivariate approach, based on Principal Component Analysis PCA), as software sensor for fault detection and reconstruction of missing measurements in on-line monitoring of sewer water quality. The analysis was carried out on a 16-months dataset of five commonly available on-line measurements (flow, turbidity, ammonia, conductivity and temperature). The results confirmed the great performance of PCA (up to 10 weeks after parameter estimation) when estimating a measurement from the combination of the remaining four variables, a useful feature in data validation. However, the study also showed a dramatic drop in predictive capability of the software sensor when used for reconstructing missing values, with performance quickly deteriorating after 1 week since parameter estimation. The software sensor provided better results when used to estimate pollutants mainly originated from wastewater sources (such as ammonia) than when used for pollutants affected by several processes (such as TSS). Overall, this study provides a first insight in the application of multivariate methods for software sensors, highlighting drawback and potential development areas. A combination of (i) advanced methods for on-line data validation, (ii) frequent parameter estimation, and (iii) automatic method for classification of dry/wet periods may provide the needed background for a successful application of these software sensors

A computer-based product classification and component detection for demanufacturing processes

This is an Author's Accepted Manuscript of an article published in International Journal of Computer Integrated Manufacturing, 24(10), 900-914, 2011 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/0951192X.2011.579169.The aim of this paper is to propose a novel computer-based product classification, component detection and tracking for demanufacturing and disassembly process. This is achieved by introducing a series of automated and sequential product scanning, component identification, image analysis and sorting – leading to the development of a bill of material (BOM). The produced BOM can then be associated with the relevant disassembly/demanufacture proviso. The proposed integrated image sorting and product classification (ISPC) approach can be considered as a step forward in automation of demanufacturing activities. The ISPC model proposed in this paper utilises and builds on the state-of-the-art technology and current body of research in computer-integrated demanufacturing and remanufacturing (CIDR). An appraisal of the latest research material and the factors that inhibit CIDR methods inpractice are presented. A novel solution for the integration of imaging and material identification techniques toovercome some of the existing shortcomings of automated recycling processes is proposed in this paper. The proposed product scanning and component detection ISPC software consists of four distinct models: the repertory database, the search engine, the product-attributes updater and the image sorting and classification algorithm. The software framework that integrates the four components is presented in this paper. Finally, an overall assessment of applying ISPC at various stages of CIDR processes concludes the article.University of Ibadan MacArthur Foundation Gran

Measuring Software Process: A Systematic Mapping Study

Context: Measurement is essential to reach predictable performance and high capability processes. It provides support for better understanding, evaluation, management, and control of the development process and project, as well as the resulting product. It also enables organizations to improve and predict its process’s performance, which places organizations in better positions to make appropriate decisions. Objective: This study aims to understand the measurement of the software development process, to identify studies, create a classification scheme based on the identified studies, and then to map such studies into the scheme to answer the research questions. Method: Systematic mapping is the selected research methodology for this study. Results: A total of 462 studies are included and classified into four topics with respect to their focus and into three groups based on the publishing date. Five abstractions and 64 attributes were identified, 25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the most measured process attributes, while effort and performance were the most measured project attributes. Goal Question Metric and Capability Maturity Model Integration were the main methods and models used in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently identified research contexts.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2- RMinisterio de Economía y Competitividad TIN2015-71938-RED