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Application Behavior Identification in DNS Tunnels Based on Spatial-Temporal Information

IEEE ACCESS 2021年 9卷 80639-80653页

作者： Bai, Huiwen Liu, Weiwei Liu, Guangjie Dai, Yuewei Huang, Shuhua Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

Due to the capability of passing through heavily censored networks or gateway equipped with the traffic-monitoring module, DNS tunnel has been the dominant covert communication technique for command and control between the victim and the attacker in network attack events. Although the discovery of DNS tunnel has been intensively studied, the internal application behavior identification for DNS tunnels still remains a challenging problem. The fine-gained identification can help to reveal more behavior information wrapped in DNS tunnels. In this study, we investigate the spatial-temporal information from the raw packets to identify the internal application behaviors in DNS tunnels. Multi-dimensional features on packet length and timing for DNS tunnels with different internal application behaviors are incorporated with a machine-learning algorithm to identify the internal application behaviors in DNS tunnels. We consider 4 common types of application behaviors in our research, including browsing webpages, emailing, downloading data, and controlling the remote servers. The experimental results show that the proposed scheme can achieve higher identification accuracy with a much lower packet consuming rate when compared with the state-of-the-art internal protocol identification scheme. The experiment results depict that our proposed scheme is better in terms of F-score, which can reach 99% with only 100 packets.

关键词： Tunneling Feature extraction Protocols machine learning Payloads Neural networks Malware DNS tunnel internal application behavior identification spatial-temporal features machine-learning algorithm

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Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network algorithm

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Computers, Materials & Continua 2021年第7期68卷 805-819页

作者： Fawaz Waselallah Alsaade Theyazn H.H.Aldhyani Mosleh Hmoud Al-Adhaileh College of Computer Science and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia Community College of Abqaiq King Faisal UniversityAl-AhsaSaudi Deanship of E-Learning and Distance Education King Faisal University Saudi Arabia Al-AhsaSaudi Arabia

The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of *** study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray *** test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS *** scenarios were considered to develop a system for predicting *** the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray *** segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting *** features were processed using SVM and *** the second scenario,a CNN transfer model(ResNet 50)was used to detect *** system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,***,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.

关键词： machine-learning algorithm recognition system COVID-19 convolutional neural network

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Testing and Validating Semi-automated Approaches to the Occupational Exposure Assessment of Polycyclic Aromatic Hydrocarbons

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ANNALS OF WORK EXPOSURES AND HEALTH 2021年第6期65卷 682-693页

作者： Santiago-Colon, Albeliz Rocheleau, Carissa M. Bertke, Stephen Christianson, Annette Collins, Devon T. Trester-Wilson, Emma Sanderson, Wayne Waters, Martha A. Reefhuis, Jennita Ctr Dis Control & Prevent Natl Inst Occupat Safety & Hlth 1090 Tusculum Ave Cincinnati OH 45226 USA Univ Cincinnati Dept Environm & Publ Hlth Sci Kettering Lab Bldg0056 160 Panzeca Way Cincinnati OH 45267 USA Univ Kentucky Coll Publ Hlth Dept Epidemiol 111 Washington Ave Lexington KY 40536 USA Inova Fairfax Med Campus 8110 Gatehouse Rd Falls Church VA 22042 USA Ctr Dis Control & Prevent Natl Ctr Birth Defects & Dev Disabil 4770 Buford HighwayMS-S106-3 Atlanta GA 30341 USA

Introduction: When it is not possible to capture direct measures of occupational exposure or conduct biomonitoring, retrospective exposure assessment methods are often used. Among the common retrospective assessment methods, assigning exposure estimates by multiple expert rater review of detailed job descriptions is typically the most valid, but also the most time-consuming and expensive. Development of screening protocols to prioritize a subset of jobs for expert rater review can reduce the exposure assessment cost and time requirement, but there is often little data with which to evaluate different screening approaches. We used existing job-by-job exposure assessment data (assigned by consensus between multiple expert raters) from a large, population-based study of women to create and test screening algorithms for polycyclic aromatic hydrocarbons (PAHs) that would be suitable for use in other population-based studies. Methods: We evaluated three approaches to creating a screening algorithm: a machine-learning algorithm, a set of a priori decision rules created by experts based on features (such as keywords) found in the job description, and a hybrid algorithm incorporating both sets of criteria. All coded jobs held by mothers of infants participating in National Birth Defects Prevention Study (NBDPS) (n = 35,424) were used in developing or testing the screening algorithms. The job narrative fields considered for all approaches included job title, type of product made by the company, main activities or duties, and chemicals or substances handled. Each screening approach was evaluated against the consensus rating of two or more expert raters. Results:The machine-learning algorithm considered over 30,000 keywords and industry/occupation codes (separate and in combination). Overall, the hybrid method had a similar sensitivity (87.1%) as the expert decision rules (85.5%) but was higher than the machine-learning algorithm (67.7%). Specificity was best in the machine-lea

