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Attack-Detection Architectural Framework Based on Anomalous Patterns of System Performance and Resource Utilization-Part II

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IEEE ACCESS 2021年 9卷 87611-87629页

作者： Aloseel, Abdulmohsan Al-Rubaye, Saba Zolotas, Argyrios Shaw, Carl Cranfield Univ Sch Aerosp Transport & Mfg SATM Bedford MK43 0AL England Cerberus Secur Labs Ltd Bristol BS34 8RB Avon England

This paper presents a unique security approach for detecting cyber-attacks against embedded systems (ESs). The proposed approach has been shaped within an architectural framework called anomalous resource consumption detection (ARCD). The approach's detection mechanism detects cyber-attacks by distinguishing anomalous performance and resource consumption patterns from a pre-determinable reference model. The defense mechanism of this approach acts as an additional layer of protection for ESs. This technique's effectiveness was previously evaluated statistically, and in this paper, we tested this approach's efficiency computationally by using the support-vector machine algorithm. The datasets were generated and collected based on a testbed model, where it was run repeatedly under different operation conditions (normal cases (Rs) versus attacked cases). The executed attack scenarios are 1) denial-of-service (DoS);2) brute force (BF);and 3) remote code execution (RCE), and man-in-the-middle (MITM). A septenary tuple model, which consists of seven determinants that are analyzed based on seven statistical criteria, is the core of the detection mechanism. The prediction accuracy in terms of classifying anomalous patterns compared to normal patterns based on the confusion matrix revealed promising results, proving this approach's effectiveness, where the final results confirmed very high prediction accuracies in terms of distinguishing anomalous patterns from the typical patterns. Integrating the ARCD concept into an operating system's functionality could help software developers augment the existing security countermeasures of ESs. Adopting the ARCD approach will pave the way for software engineers to build more secure operating systems in line with the embedded system's capabilities, without depleting its resources.

关键词： Security Computer security Supervised learning Reinforcement learning Encryption Embedded systems Computational modeling Anomalous detection cybersecurity embedded systems machine learning support vector machine algorithm anomalous resource consumption detection framework

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Automatic categorization of diverse experimental information in the bioscience literature

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BMC BIOINFORMATICS 2012年第1期13卷 1-12页

作者： Fang, Ruihua Schindelman, Gary Van Auken, Kimberly Fernandes, Jolene Chen, Wen Wang, Xiaodong Davis, Paul Tuli, Mary Ann Marygold, Steven J. Millburn, Gillian Matthews, Beverley Zhang, Haiyan Brown, Nick Gelbart, William M. Sternberg, Paul W. CALTECH Howard Hughes Med Inst Pasadena CA 91125 USA CALTECH Div Biol Pasadena CA 91125 USA Wellcome Trust Sanger Inst Cambridge CB10 1SA England Univ Cambridge Dept Genet Cambridge CB2 3EH England Harvard Univ Dept Mol & Cellular Biol Cambridge MA 02138 USA Univ Cambridge Gurdon Inst Cambridge CB2 1QN England Univ Cambridge Dept Physiol Dev & Neurosci Cambridge CB2 1QN England

Background: Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method support vector machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results: We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at W

关键词： support vector machine Data Type support vector machine algorithm support vector machine Method Mouse Genomic Informatics

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Power System Transient Stability Assessment Model Construction Using Improved SVM

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IEEE ACCESS 2023年 11卷 131866-131876页

作者： Chen, Qianhui Shangqiu Inst Technol Sch Mech Engn Shangqiu 476000 Peoples R China

As computer technology and intelligence progress, many fields begin to develop towards intelligence. Power system is an important component of the whole power grid operation and the work of electronic components and electronic devices, so how to evaluate its stability is an important issue to ensure the stable operation of power system. In this study, a power system evaluation model using improved support vector machine algorithm is proposed for the transient state evaluation system of power system. Firstly, the characteristic vector is extracted from the transient steady-state data of power system, and then the traditional support vector machine algorithm is improved by adding Mahalanobis distance. Finally, the algorithm accuracy and precision are compared. It is found that the highest accuracy of the improved algorithm was 96.79%, which was 1.70% higher than that of the lowest algorithm. At the same time, the false alarm rate was lower, which was 0.42% lower than that of the highest algorithm. The accuracy of the improved support vector machine algorithm was 95.62%. Results show a higher accuracy and precision of the improved vector machine algorithm, and it is better in the processing ability and evaluation ability of power system data.

