Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

With the high level of proliferation of connected mobile devices,the risk of intrusion becomes higher.Artificial Intelligence(AI)and Machine Learning(ML)algorithms started to feature in protection software and showed effective results.These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’malware,especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly.In this article,we present a framework for mobile malware detection based on a new dataset containing new categories of mobile malware.We focus on categories of malware that were not tested before by Machine Learning algorithms proven effective in malware detection.We carefully select an optimal number of features,do necessary preprocessing,and then apply Machine Learning algorithms to discover malicious code effectively.From our experiments,we have found that the Random Forest algorithm is the best-performing algorithm with such mobile malware with detection rates of around 99%.We compared our results from this work and found that they are aligned well with our previous work.We also compared our work with State-of-the-Art works of others and found that the results are very close and competitive.

A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

Monitoring the Heavy Element of Cr in Agricultural Soils Using a Mobile Laser-Induced Breakdown Spectroscopy System with Support Vector Machine

Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.

Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

Machine Learning Approach to Mobile Forensics Framework for Cyber Crime Detection in Nigeria

The mobile Cyber Crime detection is challenged by number of mobiledevices (internet of things), large and complex data, the size, the velocity,the nature and the complexity of the data and devices has become sohigh that data mining techniques are no more efficient since they cannothandle Big Data and internet of things. The aim of this research work wasto develop a mobile forensics framework for cybercrime detection usingmachine learning approach. It started when call was detected and thisdetection is made by machine learning algorithm furthermore intelligentmass media towers and satellite that was proposed in this work has theability to classified calls whether is a threat or not and send signal directlyto Nigerian communication commission (NCC) forensic lab for necessaryaction.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

Satisfaction of Patients Examined with Mobile X-Ray vs. X-Ray at the Hospital—A Randomized Controlled Trial

Background: In the Municipality of Aarhus, Denmark, mobile X-ray is offered to selected patients when a transfer to the Department of Radiology may be an obstacle. To our knowledge, no studies have examined patient’s satisfaction with mobile X-ray in a randomized controlled trial, but international qualitative and quantitative studies report a high level of patient satisfaction with mobile X-ray. Purpose: The purpose of the study was to investigate whether patients in aged care facilities who were offered mobile X-ray were more satisfied with the X-ray examination compared to patients examined with X-ray at the Department of Radiology, Aarhus University Hospital. Design: A part of a randomized controlled trial. Methods: Satisfaction was measured using a self-developed questionnaire, containing 13 questions measuring satisfaction from different perspectives. Participants: Due to patient’s fragility, healthcare staff members answered questions concerning satisfaction on behalf of the patients, who had been examined with mobile X-ray (n = 66) or X-ray at the hospital (n = 63). The patients were living in nursing homes and homes for the elderly in Aarhus Municipality. Data: Data were collected and stored using the computer program REDCap. Data were statistically analyzed using Fisher’s exact test. Results: Patients examined with mobile X-ray had a significantly higher satisfaction rate than those examined with X-ray at the hospital. Conclusion: Satisfaction of patients examined with X-ray was reported by healthcare staff to be in favor of mobile X-ray.

Design of intelligent controller for mobile robot based on fuzzy logic

In order to improve a mobile robot＇s autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot＇s autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot＇s navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine （FSM）, which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.

基于Mobile-VIT的旋转机械故障诊断方法

主要提出了一种基于Mobile-VIT神经网络技术的旋转机械故障的识别分析方法:首先,将旋转的机械构件在其各种连续运行工作状态条件下获得的高频振动信号通过短时的傅里叶变换方法转换为时频图像;然后,将图像输入到搭建好的Mobile-VIT网络模型中,通过其对时频图的识别以及特征提取实现旋转机械故障诊断。实验结果表明,提出的方法具有较高的故障识别准确率,能够有效识别旋转机械的运行状态。

基于多认证方式的MOBILE VPN系统

文章介绍了一个MOBILEVPN应用系统的认证框架及相应的技术特点。通过对现有移动通信网络的分析,以及针对这些特点提出一些与之相应的适合服务。同时结合其他相关技术构架设计出了一个具体的认证服务系统,为移动用户的访问时提出了多认证方法,并阐述了其实现过程和应用价值。最后提出本系统的服务易扩充性,以及在未来移动通信中的应用前景。

Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

Recently,the fifth generation(5G)of mobile networks has been deployed and various ranges of mobile services have been provided.The 5G mobile network supports improved mobile broadband,ultra-low latency and densely deployed massive devices.It allows multiple radio access technologies and interworks them for services.5G mobile systems employ traffic steering techniques to efficiently use multiple radio access technologies.However,conventional traffic steering techniques do not consider dynamic network conditions efficiently.In this paper,we propose a network aided traffic steering technique in 5G mobile network architecture.5G mobile systems monitor network conditions and learn with network data.Through a machine learning algorithm such as a feed-forward neural network,it recognizes dynamic network conditions and then performs traffic steering.The proposed scheme controls traffic for multiple radio access according to the ratio of measured throughput.Thus,it can be expected to improve traffic steering efficiency.The performance of the proposed traffic steering scheme is evaluated using extensive computer simulations.

CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.

Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.

Predicting End-User Adoption to Mobile Services

It is no doubt that the sub-field of Artificial Intelligence, which uses the tenets of Machine learning and data mining has progressively gained popularity in the past years to become one of fundamental yet revolutionary technologies. It is the basis of systems that can learn and improve using algorithms and big data with minimal programming or none. It is envisaged that mobile computing will empower end-users to seamlessly access and consume digital content services regardless of spatial or temporal orientations. Such are already the features of smart phones that at production are bundled with trending and necessary services. Of the many capabilities that advancement in technology have actualized in smart devices, gaming, video streaming, online library access, and m-commerce access services are the commonly among smart device owners. Given the near-exponential growth in ownership of smart devices, there is a need to identify and prioritize mobile services, and such was focus of this study. In specific, the study used Decision Tree, a popular machine learning algorithm, to predict the adoption of mobile services among smart device owners. Besides this purpose, the study identified the core uses of smart phones, and data used in the study was from an open source and was retrieved from Pew Research Centre Internet and Technology website. The dataset had 140 variables and 2001 themes, from which only the key attributes were selected for analysis. The study established that the level of education was the significant predictor of the mobile phones usage while race of the user emerged as the least predictor of smart device usage. The findings indicated that smart mobile phones were mostly used for entertainment, getting locations, direction and for recommendation purposes.

Mobile Memory Management System Based on User’s Application Usage Patterns

Currently,the number of functions to improve user convenience in smartphone applications is increasing.In addition,more mobile applications are being loaded into mobile operating system memory for faster launches,thus increasing the memory requirements for smartphones.The memory used by applications in mobile operating systems is managed using software;allocated memory is freed up by either considering the usage state of the application or terminating the least recently used(LRU)application.As LRU-based memory management schemes do not consider the application launch frequency in a low memory situation,currently used mobile operating systems can lead to the termination of a frequently executed application,thereby increasing its relaunch time.This study proposes a memory management system that can efficiently utilize the main memory space by analyzing the application usage information.The proposed system reduces the application launch time by leaving the most frequently used or likely to be run applications in the main memory for as long as possible.The performance evaluation conducted utilizing actual smartphone usage records showed that the proposed memory management system increases the number of times the applications resume from the main memory compared with the conventional memory management system,and that the average application execution time is reduced by approximately 17%.

MOBILE GEO-LOCATION ALGORITHM BASED ON LS-SVM

Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.

Establishing Efficient C2C E-alliance Based on Mobile Agent

In order to alleviate difficulties of conducting consumer-to-consumer(C2C)e-commerce transaction,establishing efficient e-alliance was proposed.E-alliance is the union of e-commerce sites.It is constructed by mobile agents.The mobile agent architecture was discussed.The process of selecting suitable e-commerce site to e-alliance was presented based on support vector machine(SVM)and fuzzy method.A prototype of the proposed system is implemented on a web platform.To enable data exchange between e-alliance and e-commerce,the system employs XML as data format.The prototype has demonstrated that the efficient C2C e-alliance is reasonable.

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Different types of pandemics that have appeared from time to time have changed many aspects of daily life.Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown.The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store.A huge number of reviews are written daily by users to express their opinions,which include significant information to improve these applications.The manual processing and extracting of information from users’reviews is an extremely difficult and time-consuming task.Therefore,the use of intelligent methods is necessary to analyse users’reviews and extract issues that can help in improving these apps.This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods.To the best of our knowledge,this is the first study to explore users’opinions about governmental apps in Saudi Arabia.The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases.A new dataset used for this research includes 8000 user reviews gathered from social media,Google Play and the app store.Different methods are applied to the dataset,and the results show that the k nearest neighbourhood(KNN)method generates the highest accuracy compared to other implemented methods.