关键词： Ransomware   Volatile memory   Forensics   Memory dumps   Virtual machine   Private cloud   Machine Learning   Detection   Malware  

摘要： Cloud computing is one of today's most popular and important IT trends. Currently, most organizations use cloud computing services (public or private) as part of their computer infrastructure. Virtualization technology is at the core of cloud computing, and virtual resources, such as virtual servers, are commonly used to provide services to the entire organization. Due to their importance and prevalence, virtual servers in an organizational cloud are constantly targeted by cyber-attackers who try to inject malicious code or malware into the server (e.g., ransomware). Many times, server administrators are not aware that the server has been compromised, despite the presence of detection solutions on the server (e.g., antivirus engine). In other cases, the breach is detected after a long period of time when significant damage has already occurred. Thus, detecting that a virtual server has been compromised is extremely important for organizational security. Existing security solutions that are installed on the server (e.g., antivirus) are considered untrusted, since malware (particularly sophisticated ones) can evade them. Moreover, these tools are largely incapable of detecting new unknown malware. Machine learning (ML) methods have been shown to be effective at detecting malware in various domains. In this paper, we present a novel methodology for trusted detection of ransomware in virtual servers on an organization's private cloud. We conducted trusted analysis of volatile memory dumps taken from a virtual machine (memory forensics), using the Volatility framework, and created general descriptive meta-features. We leveraged these meta-features, using machine learning algorithms, for the detection of unknown ransomware in virtual servers. We evaluated our methodology extensively in five comprehensive experiments of increasing difficulty, on two different popular servers (IIS server and an email server). We used a collection of real-world, professional, and notorious r

关键词： Radon   Machine learning   Neural network   Linear regression   Environment   Sensitivity analysis  

摘要： Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon (Rn-222). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resamplecl on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant- as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical predictor

关键词： Animal cells   Artificial intelligence   Artificial neural networks   Asia   Biology and life sciences   Cell biology   Cellular neuroscience   Cellular types   China   Computational biology   Computational neuroscience   Computer and information sciences   Computer networks   Database and informatics methods   Epidemiology   Forecasting   Geographical locations   HIV epidemiology   Infectious diseases   Information retrieval   Internet   Mathematical and statistical techniques   Mathematics   Medicine and health sciences   Neurons   Neuroscience   People and places   Physical sciences   Research and analysis methods   Research Article   Statistical methods   Statistics (mathematics)   Viral diseases  

摘要： Background AIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Internet, the 'big data' aggregated from Internet search engines, which contain users' information on the concern or reality of their health status, provide a new opportunity for AIDS surveillance. This paper uses search engine data to monitor and forecast AIDS in China. Methods A machine learning method, artificial neural networks (ANNs), is used to forecast AIDS incidences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu. com, are collected and selected as the input variables of ANNs, and officially reported actual AIDS incidences and deaths are used as the output variable. Three criteria, the mean absolute percentage error, the root mean squared percentage error, and the index of agreement, are used to test the forecasting performance of the ANN method. Results Based on the monthly time series data from January 2011 to June 2017, this article finds that, under the three criteria, the ANN method can lead to satisfactory forecasting of AIDS incidences and deaths, regardless of the change in the number of search queries. Conclusions Despite the inability to self-detect HIV/AIDS through online searching, Internet-based data should be adopted as a timely, cost-effective complement to a traditional AIDS surveillance system.

关键词： 机器学习   敏感性评估   K近邻   朴素贝叶斯   随机森林  

摘要： 为了比较不同机器学习方法在灾害敏感性评估中的适用性,将K近邻、朴素贝叶斯和随机森林3种典型机器学习方法运用到灾害敏感性评估中,比较3种方法在预测准确率、混淆矩阵和敏感性图上的异同;并基于深圳市电动自行车出险数据以及孕灾环境数据,运用3种方法评估深圳市电动自行车出险的敏感性,进而绘制敏感性图,探讨3种方法对于不同性质数据的适用条件,为开展灾害敏感性评估工作时选取更为适宜的方法,提供参考建议。

关键词： Analytical techniques   Proteins   Spectroscopy   Terahertz spectroscopy   Thin films   Transmission coefficient  

摘要： The development of new spectral analysis methods in bio thin-film detection has generated intense interest in terahertz (THz) spectroscopy and its application in a wide range of fields. In this paper, it is the first time that machine learning methods are applied to the quantitative characterization of bovine serum albumin (BSA) deposited thin-films detected by terahertz time-domain spectroscopy. The spectra data of BSA thin-films prepared by solutions with concentrations ranging from 0.5 to 35 mg/ml are analyzed using the support vector regression method to learn the underlying model of the frequency against the target concentration. The learned mode successfully predicts the concentrations of the unknown test samples with a coefficient of determination R-2 = 0.97932. Furthermore, aiming to identify the relevance of each frequency to the concentration, the maximal information coefficient statistical analysis is used and the three most discriminating frequencies in THz frequency are identified at 1.2, 1.1 and 0.5 THz respectively, which means a good prediction for BSA concentration can be achieved by using the top three relevant frequencies. Moreover, the top discriminating frequencies are in good agreement with the frequencies predicted by a long-wavelength elastic vibration model for BSA protein. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： SEDENTARY BEHAVIOR   STANDING   RANDOM FOREST CLASSIFIER   FREE-LIVING  

摘要： Purpose This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data. Methods Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit-stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models. Results Overall, the algorithm predicted the postures with moderate accuracy (stepping, 77%;standing, 63%;sitting, 67%;sit-to-stand, 52%;and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts. Conclusions This is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted.

