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An efficient block-discriminant identification of packed malware

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2015年第5期40卷 1435-1456页

作者： Naval, Smita Laxmi, Vijay Gaur, Manoj Singh Vinod, P. Malaviya Natl Inst Technol Dept Comp & Sci Engn Jaipur 302017 Rajasthan India SCMS Sch Engn Ernakulam 683582 Kerala India

Advanced persistent attacks, incorporated by sophisticated malware, are on the rise against hosts, user applications and utility software. Modern malware hide their malicious payload by applying packing mechanism. Packing tools instigate code encryption to protect the original malicious payload. Packing is employed in tandem with code obfuscation/encryption/compression to create malware variants. Despite being just a variant of known malware, the packed malware invalidates the traditional signature based malware detection as packing tools create an envelope of packer code around the original base malware. Therefore, unpacking becomes a mandatory phase prior to anti-virus scanning for identifying the known malware hidden behind packing layers. Existing techniques of unpacking solutions increase execution overhead of AV scanners in terms of time. This paper illustrates an easy to use approach which works in two phases to reduce this overhead. The first phase (ESCAPE) discriminates the packed code from the native code (non-packed) by using random block entropy. The second phase (PEAL) validates inferences of ESCAPE by employing bi-classification (packed vs native) model using relevant hex byte features extracted blockwise. The proposed approach is able to shrink the overall execution time of AV scanners by filtering out native samples and avoiding excessive unpacking overhead. Our method has been evaluated against a set consisting of real packed instances of malware and benign programs.

关键词： Malware obfuscation packing unpacking entropy classification algorithm

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SYMBOLIC ONE-CLASS LEARNING FROM IMBALANCED DATASETS: APPLICATION IN MEDICAL DIAGNOSIS

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2009年第2期18卷 273-309页

作者： Mena, Luis Gonzalez, Jesus A. Univ Zulia Fac Engn Dept Comp Sci Maracaibo 4011 Venezuela Natl Inst Astrophys Opt & Elect Dept Comp Sci Puebla Mexico

When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem;we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.

关键词： Machine learning imbalanced datasets one-class learning classification algorithm rule extraction

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Trend Detection for the Extent of Irrigated Agriculture in Idaho's Snake River Plain, 1984-2016

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REMOTE SENSING 2018年第1期10卷 145-145页

作者： Chance, Eric W. Cobourn, Kelly M. Thomas, Valerie A. Virginia Polytech Inst & State Univ Dept Forest Resources & Environm Conservat 310 West Campus Dr Blacksburg VA 24061 USA

Understanding irrigator responses to changes in water availability is critical for building strategies to support effective management of water resources. Using remote sensing data, we examine farmer responses to seasonal changes in water availability in Idaho's Snake River Plain for the time series 1984-2016. We apply a binary threshold based on the seasonal maximum of the Normalized Difference Moisture Index (NDMI) using Landsat 5-8 images to distinguish irrigated from non-irrigated lands. We find that the NDMI of irrigated lands increased over time, consistent with trends in irrigation technology adoption and increased crop productivity. By combining remote sensing data with geospatial data describing water rights for irrigation, we show that the trend in NDMI is not universal, but differs by farm size and water source. Farmers with small farms that rely on surface water are more likely than average to have a large contraction (over -25%) in irrigated area over the 33-year period of record. In contrast, those with large farms and access to groundwater are more likely than average to have a large expansion (over +25%) in irrigated area over the same period.

关键词： agriculture classification algorithm farm size groundwater irrigation technology surface water water rights

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Inductive learning from preclassified training examples: An empirical study

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 1998年第2期28卷 288-295页

作者： Li, WQ Aiken, M Univ Mississippi Dept Management & Mkt University MS 38677 USA

Many real-world decision-making problems fall into the general category of classification. algorithms for constructing knowledge by inductive inference from example have been widely used for some decades. Although these learning algorithms frequently address the same problem of learning from preclassified examples and much previous work in inductive learning has focused on the algorithms' predictive accuracy, little attention has been paid to the effect of data factors on the performance of a learning system. An experiment was conducted using five learning algorithms on two data sets to investigate how the change in labeling the class attribute can alter the behavior of learning algorithms. The results show that different preclassification rules applied on the training examples can affect either the classification accuracy or classification structure.

