Handle Research Articles

Securing future healthcare environments in a post-COVID-19 world: moving from frameworks to prototypes

The deployment of Internet of Things platforms as well as the use of mobile and wireless technologies to support healthcare environments have enormous potential to transform healthcare. This has also led to a desire to make eHealth and mHealth part of national healthcare systems. The COVID-19 pandemic has accelerated the requirement to do this to reduce the number of patients needing to attend hospitals and General Practitioner surgeries. This direction, however, has resulted in a renewed need to look at security of future healthcare platforms including information and data security as well as network and cyber-physical security. There have been security frameworks that were developed to address such issues. However, it is necessary to develop a security framework with a combination of security mechanisms that can be used to provide all the essential security requirements for healthcare systems. In addition, there is now a need to move from frameworks to prototypes which is the focus of this paper. Several security frameworks for eHealth and mHealth are first examined. This leads to a new reference model from which an implementation framework is developed using new mechanisms such as Capabilities, Secure Remote Procedure Calls, and a Service Management Framework. The prototype is then evaluated against practical security requirements.

LibDroid: Summarizing information flow of android native libraries via static analysis

With advancements in technology, people are taking advantage of mobile devices to access e-mails, search the web, and video chat. Therefore, extracting evidence from mobile phones is an important component of the investigation process. As Android app developers could leverage existing native libraries to implement a part of the program, evidentiary data are generated and stored by these native libraries. However, current state-of-art Android static analysis tools, such as FlowDroid (Arzt et al., 2014), Evihunter (Cheng et al., 2018), DroidSafe (Gordon et al., 2015) and CHEX (Lu et al., 2012) adopt the conservative approach for data-flow analysis on native method invocation. None of those tools have the capability to capture the data-flow within native libraries.In this work, we propose a new approach to conduct native data-flow analysis for security vetting of Android native libraries and build an analysis framework, called LibDroid to compute data-flow and summarize taint propagation for Android native libraries. The common question app users and developers often face is whether certain native libraries contain hidden functions or utilize user private information. LibDroid aims to answer this question. Therefore, we build a precise and efficient data-flow analysis with the support of SummarizeNativeMethod algorithm, and pre-compute an Android Native Libraries Database (ANLD) for 13,138 native libraries collected from 2,627 real-world Android applications. The ANLD includes the taint propagation summary of each native method and potential evidentiary data generated or stored within the native library. We evaluate LibDroid on 52 open-source native libraries and 2,627 real-world apps. Our results show that LibDroid can precisely summarize the information flow within the native libraries.

Algorithm 1025: PARyOpt: A Software for P arallel A synchronous R emote Ba y esian Opt imization

PARyOpt 1 is a Python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due to its low cost function footprint as well as the ability to account for uncertainties in data. A key challenge to efficiently deploy any optimization strategy on distributed computing systems is the synchronization step, where data from multiple function calls is assimilated to identify the next campaign of function calls. Bayesian optimization provides an elegant approach to overcome this issue via asynchronous updates. We formulate, develop and implement a parallel, asynchronous variant of Bayesian optimization. The framework is robust and resilient to external failures. We show how such asynchronous evaluations help reduce the total optimization wall clock time for a suite of test problems. Additionally, we show how the software design of the framework allows easy extension to response surface reconstruction (Kriging), providing a high performance software for autonomous exploration. The software is available on PyPI, with examples and documentation.

Variable Step Hybrid Block Method for the Approximation of Kepler Problem

In this article, a variable step size strategy is adopted in formulating a new variable step hybrid block method (VSHBM) for the solution of the Kepler problem, which is known to be a rigid and stiff differential equation. To derive the VSHBM, the step size ratio r is left the same, halved, or doubled in order to optimize the total number of steps, minimize the number of formulae stored in the code, and ensure that the method is zero-stable. The method is formulated by integrating the Lagrange polynomial with limits of integration selected at special points. The article further analyzed the stability, order, consistency, and convergence properties of the VSHBM. The stability regions of the VSHBM at different values of the step size ratios were also plotted and plots showed that the method is fit for solving the Kepler problem. The results generated were then compared with some existing methods, including the MATLAB inbuilt stiff solver (ode 15 s), with respect to total number of failure steps, total number of steps, total function calls, maximum error, and computation time.

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises.