关键词： groundwater   dissolved organic nitrogen   urbanization   machine learning   sensitivity analysis  

摘要： The effect of groundwater nutrient inputs on river and estuary water quality and the potential impacts of urbanization on groundwater are central concerns in many coastal areas. It has been previously identified that dissolved organic nitrogen (DON) can be the dominant form of total dissolved nitrogen (TDN) in some aquifers. However, there is a paucity of evidence about the sources and flow paths of DON, relative to inorganic nitrogen in groundwater. DON and dissolved organic carbon/DON were first compared against different landscape variables in this study, and no significant relationships were found. However, the relationships became statistically significant when shallow samples (sampling depth < 10 m) were separated from deep samples. A random forest model and sensitivity analysis were then applied to further our understanding of the ecohydrological drivers and seasonal patterns that shape DON variability. The random forest algorithm was built to classify 171 groundwater wellbores into three classes (low: <0.5 mg/L;medium: 0.5-2.5 mg/L;and high: >2.5 mg/L) which achieved 72% classification accuracy using landscape characteristics, hydrological conditions, and temporal information. The results indicated that the effects of landscapes on sandy shallow groundwater DON were controlled both by certain landscape characteristics and depth to groundwater. A conceptual model of groundwater DON is therefore proposed where the balance of exposure and processing time scales from the surface to groundwater is the critical control on the preservation of landscape signatures;we expect that this conceptual model would be applicable for other sandy, shallow groundwater areas.

关键词： Artificial neural networks   Machine learning   Particle swarm optimization   Strength of soft soils  

摘要： Shear strength of the soil is an important engineering parameter used in the design and audit of geo-technical structures. In this research, we aim to investigate and compare the performance of four machine learning methods, Particle Swarm Optimization - Adaptive Network based Fuzzy Inference System (PANFIS), Genetic Algorithm - Adaptive Network based Fuzzy Inference System (GANFIS), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), for predicting the strength of soft soils. For this purpose, case studies of 188 plastic clay soil samples collected from two major projects, Nhat Tan and Cua Dai bridges in Viet Nam have been used for generating training and testing datasets for constructing and validating the models. Validation and comparison of the models have been carried out using RMSE, and R. The results show that the PANFIS has the highest prediction capability (RMSE = 0.038 and R = 0.601), followed by the GANFIS (RMSE = 0.04 and R = 0.569), SVR (RMSE = 0.044 and R = 0.549), and ANN (RMSE = 0.059 and R = 0.49). It can be concluded that out of four models the PANFIS indicates as a promising technique for prediction of the strength of soft soils.

关键词： Knee osteoarthritis   cartilage damage index   informative locations   feature representation   machine learning  

摘要： This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage compartment from 3-D MR imaging and used principal component analysis (PCA) analysis to process the feature set. Four machine learning methods (artificial neural network (ANN), support vector machine, random forest, and naive Bayes) were employed to predict the progression of OA, which was measured by the change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on Medial compartment (JSM) grade, and Joint Space Narrowing on Lateral compartment (JSL) grade. To examine the different effects of medial and lateral informative locations, we have divided the 36-D feature set into a 18-D medial feature set and a 18-D lateral feature set and run the experiment on four classifiers separately. Experiment results showed that the medial feature set generated better prediction performance than the lateral feature set, while using the total 36-D feature set generated the best. PCA analysis is helpful in feature space reduction and performance improvement. For KL grade prediction, the best performance was achieved by ANN with AUC = 0.761 and F-measure = 0.714. For JSM grade prediction, the best performance was achieved by random forest with AUC = 0.785 and F-measure = 0.743, while for JSL grade prediction, the best performance was achieved by ANN with AUC 0.695 and F-measure = 0.796. As experiment results showing that the informative locations on medial compartment provide more distinguishing features than informative locations on the lateral compartment, it could be considered to select more points from the medial compartment while reducing the number of points from the lateral compartment to improve clinical CDI design.

关键词： Feature representation   automated microaneurysm detection   diabetic retinopathy   random forest   support vector machine   neural network  

摘要： Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR. In this paper, we analyzed MA detectability using small 25 by 25 pixel patches extracted from fundus images in the DIAbetic RETinopathy DataBase - Calibration Level 1 (DIARETDB1). Raw pixel intensities of extracted patches served directly as inputs into the following classifiers: random forest (RF), neural network, and support vector machine. We also explored the use of two techniques (principal component analysis and RF feature importance) for reducing input dimensionality. With traditional machine learning methods and leave-10-patients-out cross validation, our method outperformed a deep learning-based MA detection method, with AUC performance improved from 0.962 to 0.985 and F-measure improved from 0.913 to 0.926, using the same DIARETDB1 database. Furthermore, we validated our method on a different dataset-retinopathy online challenge (ROC) data set. The performance of the three classifiers and the pattern with different percentage of principal components are consistent on the two data sets. Especially, we trained the RF on DIARETDB1 and applied it to ROC;the performance is very similar to that of the RF trained and tested using cross validation on ROC data set. This result indicates that our method has the potential to generalize to different datasets.