关键词： classification algorithm inductive learning learning system performance machine learning

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Subconcept perturbation-based classifier for within-class multimodal data

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NEURAL COMPUTING & APPLICATIONS 2024年第5期36卷 2479-2491页

作者： Cavalcanti, George D. C. Soares, Rodolfo J. O. Araujo, Edson L. Univ Fed Pernambuco UFPE Ctr Informat CIn Ave Jornalista Anibal Fernandes S-N Recife PE Brazil Univ Fed Vale Sao Francisco Ave Antonio Carlos Magalhaes 510 BR-48902300 Juazeiro BA Brazil

In classification, it is generally assumed that data from one class consist of one pure compact data cluster. However, in many cases, this cluster might consist of multiple subclusters, in other words, within-class multimodality. In such a scenario, it may be difficult or even impossible for a single classifier to find a suitable model using limited data. So, training a model using smaller chunks of data is an alternative that helps avoid complex models and reduces the task's complexity. This paper proposes the subconcept Perturbation-based Classifier (sPerC) that finds the best clusters per class using cluster validation measures, and one meta-classifier is trained per subcluster. This way, each class is represented by a set of meta-classifiers instead of one classifier. Such a design diminishes the complexity of the task, and using a divide-to-conquer strategy favors the precision of each meta-classifier. Through a set of comprehensive experiments on 30 datasets, the sPerC results compared favorably to other classifiers in multi-class classification tasks, showing that creating specialized classifiers per class in different regions of the feature space can be advantageous.

关键词： classification algorithm Multimodal data Subconcepts Cluster validation measures Perturbation

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Isomorph-free exhaustive generation of designs with prescribed groups of automorphisms

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SIAM JOURNAL ON DISCRETE MATHEMATICS 2005年第3期19卷 664-690页

作者： Kaski, P Aalto Univ Lab Theoret Comp Sci FI-02015 Helsinki Finland

We develop an algorithm framework for isomorph-free exhaustive generation of designs admitting a group of automorphisms from a prescribed collection of pairwise nonconjugate groups, where each prescribed group has a large index relative to its normalizer in the isomorphism-inducing group. We demonstrate the practicality of the framework by producing a complete classification of the Steiner triple systems of order 21 admitting a nontrivial automorphism group. The number of such pairwise nonisomorphic designs is 62336617, where 958 of the designs are anti-Pasch. We also develop consistency checking methodology for gaining confidence in the correct operation of the algorithm implementation.

关键词： classification algorithm combinatorial search consistency checking isomorph rejection isomorph-free generation Kramer-Mesner method Steiner triple system symmetry reduction

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High-resolution mass spectra processing for the identification of different pathological tissue types of brain tumors

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EUROPEAN JOURNAL OF MASS SPECTROMETRY 2017年第4期23卷 213-216页

作者： Zhvansky, E. S. Sorokin, A. A. Popov, I. A. Shurkhay, V. A. Potapov, A. A. Nikolaev, E. N. Moscow Inst Phys & Technol Dolgoprudnyi Moscow Region Russia Russian Acad Sci Inst Energy Problems Chem Phys Moscow Russia Minist Healthcare Russian Federat NN Burdenko Natl Sci & Pract Ctr Neurosurg Fed State Autonomous Inst Moscow Russia Skolkovo Inst Sci & Technol Skolkovo Moscow Region Russia

The purpose of the work is to demonstrate the possibilities of identifying the different types of pathological tissue identification directly through tissue mass spectrometry. Glioblastoma parts dissected during neurosurgical operation were investigated. Tumor fragments were investigated by the immunohistochemistry method and were identified as necrotic tissue with necrotized vessels, necrotic tissue with tumor stain, tumor with necrosis (tumor tissue as major), tumor, necrotized tumor (necrotic tissues as major), parts of tumor cells, boundary brain tissue, and brain tissue hyperplasia. The technique of classification of tumor tissues based on mass spectrometric profile data processing is suggested in this paper. Classifiers dividing the researched sample to the corresponding tissue type were created as a result of the processing. Classifiers of necrotic and tumor tissues are shown to yield a combined result when the tissue is heterogeneous and consists of both tumor cells and necrotic tissue.