关键词： exposure assessment female worker jobs machine-learning algorithm National Birth Defects Prevention Study occupation prediction model polycyclic aromatic hydrocarbons population-based regularized logistic regression

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machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study

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SCIENTIFIC REPORTS 2024年第1期14卷 1-9页

作者： Yoon, Soo Bin Lee, Jeong-Moo Jung, Chul-Woo Suh, Kyung-Suk Lee, Kwang-Woong Yi, Nam-Joon Hong, Suk Kyun Choi, Youngrok Hong, Su young Lee, Hyung-Chul Seoul Natl Univ Coll Med Seoul Natl Univ Hosp Dept Anesthesiol & Pain Med 101 Daehak Ro Seoul 03080 South Korea Seoul Natl Univ Coll Med Seoul Natl Univ Hosp Dept Surg Seoul South Korea

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models. Long short-term memory (LSTM) and gradient-boosted regression tree (GBRT) models were developed using time-series doses and concentrations of tacrolimus with covariates of age, sex, weight, height, liver enzymes, total bilirubin, international normalized ratio, albumin, serum creatinine, and hematocrit. We conducted performance comparisons with linear regression and populational pharmacokinetic models, followed by external validation using the eICU Collaborative Research Database collected in the United States between 2014 and 2015. In the external validation, the LSTM outperformed the GBRT, linear regression, and populational pharmacokinetic models with median performance error (8.8%, 25.3%, 13.9%, and - 11.4%, respectively;P < 0.001) and median absolute performance error (22.3%, 33.1%, 26.8%, and 23.4%, respectively;P < 0.001). Dosing based on the LSTM model's suggestions achieved therapeutic concentrations more frequently on the chi-square test (P < 0.001). Patients who received doses outside the suggested range were associated with longer ICU stays by an average of 2.5 days (P = 0.042). In conclusion, machine learning models showed excellent performance in predicting tacrolimus concentration in liver transplantation recipients and can be useful for concentration titration in these patients.

关键词： Tacrolimus concentration Liver transplantation machine-learning algorithm Long short-term memory Gradient-boosted regression tree

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An automated optical inspection (AOI) platform for three-dimensional (3D) defects detection on glass micro-optical components (GMOC)

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OPTICS COMMUNICATIONS 2023年第1期545卷

作者： Du, Yinchao Chen, Jiangpeng Zhou, Han Yang, Xiaoling Wang, Zhongqi Zhang, Jie Shi, Yuechun Chen, Xiangfei Zheng, Xuezhe Nanjing Univ Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China Nanjing Univ Microwave Photon Technol Lab Coll Engn & Appl Sci Nanjing 210093 Peoples R China CAS Intelligent Comp Technol Suzhou 215000 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China Chinese Acad Sci Inst Comp technol Beijing 100090 Peoples R China Innolight Technol Res Inst ITRI 8 Xiasheng Rd Suzhou Ind Pk Suzhou 215000 Peoples R China

With the widespread deployment of wavelength division multiplexing (WDM), optical transceivers increasingly use many glass micro-optical components (GMOC). Visual inspection of these GMOCs is a critical manufacturing step to ensure quality and reliability. However, manual inspection is often labor-intensive and time-consuming due to the transparent nature of glass components and the small, randomly located defects in three dimensions. Although automated optical inspection (AOI) exists, it has not yet been able to provide the desired level of accuracy and efficiency. This paper reports the development of an AOI platform for 3D defect detection on GMOCs. The platform incorporates 3D video acquisition and a novel two-stage neural network machine-learning algorithm. It includes a robotic arm for moving parts in 3D, a camera with an illumination module for video acquisition, and a video streaming processing unit with a machine vision algorithm for real-time defect detection on a production line. The robotic arm enables multi-perspective video capture of a test sample without refocusing. The twostage machine learning network uses a modified YOLOv4 architecture with color channel separation (CCS) convolution, an image quality evaluation (IQE) module, and a frame fusion module to integrate the single frame detection results. This network can process multi-perspective video streams in real-time for defects detection in a coarse-to-fine manner. The AOI platform was trained with only 30 samples and achieved promising performances with a recall rate of 1, a detection accuracy of 97%, and an inspection time of 48 s per part.

