Flow Graph Research Articles

An analytical model for predicting the shielding effectiveness and resonances of a lossy enclosure with apertures

In this paper, an improved analytical model is presented to predict the shielding effectiveness (SE) and resonances of an apertured enclosure composed of the material of finite conductivity. In the model, the modified parameters of the rectangular waveguide are introduced to deal with the lossy enclosure with apertures, which can consider the effects of wall loss on the SE. Firstly, according to the circuit theory and electromagnetic topology (EMT) theory, the lossy enclosure is modeled as the circuit model and signal flow graph, respectively. Then, the electric field components at the monitor point can be obtained; hence, the SE can be predicted accurately. Finally, the presented model is utilized to analyze the effects of various parameters and conditions on the SE. The validity of the presented model is verified by the CST through several cases, showing that the lossy enclosure is feasible to damp all the resonant modes.

SANT: Static Analysis of Native Threads for Security Vetting of Android Applications

Most of the current research on static analysis of Android applications for security vetting either work on Java source code or the Dalvik bytecode. Nevertheless, Android allows developers to use C or C++ code in their programs that is compiled into various binary architectures. Moreover, Java and the native code components (C or C++) can collaborate with each other using Java Native Interface. Recent research shows that native codes are frequently used in both benign and malicious Android applications. Most of the present Android static analysis tools avert considering native codes in their analysis and applied trivial models for their data-flow analysis. As we know only the open source JN-SAF tool has tried to solve this issue statically. However, there are still challenges like libC functions and multi-threading in native codes that we want to address in this work. We presented SANT as an extension of JN-SAF for supporting Static Analysis of Native Threads. We considered modeling libC functions in our data-flow analysis to have a more precise analysis when dealing with security vetting of native codes. We also used control flow and data dependence graphs in SANT to handle multiple concurrent threads and find implicit data-flow between them. Our experiments show that the conducted improvements outperforms JN-SAF in real-world benchmark applications.

STOCHASTIC MODELING OF THE TRAINING DEVELOPMENT PROCESS AT THE ENTERPRISE

The article proposes a stochastic model of the training development process based on W-functions and the theory of closed flow graphs, which allows to take into account the probabilistic nature of the process, the possibility of various problems and risks, and to assess the probability of obtaining the desired result. The use of such modeling can increase the effectiveness of decision-making during the planning and implementation of training programs for employees.

Information Freshness Analysis of Slotted ALOHA in Gilbert-Elliot Channels

This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel. Considering a Gilbert-Elliot channel model, information freshness is evaluated through a penalty function that follows a power law of the time elapsed since the last received update, in contrast with the linear growth of age of information. By means of a signal flow graph analysis of Markov processes, we provide exact closed form expressions for the average penalty and for the peak penalty violation probability.

An Efficient 2.5D Shadow Detection Algorithm for Urban Planning and Design Using a Tensor Based Approach

Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are the main factor of defeatist virtual settings, they are expensive to render in real-time. This paper investigates how the amount of sunlight varies by season and how seasons can indicate the time of year to understand how shadows vary in length at different times of the day and how they change over the seasons. We propose a novel efficient (fast and scalable) algorithm to calculate a 2.5D cast-shadow map from a given LiDAR-derived Digital Surface Model (DSM). We present a proof-of-concept demonstration to examine the technical practicability of the introduced algorithm. Tensor-based techniques such as singular value decomposition, tensor unfolding are examined and deployed to represent the multidimensional data. The proposed method exploits horizon mapping ideas and extends the method to a modern graphics algorithm (Bresenham’s line drawing algorithm) to account for the DSM’s underlying surface geometry. A proof-of-concept is developed utilizing Python’s TensorFlow library, exploring data flow graphs and the tensor data structure. The heavy computer graphics algorithm used in this paper is parallelized using PySpark. Results explicate significant enhancements in overall performance while preserving accuracy at negligible variations.

Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.