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

DroidFDR: Automatic Classification of Android Malware Using Model Checking

Android faces an increasing threat of malware attacks. The few existing formal detection methods have drawbacks such as complex code modeling, incomplete and inaccurate expression of family properties, and excessive manual participation. To this end, this paper proposes a formal detection method, called DroidFDR, for Android malware classification based on communicating sequential processes (CSP). In this method, the APK file of an application is converted to an easy-to-analyze representation, namely Jimple, in order to model the code behavior with CSP. The process describing the behavior of a sample is inputted to an FDR model checker to be simplified and verified against a process that is automatically abstracted from the malware to express the property of a family. The sample is classified by detecting whether it has the typical behavior of any family property. DroidFDR can capture the behavioral characteristics of malicious code such as control flow, data flow, procedure calls, and API calls. The experimental results show that the automated method can characterize the behavior patterns of applications from the structure level, with a high family classification accuracy of 99.06% in comparison with another formal detection method.

Characterizing and synthesizing the workflow structure of microservices in ByteDance Cloud

AbstractModern Cloud applications have evolved from monolithic systems to numerous distributed microservices whose workflows interact via Remote Procedure Calls (RPCs). The benchmarks focusing on individual microservice components are insufficient because the reference relationships between components, which form the microservice workflow structure, are another critical aspect of microservice applications. Unfortunately, understanding the characteristics of microservice workflow in the production Cloud remains a missing piece in the literature, which prevents the representative of microservice benchmarks. In this paper, we fill this gap by characterizing and synthesizing the microservice workflows based on the trace data of the Toutiao application that is running on ByteDance Cloud. We examine the microservice workflows starting from DAG graphs, introduce observed properties that are easy to ignore but important, show the artificiality of the workflow by statistic description, and explore the high cost of network overhead. We further synthesize the workflow following the characteristics observed. The extensive evaluations show that the synthesized microservice workflows have consistent statistical characteristics as the production ones. A case study applying the synthesized workflows proves its usability.

An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network

JavaScript has to become a pervasive web technology that facilitates interactive and dynamic Web sites. The extensive usage and the properties permit the authors to simply obfuscate the code and make JavaScript an interesting place for hackers. JavaScript is usually used for adding functionalities and improving the usage of web applications. Despite several merits and usages of JavaScript, the major issue is that several recent cyber-attacks like drive-by-download attacks utilized the susceptibility of JavaScript codes. This paper devises a novel technique for detecting malicious JavaScript. Here, JavaScript codes are fed to the feature extraction phase for extracting the noteworthy features that include execution time, function calls, conditional statements, break statements, and Boolean. The extracted features are further subjected to data transformation wherein log transformation is adapted to normalize the data. Then, feature selection is performed using mutual information.

Fast Privileged Function Calls

Fast Privileged Function Calls

PRINCIPLES OF X-RAY TECHNOLOGY IN MEDICAL IMAGING AND IMPROVEMENT OF RADIOLOGICAL IMAGES

The X-ray tube is one of the most important components in any X-ray system. In the beginning, physicists and physicians used gas ion tubes. The so-called Coolidge tube applied a high vacuum and is still used today. Medical examinations have required continuously improved designs of X-ray tubes (smaller focal spots at a higher output). The principle of the Goetze line focus is still applied in any diagnostic X-ray tube. Different anode materials and the rotating anode contributed to an increased output and reduced exposure time. Bearings needed special attention. Spiral groove bearings are the most advanced design today. The heat storage capacity of the anode and the tube housing assembly influences examination time and patient throughput. Cardiac imaging required less motion blurring in cine film images and increasing radiation exposure in interventional procedures calling for measures to reduce dose. Protection against radiation and electric shock has always been a concern of design engineers. Focal spot sizes dedicated to specific applications and heat management within the total tube housing assembly will be future issues. Even in the event of ultrasound and MR technology, X-ray procedures will still be applied for diagnostic and interventional purposes.

IRePf: An Instruction Reorganization Virtual Platform for Kernel Stack Overflow Detection

Stack overflow vulnerabilities are among the most common security issues. However, the existing stack overflow detection solutions only protect the return address and ignore the imbalance between function calls and returns in the system, which will lead to a higher false-positive rate. In this paper, we propose an instruction reorganization virtual platform technique for kernel stack overflow detection, named IRePf. It can dynamically monitor the kernel stack when the system is running through dynamic reorganization instructions, thread creation and termination, call instructions, and RET instructions. IRePf uses backup stack creation and destruction technology to back up the return address and the address of the return address at the same time. IRePf determines whether the stack overflow occurs in the system when the function call and return are unbalanced to realize the kernel stack overflow detection. The experimental results show that IRePf can effectively detect stack overflow attacks, has low system resource occupancy and high real-time performance, and effectively improves the ability and security of defense stack attacks.