关键词： Power system stability Transient analysis support vector machines Generators Feature extraction Thermal stability Circuit faults Quality assessment Accuracy Power system transient state support vector machine algorithm assessment capability accuracy

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Fused spectral-decomposition seismic attributes and forward seismic modelling to predict sand bodies in meandering fluvial reservoirs

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MARINE AND PETROLEUM GEOLOGY 2019年 99卷 27-44页

作者： Yue, Dali Li, Wei Wang, Wurong Hu, Guangyi Qiao, Huili Hu, Jiajing Zhang, Manling Wang, Wenfeng China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China Res Inst CNOOC Beijing 100028 Peoples R China

Understanding the hierarchical architectural elements of fluvial sand bodies is important for planning their development strategy and to enhance oil recovery. Red-Green-Blue (RGB) blending of multiple seismic attributes and forwarding seismic modelling are commonly used in the analysis of compound sand bodies. However, RGB blending of multiple seismic attributes can only qualitatively describe the boundaries and thickness of sand bodies. The forward seismic modelling techniques previously documented in the literature are not effective when depicting the geometry of, and stacking relationships between, sand bodies (i.e., reservoir compartmentalisation). Hence, we propose in this work a new workflow that combines fused spectral-decomposition seismic attributes (SDSAs) and forwarding seismic modelling to quantitatively predict sand thickness, and to characterise stacking relationships between sand bodies. First, we employ a support vector machine (SVM) algorithm to fuse high, middle, and low frequency components (attributes) of seismic data so as to quantitatively predict the thickness of sand bodies. Second, we define the seismic waveform response patterns corresponding to the typical conceptual stacked sand bodies. With the constraints of waveform patterns and predicted sand thickness (fused SDSAs), the geometry and stacking relationships of the sand bodies are characterised by forward seismic modelling. To illustrate the effectiveness of our proposed workflow, we apply it to the Neogene Minghuazhen Formation (Nm) of the QHD 32-6 oil field, Bohai Bay Basin, China. We define five architectural elements of a meandering fluvial reservoir by analysing the hierarchy of sand bodies using our workflow. The predicted sand bodies in this workflow were further proven by horizontal drilling and production data.

关键词： Fused spectral-decomposition seismic attributes support vector machine algorithm Forward seismic modelling Stacked sand body models Reservoir compartmentalisation Bozhong sub-basin

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Taxonomy of Manufacturing Firms' Flexibility Under Influence of Dynamic Organizational Capabilities

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2021年第3期20卷 549-574页

作者： Khoobiyan, Mehdi Pooya, Alireza Tavakoli, Ahmad Rahimnia, Fariborz Ferdowsi Univ Mashhad Dept Management Mashhad Razavi Khorasan Iran

Many scholars mentioned various dimensions and complex relations for manufacturing flexibility that implementing all of them is not possible for a manufacturing firm. On the other hand, today's dynamic environment convinces flexible organizations to create capabilities in order to take over competitors. Hence, the aim of this study is to determine dominant groups of flexibility for manufacturing firms and to prioritize them under the influence of dynamic organizational capabilities. In this research, firstly components of dynamic organizational capabilities and flexibility of manufacturing are identified by content analysis method. Then, based on that, taxonomy for Iran's manufacturing firms was determined using support vector machine algorithm. In the next step, based on experts' view using FDEMATEL method, conceptual model for relationship of manufacturing flexibility dimension as well as dynamic capabilities are provided. Conceptual model in three flexibility clusters was tested by structural equation modeling, and dominant groups were prioritized based on dynamic organizational capabilities. Results show that the best number of clusters is three. Results also show that dynamic organizational capabilities not only affect flexibility dimensions of manufacturing but also this influence is different in manufacturing firms with various industry fields.

关键词： Typology manufacturing flexibility dynamic capabilities dominant groups support vector machine algorithm

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Forest Fire Risk Assessment and Mapping Using Remote Sensing and GIS Techniques: A Case Study in Nghe An Province, Vietnam

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2024年第9期60卷 982-991页

作者： Doan, Th. N. Ph. Trinh, L. H. Zablotskii, V. R. Nguyen, V. T. Tran, X. T. Pham, Th. Th. H. Le, Th. Th. H. Le, V. Ph. Hanoi Univ Min & Geol Geomat Earth Sci Res Grp Hanoi Vietnam Le Quy Don Tech Univ Hanoi Vietnam Moscow State Univ Geodesy & Cartog Moscow 105064 Russia

This paper presents the results of modeling the risk of forest fires in the west of Nghe An Province (north-central Vietnam) using remote sensing and GIS data. We built models for the occurrence of forest fires using machine learning methods, including Random Forest (RF), Suppor vector machine (SVM), and Classification and Regression Trees (CART). The models took into account nine factors influencing the risk of forest fires, including vegetation cover (the normalized difference vegetation index (NDVI)), surface evapotranspiration, elevation, slope, aspect, wind speed, ground surface temperature, average monthly precipitation, and population density. Various parameters are tested in the RF, SVM, and CART algorithms to select the algorithm with the highest accuracy in forest fire risk prediction. The results show that the RF algorithm with the value of the "numberOfTrees" parameter equal to 100 has the highest accuracy in predicting the risk of forest fires in the study area.