关键词： Automated optical inspection Glass micro -optical components Defects detection 3D video acquisition machine-learning algorithm

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Design method for polyurethane-modified asphalt by using Kriging-Particle Swarm Optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期117卷

作者： Lu, Pengzhen Ye, Kai Jin, Tian Ma, Yiheng Huang, Simin Zhou, Chenhao Zhejiang Univ Technol Hangzhou 310014 Zhejiang Peoples R China

The preparation process of polyurethane (PU)-modified bitumen involves numerous design parameters and performance response indexes. Due to the variety of polyurethane modifiers, the preparation process of the polyurethane-modified bitumen is not universally applicable. However, the traditional methods such as the response surface method and orthogonal design method have some problems such as low accuracy and a large number of samples required in the preparation process design. Therefore, according to different application environments, the problem of determining the process parameters of the polyurethane-modified bitumen accurately and efficiently needs to be solved urgently. Using Kriging-Particle Swarm Optimization (PSO) algorithm, an efficient process design method for the preparation of polyurethane modified asphalt is proposed in this paper. Combined with the sensitivity analysis method, the relatively sensitive response indexes are screened out to reduce the number of samples and improve the design accuracy. Among them, the dispersion coefficient was evaluated by fluorescence microscopy test using the Christiansen coefficient method to evaluate the uniformity of the dispersed phase of the polyurethane modifier. According to the target performance, the main process parameters of PU modified asphalt were obtained by the Kriging-Particle Swarm Optimization algorithm: shear time 86 min, shear speed 2450 rpm, shear temperature 148 degrees C, and polyurethane content 18.6%. The polyurethane-modified bitumen prepared by this optimal process met the expected performance indicators. This study achieved the expected results with a small number of samples, indicating that this method can achieve the purpose of designing the ideal process parameters of polyurethane-modified asphalt efficiently.

关键词： Polyurethane-modified bitumen Preparation process machine-learning algorithm Kriging-PSO model Design method

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A machine learning based prediction model for short term efficacy of nasopharyngeal carcinoma

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Zhong, Qiulu Li, Xiangde Du, Qinghua Liang, Qianfu Luo, Danjing Wen, Jiaying Yue, Haiying Liu, Wenqi Zhu, Xiaodong Li, Jian Guangxi Med Univ Dept Radiat Oncol Affiliated Hosp 2 Nanning 530000 Peoples R China Guangxi Med Univ Dept Radiat Oncol Wuming Hosp Nanning 530000 Peoples R China Guangxi Med Univ Dept Radiat Oncol Canc Hosp Nanning 530000 Peoples R China

The radiological dosimetric parameters and clinical features were screened by machine learning to construct a prediction model for the short-term efficacy of locally advanced Nasopharyngeal Carcinoma (LANPC). Patients diagnosed with Nasopharyngeal Carcinoma were retrospectively collected in the study. Twenty-four clinical features and twelve radiological dosimetric features were included. Three machine learning algorithms were used to construct predictive models for the short-term efficacy of LANPC. Kaplan-Meier log-rank method was used to compare the prognosis of patients with different efficacies in the model. The reliability of the model was evaluated using the calibration curve and the area under the curve (AUC). There were 194 patients who met the inclusion criteria. Among the three models being constructed, Random forest (RSF) model showed the best predictive ability, with AUC values of 1.000 in the training group and 0.944 in the test group, followed by XGBoost decision tree (GBDT) model (0.866/0.849) and decision tree (DT) model (0.848/0.783). In RSF model, the 3-year and 5-year overall survival rates of patients in complete remission (CR) group were 98.9% (95% CI 0.9688-1.0000) and 89.7% (95% CI 0.8256-0.9752), *** for patients in non-CR group, the 3-year and 5-year overall survival (OS) rate was 100% (95%CI 1.000 similar to 1.000) and 98.8% (95% CI 0.9652-1.0000), respectively. There has statistically significant difference between the two groups (P = 0.0037). RSF model constructed by machine-learning algorithm based on radiological dosimetric parameters and clinical characteristics can better predict the short-term efficacy of LANPC, and is an effective tool to evaluate the short-term efficacy of different LANPC patients during treatment.