关键词： Direct mass spectrometry ambient ionization brain tumor tissue identification classification algorithm neurosurgery

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Experimental bifurcation diagram of a circuit-implemented neuron model

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PHYSICS LETTERS A 2010年第45期374卷 4589-4593页

作者： Linaro, D. Poggi, T. Storace, M. Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

An experimental bifurcation diagram of a circuit implementing an approximation of the Hindmarsh-Rose (HR) neuron model is presented. Measured asymptotic time series of circuit voltages are automatically classified through an ad hoc algorithm. The resulting two-dimensional experimental bifurcation diagram evidences a good match with respect to the numerical results available for both the approximated and original HR model. Moreover, the experimentally obtained current-frequency curve is very similar to that of the original model. The obtained results are both a proof of concept of a quite general method developed in the last few years for the approximation and implementation of nonlinear dynamical systems and a first step towards the realisation in silica of HR neuron networks with tunable parameters. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Experimental bifurcation diagram Hardware neuron model classification algorithm

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A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

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Big Earth Data 2021年第2期5卷 237-257页

作者： Amin Naboureh Jinhu Bian Guangbin Lei Ainong Li Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina University of Chinese Academy of Sciences BeijingChina

Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale project of the Belt and Road Initiative(BRI),covers a region that is home to more than 1.6 billion *** numerous studies have been conducted on strategies and the economic potential of the Economic Corridor,reviewing LULCC mapping studies in this area has not been *** study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study *** review will be helpful for the decision-making of sustainable development and construction in the Economic *** this end,350 peer-reviewed journal and conference papers,as well as book chapters were analyzed based on 17 attributes,such as main driving factors of LULCC,data collection methods,classification algorithms,and accuracy assessment *** was observed that:(1)rapid urbanization,industrialization,population growth,and climate change have been recognized as major causes of LULCC in the study area;(2)LULCC has,directly and indirectly,caused several environmental issues,such as biodiversity loss,air pollution,water pollution,desertification,and land degradation;(3)there is a lack of well-annotated national land use data in the region;(4)there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area;and(5)several technical issues still require more attention from the scientific ***,several recommendations were proposed to address the identified issues.

关键词： Land use change land cover change China-Central Asia-West Asia Economic Corridor accuracy assessment reference data classification algorithm

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Fluorescence photo-bleaching of urine and its applicability in oral cancer diagnosis

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PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY 2019年 28卷 18-24页

作者： Dutta, Surjendu Bikash Krishna, Hemant Gupta, Sharad Majumder, Shovan K. Raja Ramanna Ctr Adv Technol Laser Biomed Applicat Div Indore 452013 Madhya Pradesh India Indian Inst Technol Indore Discipline Phys Indore 453552 Madhya Pradesh India HBNI Training Sch Complex Mumbai 400094 Maharashtra India Indian Inst Technol Indore Discipline Met Engn & Mat Sci Discipline Biosci & Biomed Engn Indore 453552 Madhya Pradesh India

Photo-stability of urine is of crucial importance for the applicability of fluorescence spectroscopy of urine samples for diagnosis of cancer. We report the results of a detailed study on fluorescence photo-bleaching of human urine samples. We also present the results of a preliminary investigation on evaluation of the applicability of photo-bleaching characteristics of urine for discriminating patients with oral cancer from healthy volunteers. The time-lapse fluorescence induced by continuous shining of 405 nm radiation from a diode laser was recorded from the urine samples obtained from 18 patients with oral cancer as well as from 22 healthy volunteers with history of no known major illness in the past two months. The integrated fluorescence intensity (Sigma I), calculated for each spectrum, was found to decrease with time till a point after which no further decrease was observed. Further, while significant differences were observed in the spectra of cancerous patients and healthy volunteers, these differences were found to be varying with time till the intensities of the observed fluorescence spectra corresponding to the two categories of urine samples became stable. The curve, generated by plotting Sigma I vs. time, was found to be best fitted (R-2 > 0.95) with a double-exponential decay function. The photo-bleaching constants, obtained from curve-fitting, were found to have statistically significant differences corresponding to the urine samples of cancerous patients and healthy volunteers. A classification algorithm developed based on nearest-mean classifier (NMC) and applied on the photo-bleaching constants in leave-one-subject-out cross-validation mode was found to provide a sensitivity and specificity of up to similar to 86% in discriminating the two categories of urine samples.

关键词： Fluorescence spectroscopy of urine Photo-bleaching classification algorithm Double-exponential function

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