关键词： forest fire risk remote sensing GIS support vector machine algorithm Nghe An province

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Mapping distribution of water hyacinth (Eichhornia crassipes) in Rwanda using multispectral remote sensing imagery

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AFRICAN JOURNAL OF AQUATIC SCIENCE 2019年第4期44卷 339-348页

作者： Mukarugwiro, J. A. Newete, S. W. Adam, E. Nsanganwimana, F. Abutaleb, K. A. Byrne, M. J. Univ Witwatersrand Sch Anim Plant & Environm Sci APES Johannesburg South Africa Univ Witwatersrand Ctr Invas Biol Sch APES Johannesburg South Africa Agr Res Council Inst Soil Climate & Water Pretoria South Africa Univ Witwatersrand Sch Geog Archaeol & Environm Studies GAES Johannesburg South Africa Univ Rwanda African Ctr Excellence Innovat Teaching & Learnin Kigali Rwanda Natl Author Remote Sensing & Space Sci NARSS Cairo Egypt

Water hyacinth, Eichhornia crassipes (C. Mart) Solms (Pontederiaceae), is an invasive aquatic macrophyte with major negative economic and ecological impacts in Rwanda and other East African countries since its establishment in the region in the 1960s. Reliable estimates of water hyacinth distribution are required to determine the severity of the problem and identify waterbodies requiring management. Remote sensing techniques, based on the Landsat 8 sensor, offer promising alternatives to accurately detect, map and monitor the extent of the water hyacinth invasion in Rwandan waterbodies. The aim of the current study was to investigate the utility of multispectral remote sensed imagery using Random Forest and support vector machine algorithms to detect and map water hyacinth in Rwandan waterbodies. Random Forest had a high overall accuracy of 85%, compared with support vector machine (65%). These algorithms confirmed different levels of water hyacinth infestations in three main Rwandan rivers. Many of the wetlands along these riparian systems and most of the lakes, particularly those from the Eastern Province of the country were found to be invaded by water hyacinth. These findings would, therefore, assist government partners and policy makers to put in place sustainable methods, such as biological control, along with integrated pest management, to control the of water hyacinth invasion in Rwanda.

关键词： invasive aquatic macrophytes Landsat 8 Random Forest algorithm spatial distribution support vector machine algorithm

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Detection of Denatonium Benzoate (Bitrex) Remnants in Noncommercial Alcoholic Beverages by Raman Spectroscopy

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JOURNAL OF FORENSIC SCIENCES 2014年第5期59卷 1358-1363页

作者： Kwiatkowski, Andrzej Czerwicka, Malgorzata Smulko, Janusz Stepnowski, Piotr Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland Univ Gdansk Fac Chem PL-80308 Gdansk Poland

Illegal alcoholic beverages are often introduced into market using cheap technical alcohol, which is contaminated by denatonium benzoate (Bitrex) of very small concentration. Bitrex is the most bitter chemical compound and has to be removed before alcohol consumption. The home-made methods utilize sodium hypochlorite to disintegrate particles of denatonium benzoate in alcohol and to remove bitter taste before trading. In this experimental studies, we propose a novel method that detects in a fast way the remnants of denatonium benzoate in dubious alcohol samples by Raman spectroscopy. This method applies a portable Raman spectrometer of excitation wavelength 785nm and utilizes the effect of surface-enhanced Raman spectroscopy (SERS) to recognize the suspected alcoholic beverages. High effectiveness (over 98%) of YES/NO classification of the investigated samples was observed when the nonlinear algorithm support vector machine (SVM) was exploited at carefully adjusted detection parameters. The method can identify illicit alcohol within minutes.

关键词： forensic science Raman spectroscopy denatonium benzoate Bitrex illicit alcohol support vector machine algorithm

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Determination of sodium alginate in algae by near-infrared spectroscopy

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DESALINATION AND WATER TREATMENT 2019年第0期168卷 117-122页

作者： Xu, Peilong Na, Na Gao, Shouwu Geng, Cunzhen Qingdao Univ State Key Lab Biofibers & Ecotext Qingdao 266071 Shandong Peoples R China Qingdao Municipal Hlth Commiss Qingdao 266071 Shandong Peoples R China

In this study, the components of natural seaweed fiber were determined by near-infrared spectroscopy, and the qualitative analysis model of natural seaweed was established by using support vector machine algorithm based on principal component analysis. For the natural algae containing sodium alginate, a near-infrared spectrum analysis model for the content of sodium alginate was established by using multi-model method. The model has very good analytical and predictive ability. The model can be used to distinguish the composition and active component contents of natural algae quickly, and it also has the advantages of fast, nondestructive, convenient and so on.

关键词： Near-infrared spectrum Natural algae support vector machine algorithm Multi-model method

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Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features

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VIROLOGY JOURNAL 2009年第1期6卷 1-8页

作者： Kumar, Shiva Ansari, Faraz A. Scaria, Vinod CSIR Inst Genom & Integrat Biol GN Ramachandran Knowledge Ctr Genome Informat Delhi 110007 India

MicroRNAs (small similar to 22 nucleotide long non-coding endogenous RNAs) have recently attracted immense attention as critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Recent studies have proved that viruses also express microRNAs, which are thought to contribute to the intricate mechanisms of host-pathogen interactions. Computational predictions have greatly accelerated the discovery of microRNAs. However, most of these widely used tools are dependent on structural features and sequence conservation which limits their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs. In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors. The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.

关键词： support vector machine support vector machine algorithm Latency Associate Transcript Hairpin Sequence microRNA Precursor

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