关键词： Locoregionally advanced nasopharyngeal carcinoma Short-term efficacy machine-learning algorithm Radiological dosimetric parameters

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On the Problem of State Recognition in Injection Molding Based on Accelerometer Data Sets

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SENSORS 2022年第16期22卷 020052-405页

作者： Brunthaler, Julian Grabski, Patryk Sturm, Valentin Lubowski, Wolfgang Efrosinin, Dmitry AISEMO GmbH A-4675 Weibern Austria Linz Ctr Meachtron GmbH A-4040 Linz Austria Johannes Kepler Univ Linz Inst Stochast A-4040 Linz Austria

The last few decades have been characterised by a very active application of smart technologies in various fields of industry. This paper deals with industrial activities, such as injection molding, where it is required to monitor continuously the manufacturing process to identify both the effective running time and down-time periods. Supervised machine learning algorithms are developed to recognize automatically the periods of the injection molding machines. The former algorithm uses directly the features of the descriptive statistics, while the latter one utilizes a convolutional neural network. The automatic state recognition system is equipped with an 3D-accelerometer sensor whose datasets are used to train and verify the proposed algorithms. The novelty of our contribution is that accelerometer data-based machine learning models are used to distinguish producing and non-producing periods by means of recognition of key steps in an injection molding cycle. The first testing results show the approximate overall balanced accuracy of 72-92% that illustrates the large potential of the monitoring system with the accelerometer. According to the ANOVA test, there are no sufficient statistical differences between the comparative algorithms, but the results of the neural network exhibit higher variances of the defined accuracy metrics.

关键词： injection molding accelerometer sensor state recognition machine-learning algorithm convolutional neural network

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Recent Progress in Bio-Integrated Intelligent Sensing System

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2022年第6期4卷

作者： Liu, Mengwei Zhang, Yujia Tao, Tiger H. Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol State Key Lab Transducer Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Sch Grad Study Beijing 100049 Peoples R China Univ Oxford Dept Chem Oxford OX1 3TA England Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol 2020 X Lab Shanghai 200050 Peoples R China ShanghaiTech Univ Sch Phys Sci & Technol Shanghai 200031 Peoples R China Inst Brain Intelligence Technol Zhangjiang Lab Shanghai 200031 Peoples R China Shanghai Res Ctr Brain Sci & Brain Inspired Intel Shanghai 200031 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China

It is incontrovertible that the bioelectronic enabled bio-integrated system is one of the most promising technologies in the upcoming decades. Driven by the great versatility of the emerging artificial intelligence, machine-learning-supported bio-integrated intelligent sensing system (BISS) is capable of achieving data processing and intelligent recognition while conducting multimodal human-centered sensing;such facilitated systems capitalize the benefits of both bioelectronics and supporting algorithm, enabling accurate physiological/somatosensory recognition at the cost of certain computing resources. Herein, an overview of recent progress in BISS is presented, with an emphasis on high-tech applications enabled by innovations in combining flexible bioelectronic sensors with supporting algorithmic systems. The main applications can be divided into three categories, including implantable, skin-mounted, and wearable BISS, which have different requirements for materials, fabrication methods, and algorithms, respectively. Advances in these areas open new avenues for employing BISS as future human-machine interfaces for personalized healthcare, human enhancement, as well as other broad applications.

关键词： implantable devices intelligent bio-integrated system machine-learning algorithm multimodal sensing skin electronics wearable devices

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Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

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REMOTE SENSING OF ENVIRONMENT 2021年 260卷 112453-112453页

作者： Camacho, Fernando Fuster, Beatriz Li, Wenjuan Weiss, Marie Ganguly, Sangram Lacaze, Roselyne Baret, Fred Parc Cient Univ Valencia EOLAB C Catedrat Agustin Escardino 9 Valencia 46980 Spain EMMAH Domaine St Paul INRA UMR114 F-84914 Avignon France NASA Ames Res Ctr BAERI Moffett Field California CA 94043 USA HYGEOS Euratechnol 165 Ave Bretagne F-59000 Lille France

Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to retrieve GAI and fAPAR from the Landsat-8 top of canopy reflectance values, either using empirical or simulated calibration databases and generic or crop-type specific algorithms. Results show that using the six Landsat-8 bands together provided the best estimates of GAI and fAPAR. machine learning techniques trained over a dataset simulated by the PROSAIL model provided less accurate estimates of GAI and fAPAR as compared to machine learning techniques trained over the ground data collected and applied over a similar dataset (best-case scenario). All machine learning techniques performed similarly when calibrated on PROSAIL simulations. The Gaussian process regression (GPR) was the best performing machine learning technique as compared to artificial neural networks (ANN), support vector machine regression (SVM) and as compared to NDVI simple model when calibrated over the ground dataset in the best-case scenario. However, the performance of the GPR trained over ground data is notably degraded when applied to crop types excluded from the calibration (worst-case scenario), and models based on simulations performs similarly of even better. Furthermore, training the GPR over specific crop types was performing slightly better than training over all the crop types together if at least 100 well di

关键词： GAI fAPAR Landsat-8 machine-learning algorithm Croplands Gaussian processes Regresion PROSAIL simulations Artificial neural networks

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