M2FaaS: Transparent and fault tolerant FaaSification of Node.js monolith code blocks

Porting existing monoliths to the Function-as-a-Service (FaaS) (FaaSification) can be very challenging for software developers due to different architectural styles. For a successful porting, developers need to resolve various dependencies, such as method invocations of external packages or user-defined codes, as well as global and local variables used in and after the code block that should be faasified. To bridge the gap and automatize FaaSification, this paper introduces M2FaaS, a FaaSifier that automatically converts a Node.js monolith into a hybrid by faasifying annotated code blocks as serverless functions on multiple FaaS providers. M2FaaS is a novel FaaSifier that resolves many challenges for the resulting monolith to work properly after the FaaSification. Developers may annotate all dependencies that need to be resolved for the generated functions to run properly and specify variables that should be returned by the function to the monolith because they are used later in the monolith. Moreover, M2FaaS is the first FaaSifier that faasifies arbitrary code blocks. The current M2FaaS prototype supports FaaSification of individual functions on two FaaS providers, AWS Lambda and IBM Cloud Functions. Finally, M2FaaS introduces an optional annotation for alternative functions to be invoked in case the primary faasified function fails. The resulting hybrid application invokes the automatically deployed serverless functions, while the original code remains executable. Experiments with four complementary monoliths demonstrate that M2FaaS outperforms state-of-the-art FaaSifiers in terms of development effort by up to 73.3%. Moreover, with the fault tolerance support, M2FaaS finishes all submitted functions, thereby achieving by 18.5% higher throughput than the other FaaSifiers.

On-Chip 3D Zn/NiOOH Helical Electrodes for High-Energy-Density Microbattery

Explosive progress in wireless and functional mobile electronics calls for miniaturized energy-storage units to push forward energy-autonomous and self-sustainable intelligent microsystems. Emerging three-dimensional (3D) microstructured electrodes for energy-storage devices have drawn great attention due to their attractive stereoscopic architectures to increase the areal loading of active materials. Here, we report a strategy for fabricating a 3D helical electrode for a zinc microbattery (Zn MB) by combining a “top-down” lithography technique and a “bottom-up” electrochemical depositing process. Thanks to the available stereoscopic space, the achieved 3D helical electrode shows a high surface area, outstanding electrolyte permeation, and stress adaptive capability. Based on the structural advantages of a 3D helical electrode, high-performance rechargeable Zn MB has been achieved by depositing Zn and NiOOH as anode and cathode materials, respectively. The Zn microbattery demonstrates a specific areal capacity up to 0.325 mAh·cm–2 corresponding to a high energy density of 0.55 mWh·cm–2. It also exhibits remarkable rate capability (0.272 mAh·cm–2 at 100 mA·cm–2) and excellent cycling stability (85% retention after 1000 cycles). The outstanding electrochemical performance indicates that the 3D Zn MB can work as a promising power source for advanced electronic devices. Paired micro-/nanofabrication process for battery electrodes is also of great significance in gearing toward carbon-neutral.

Boole's Strategy in Multistep Block Method for Volterra Integro-Differential Equation

This article presents a numerical approach for solving the second kind of Volterra integro- differential equation (VIDE). The multistep block-Boole's rule method will estimate the solutions for the linear and nonlinear problems of VIDE. The method computes two solutions for VIDE along the interval. The proposed method is developed by derivation of the Lagrange interpolating polynomial. The convergence and stability analysis of the derived method are discussed. From the perspective of total function calls and time-saving, the computation results explained that the derived method performs better than other existing methods.

Toward an Ideal Particle Swarm Optimizer for Multidimensional Functions

The Particle Swarm Optimization (PSO) method is a global optimization technique based on the gradual evolution of a population of solutions called particles. The method evolves the particles based on both the best position of each of them in the past and the best position of the whole. Due to its simplicity, the method has found application in many scientific areas, and for this reason, during the last few years, many modifications have been presented. This paper introduces three modifications to the method that aim to reduce the required number of function calls while maintaining the accuracy of the method in locating the global minimum. These modifications affect important components of the method, such as how fast the particles change or even how the method is terminated. The above modifications were tested on a number of known universal optimization problems from the relevant literature, and the results were compared with similar techniques.

Spectral Gradient Method with Log-determinant Norm for Solving Non-Linear System of Equations

Solving a system of non-linear equations has always been a complex issue whereby various methods were carried out. However, most of the methods used are optimization-based methods. This paper has modified the spectral gradient method with the backtracking line search technique to solve the non-linear systems. The efficiency of the modified spectral gradient method is tested by comparing the number of iterations, the number of function calls, and computational time with some existing methods. As a result, the proposed method shows better performance and gives more stable results than some existing methods. Moreover, it can be useful in solving some non-linear application problems. Therefore, the proposed method can be considered an alternative for solving non-linear systems.

Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study


 Background
 The purpose of this simulation study is to assess the performance of multiple imputation compared to complete case analysis when assumptions of missing data mechanisms are violated.
 Methods
 The authors performed a stochastic simulation study to assess the performance of Complete Case (CC) analysis and Multiple Imputation (MI) with different missing data mechanisms (missing completely at random (MCAR), at random (MAR), and not at random (MNAR)). The study focused on the point estimation of regression coefficients and standard errors.
 Results
 When data were MAR conditional on Y, CC analysis resulted in biased regression coefficients; they were all underestimated in our scenarios. In these scenarios, analysis after MI gave correct estimates. Yet, in case of MNAR MI yielded biased regression coefficients, while CC analysis performed well.Conclusion
 The authors demonstrated that MI was only superior to CC analysis in case of MCAR or MAR. In some scenarios CC may be superior over MI. Often it is not feasible to identify the reason why data in a given dataset are missing. Therefore, emphasis should be put on reporting the extent of missing values, the method used to address them, and the assumptions that were made about the mechanism that caused missing data.

Novel learning functions design based on the probability of improvement criterion and normalization techniques

Structural reliability analysis relies on the evaluation of multivariable structural performance function. The construction of the surrogate model for reliability analysis has been widely studied to reduce the number of function calls. The novel learning function has been proposed based on the probability of improvement criterion and the introduction of covariance weight by normalization technique to estimate the failure probability efficiently. Furthermore, the weights of each candidate sample are inconsistent; thus, an improved learning function of the former is proposed by introducing the joint probability density function of the variables. Moreover, a novel adaptive truncated sampling space technique is proposed based on variable normalization transformation. The truncated sampling space is directly dependent on the failure probability rather than the manual definitions. Uniform samples are then obtained in the adaptive truncated sampling space by Latin hypercube sampling. Combined with the novel learning function and global convergence condition, the adaptive analysis of structural reliability is realized. Finally, several highly nonlinear and high-dimensional illustrative applications are used to evaluate and discuss the proposed adaptive learning strategy. Results show that this strategy can efficiently and robustly solve the problem of structural reliability analysis on the premise of ensuring accuracy.

Analysing corpus of office documents for macro-based attacks using Machine Learning

Macro-based malware attacks are on the rise in recent cyber-attacks using malicious code written in visual basic code which can be used to target computers to achieve various exploitations. Macro malware can be obfuscated using various tools and easily evade antivirus software. To detect this macro malware, several methods of machine learning techniques have been proposed with an inadequate dataset for both benign and malicious macro codes which are not reproducible and evaluated on unbalanced datasets. In this paper, use of word embedding technique such as Word2Vec embedding is used for code analysis is proposed to analyze and process macro code written in visual basic language to understand and detect the attack vector before opening the documents. The proposed word embedding technique, called Obfuscated-Word2vec is proposed to detect obfuscated keywords, Obfuscated function names from the macro code and classify them as obfuscated or benign function calls which are later used as feature vectors to train models to extract the most relevant features from macro code and even to help the classifiers to detect more accurately as a downloader, dropper malware, shellcode, PowerShell exploits, etc. Experimental results show that proposed method is reproducible and could detect completely new macro malware by analyzing the macro code by the help of Random forest classifier with 82.65 percent accuracy.

AI intelligent wayfinding based on Unreal Engine 4 static map

The development of games is closely related to the development of game engines. Unreal Engine is a mainstream game development engine. Unreal Engine has a powerful lighting rendering function and blueprint programming system.Therefore, using Unreal Engine for game development will better meet the requirements of modern game users for game quality and game content. In games, the AI path-finding system is often the core of a game. However, the path-finding component of the unreal engine is usually only applies to three-dimensional game and is used for intelligent path-finding in dynamic environment. Howerve, the 2D game usually finds the way in static environment. At present, there are a lareg number of path-finding algorithms and theoretical studies based on static environment, but there is no simulation experiment and detailed explanation of intelligent path-finding in static environment based on the Unreal Engine platform. This article firstly explains the principle of path-finding algorithms that are several commonly used in static environment. Then it compares the performance of different algorithms, and selects the optimal solution among several schemes by summarizing the experimental results. Then based on the optimal path-finding algorithm, using blueprint programming to implement AI path-finding in the Unreal engine. Finally, this article sort out and summarize the overall work process and indicate direction for future optimization.