Function-level obfuscation detection method based on Graph Convolutional Networks

The obfuscation detection technology is an important auxiliary means of malware detection. Also, for security practitioners, it can carry out automatic obfuscation detection before manual reverse analysis, which helps reverse engineers to perform reverse analysis more specifically. Existing obfuscation detection methods are mainly for Android applications and based on traditional machine learning, whose detection granularity is coarse, generality is poor, and the performance is not good enough. To address these issues, in this paper, we propose a function-level obfuscation detection method based on Graph Convolutional Networks for X86 assembly code and Android applications. Firstly, our method is function-level obfuscation detection, and we extract the Control Flow Graph (CFG) of each function as its feature, including the adjacency matrix and the basic block feature matrix. Secondly, we build a hybrid neural network model GCN-LSTM as our obfuscation detection model, which combines the Graph Convolutional Network (GCN) and the Long Short-Term Memory (LSTM). Finally, we conduct experiments using real-world open-source programs and compare results with baseline methods. For function-level detection, the accuracy of our method is 94.7575% for X86 assembly code and 98.9457% for Android applications, both of which are better than baseline methods. For APK-level detection, our method can almost completely detect the obfuscated APKs. Experimental results show that our method performs well for both X86 assembly code and Android applications and is superior to the baseline methods in both function-level detection and APK-level detection. Our research showcases a successful application of the Graph Convolutional Network and the Control Flow Graph on code obfuscation detection problems.

Exploring user requirements of network forensic tools

Network forensic tools enable security professionals to monitor network performance and compromises. These tools are used to monitor internal and external network attacks. Technological improvements have enabled criminals to wipe out tracks of cybercrime to elude alterations. Network forensics procedures use processes to expedite investigation by tracking each original packet and event that is generated in the network. There are many network forensic tools, both open source and commercial versions available in the market. In this work, the result of a survey participated by different experts in open source network forensic tools have been presented. The advantages, challenges, and necessities have been identified for network forensic investigation of such tools. A few open source network forensic tools have been studied and performed a comparative analysis based on six key parameters. Further, two malware datasets are analyzed using open source tools to perform investigation and present a comprehensive network forensic analysis comprising IO graphs, Flow graphs, TCP stream, UDP multicast stream, mac-based analysis, and operating system analysis.

Hierarchical Attention Graph Embedding Networks for Binary Code Similarity against Compilation Diversity

Binary code similarity comparison is the technique that determines if two functions are similar by only considering their compiled form, which has many applications, including clone detection, malware classification, and vulnerability discovery. However, it is challenging to design a robust code similarity comparison engine since different compilation settings that make logically similar assembly functions appear to be very different. Moreover, existing approaches suffer from high-performance overheads, lower robustness, or poor scalability. In this paper, a novel solution HBinSim is proposed by employing the multiview features of the function to address these challenges. It first extracts the syntactic and semantic features of each basic block by static analysis. HBinSim further analyzes the function and constructs a syntactic attribute control flow graph and a semantic attribute control flow graph for each function. Then, a hierarchical attention graph embedding network is designed for graph-structured data processing. The network model has a hierarchical structure that mirrors the hierarchical structure of the function. It has three levels of attention mechanisms applied at the instruction, basic block, and function level, enabling it to attend differentially to more and less critical content when constructing the function representation. We conduct extensive experiments to evaluate its effectiveness and efficiency. The results show that our tool outperforms the state-of-the-art binary code similarity comparison tools by a large margin against compilation diversity clone searching. A real-world vulnerabilities search case further demonstrates the usefulness of our system.

DFlow: A Data Flow Analysis Tool for C/C++

AbstractAbstract syntax trees (ASTs), control flow graphs (CFGs), and data flow analysis (DFA) are prerequisites for static and dynamic analysis and vulnerability detection for programs; thus, obtaining them is significant. Recently, many tools related to generating ASTs, CFGs, and DFA have been proposed. However, most tools can only construct ASTs, very few can construct ASTs and CFGs, and almost none can construct all three. The vast majority of AST, CFG, and DFA tools are for other languages (e.g., Java and Python), and while a few are for C/C++, they are implemented in other languages, creating complex working environments, and overreliance on other language‐related libraries. To address these shortcomings, we present a DFA tool, DFlow, for C/C++. First, a lexical/grammatical analyzer generated by Flex and Bison is used to analyze the program. Second, an AST is constructed from the results; then, a CFG is obtained from the analysis results and the information from the AST. Finally, based on the AST and CFG, DFA is performed, and the vulnerabilities of simple programs are determined. We test some common vulnerable code and common weakness enumeration slicing code, which show the effectiveness of DFlow in program data flow analysis and vulnerability checking. The results show that our tool can implement ASTs, CFGs, and DFA, and we add some rules to the tool for vulnerability detection. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Fast Algorithm to Solve Delay Vandermonde Systems in Phased-Array Digital Receivers

Phased-array multibeam RF beamformers require calibration of receivers used in an array of elements before the signals can be applied to an analog beamforming network. This article presents a fast <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) algorithm for solving linear systems having a delay Vandermonde matrix (DVM) for the analog beamforming matrix. The structure of the DVM enables to obtain a fast algorithm to efficiently reverse multibeams at the DVM output such that calibration of the input low noise amplifiers can be achieved. The arithmetic complexity of the proposed DVM system solving algorithm is preferable over the standard matrix inversion consuming <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations. Numerical experiments are presented for forward accuracy of the proposed algorithm computed at the calibration frequency. Moreover, numerical results are shown to compare the order of the arithmetic complexity and the execution time of the proposed algorithm. Signal flow graphs are presented for four- and eight-element multibeam phased arrays.

A Theoretical Model for Global Optimization of Parallel Algorithms

With the quickly evolving hardware landscape of high-performance computing (HPC) and its increasing specialization, the implementation of efficient software applications becomes more challenging. This is especially prevalent for domain scientists and may hinder the advances in large-scale simulation software. One idea to overcome these challenges is through software abstraction. We present a parallel algorithm model that allows for global optimization of their synchronization and dataflow and optimal mapping to complex and heterogeneous architectures. The presented model strictly separates the structure of an algorithm from its executed functions. It utilizes a hierarchical decomposition of parallel design patterns as well-established building blocks for algorithmic structures and captures them in an abstract pattern tree (APT). A data-centric flow graph is constructed based on the APT, which acts as an intermediate representation for rich and automated structural transformations. We demonstrate the applicability of this model to three representative algorithms and show runtime speedups between 1.83 and 2.45 on a typical heterogeneous CPU/GPU architecture.

Analysis of Liquid Crystal Tunable Thin-Film Optical Filters Using Signal Flow Graph Technique

In this paper, a liquid crystal tunable thin-film optical bandpass filter is studied and analyzed using the signal flow graph technique. This paper investigates an exact form for calculating the transmission coefficients, reflection coefficients, and the transmission intensity of the filter. The simulation results show the filter performance and the channel shape profile. In addition, the results show the tuning capability of the filter. The signal flow graph technique provides an attractive method for analyzing the thin-film optical filters since it overcomes the difficulty of the refractive index concept in extending to optical applications. Moreover, it simplifies the filter analysis and design process.

Towards parallelism detection of sequential programs with graph neural network

Development of the parallel processing technology is necessary to solve problems created by programs with complex structures that are computation- and data-intensive. In the parallelization process, the detection of parallelism is an important task. Automatic parallelism analysis tools help programmers in finding parallelism. However, these tools have limitations in analyzing complex programs. Herein, we propose a data-driven method that can be applied to parallelism detection. The proposed framework combines contextual flow graphs and a deep graph convolution neural network, which leverages the latest and most popular techniques for code embedding and graph classification. Further, we present a novel generator to solve the problem of existing dataset inadequacies. We use this generator to build a generic dataset for parallelism detection. The experimental results of our framework using the generated dataset demonstrate that using a graph representation of the code can capture domain-specific information, and our framework can accurately detect potential parallelism in sequential programs. This framework will enable the exploration of new tools to address the parallelism detection problem.

Verifiable Model Construction for Business Processes

Business process specified in Business Process Execution Language (BPEL), which integrates existing services to develop composite service for offering more complicated function, is error-prone. Verification and testing are necessary to ensure the correctness of business processes. SPIN, for which the input language is PROcess MEta-LAnguage (Promela), is one of the most popular tools for detecting software defects and can be used both in verification and testing. In this paper, an automatic approach is proposed to construct the verifiable model for BPEL-based business process with Promela language. Business process is translated to an intermediate two-level representation, in which eXtended Control Flow Graph (XCFG) describes the behavior of BPEL process in the first level and Web Service Description Models (WSDM) depict the interface information of composite service and partner services in the second level. With XCFG of BPEL process, XCFGs for partner services are generated to describe their behavior. Promela model is constructed by defining data types based on WSDM and defining channels, variables and processes based on XCFGs. The constructed Promela model is closed, containing not only the BPEL process but also its execution environment. Case study shows that the proposed approach is effective.

On the computational complexity of Data Flow Analysis over finite bounded meet semilattices

The problem of computing the meet over all paths (MOP) solution in a monotone data flow framework over an infinite meet semilattice is generally undecidable [1]. Hence, the maximum fixed point (MFP) solution, which is polynomial time computable on semi-lattices of finite height, is generally used in practice for program analysis questions in monotone data flow frameworks. However, we show that if the semi-lattice is finite, computing MOP solution is NL-complete with respect to log space reductions, which implies parallelizability and polynomial time computability. It is also shown that the problem of computing the maximum fixed point (MFP) solution is P-complete with respect to log space reductions, and hence not efficiently parallelizable, even when the flow graph is directed acyclic and the semilattice has just four elements. These results appear in contrast with the fact that when the semilattice is not finite, solving the MOP problem is significantly harder than MFP.

Deriving structures of FFT based filter banks by signal flow graphs

The design of filter banks, which is a frequently used operator for many practical applications, has been widely reported in the literature. The requirements of using extensive mathematical manipulations for finding computationally efficient structures of filter banks have imposed severe difficulties for those who lack sufficient mathematical background. This paper is to report procedures of deriving computationally efficient filter bank structures by using a few graphic transformations of equivalent multi-rate operations. It is seen that these procedures used in the derivation process are elegantly simple and straight forwards in concept. In particular, these procedures can be flexibly used to find the computationally efficient structures of both maximally decimated (p=1) and non-maximally decimated (p>1) filter banks, where p is the oversampling factor. For the non-maximally decimated filter banks, the computational complexity needed by our derived structure is less than that needed by the structures reported in the literature.

Thermal Analysis of a Solar Concentrating Collector with Nonuniform Flux Distribution.(Dept.M)

This paper presents a new approach to the thermal analysis of a solar concentrator. The Signal Flow Graph technique is applied to solve the heal Transfer problem in a flat receiver subjected to nonuniform flux distribution. Signal Flow Graph allows in a more easy graphical form to express the essential properties of the analyzed system, and directly obtain any parameter from the graph structural properties. With this method, there is no need of assuming uniform flux distribution over the whole receiver surface as in Hottel-Whillier-Woertz-Bliss approach, A Computer program has been established to obtain the thermal performance under different design and operating conditions using both approaches. In order to verify the theoretical results, an experimental concentrator was built with, one glass cover, flat receiver of 25 cm width and 94 cm length, The receiver is insulated with form plates of 3 cm thickness and fitted with two headers to supply the fresh water uniformly in the length direction. The reflecting surface is formed of unequal mirror segments which are arranged to concentrate most of the falling solar radiation on the upper third of the receiver surface. The set-up is suitably instrumented to measure the temperature at different points and the water mass flow rate through the receiver. The total and beam solar radiation intensities as well as the ambient temperature are also measured during the experimental work. Comparison between the predicted and measured results have shown that the thermal performance as calculated by the Signal Flow Graph is more fitted with the experimental results than that obtained by the HWWB approach. Therefore, the Signal Flow Graph approach is seen to be an easy and precise technique for the thermal analysis of concentrating collectors with nonuniform flux distribution on the receiver surface.

Bialgebraic foundations for the operational semantics of string diagrams

Turi and Plotkin's bialgebraic semantics is an abstract approach to specifying the operational semantics of a system, by means of a distributive law between its syntax (encoded as a monad) and its dynamics (an endofunctor). This setup is instrumental in showing that a semantic specification (a coalgebra) is compositional.In this work, we use the bialgebraic approach to derive well-behaved structural operational semantics of string diagrams, a graphical syntax that is increasingly used in the study of interacting systems across different disciplines. Our analysis relies on representing the two-dimensional operations underlying string diagrams in various categories as a monad, and their semantics as a distributive law for that monad.As a proof of concept, we provide bialgebraic semantics for a versatile string diagrammatic language which has been used to model both signal flow graphs (control theory) and Petri nets (concurrency theory).

A Detection Approach for Vulnerability Exploiter Based on the Features of the Exploiter

With the wide application of software system, software vulnerability has become a major risk in computer security. The on-time detection and proper repair for possible software vulnerabilities are of great importance in maintaining system security and decreasing system crashes. The Control Flow Integrity (CFI) can be used to detect the exploit by some researchers. In this paper, we propose an improved Control Flow Graph with Jump (JCFG) based on CFI and develop a novel Vulnerability Exploit Detection Method based on JCFG (JCFG-VEDM). The detection method of the exploit program is realized based on the analysis results of the exploit program. Then the JCFG is addressed through combining the features of the exploit program and the jump instruction. Finally, we implement JCFG-VEDM and conduct the experiments to verify the effectiveness of the proposed method. The experimental results show that the proposed detection method (JCFG-VEDM) is feasible and effective.