Data Flow Analysis Research Articles

Equational Systems on Posets and Their Applications

In this paper, we introduce the notion of nonlinear equational systems. We consider some important results of equational systems on posets (partially ordered sets) and Banach lattices. Also we see their applications in data-flow analysis—a general theory to solve some special types of simultaneous nonlinear equations on complete lattices—and extend the Cournot oligopoly on Banach lattices. At the end, there are some new theoretical suggestions for decision makers, especially computer program designers, to shorten and optimize their calculations.

Extending OpenMP for the Optimization of Parallel Component Applications

Motivated by the promotion of development efficiency and performance, we research on the common performance optimization technology for parallel component applications. We define OpenMP pragmas to describe the parallel component instance calls in three types of complex structure code. By data flow analysis, we can find the parallelism potential of these codes. We extends the OpenMP pragma to support virtual computing resources and the deployment of complex structure codes on them. We provide three reference rules for the component instance tasks to be scheduled on certain resources to get better performance. To better utilize resources, we provide mapping of virtual computing resources to real resources. We extends the OpenMP programming model and execution model to accommodate the extended pragmas. The extended Babel compiler is responsible for data flow analysis. The extended CCAFFEINE framework is in charge of generating the task schedule policy. Experiments show that our performance optimization platform Bomp can get better scalability and performance than other methods existed.

A consistency-guaranteed approach for Internet of Things software refactoring

The software architecture of Internet of Things defines the component model and interconnection topology of Internet of Things systems. Refactoring is a systematic practice of improving a software structure without altering its external behaviors. When the Internet of Things software is refactored, it is necessary to detect the correctness of Internet of Things software to ensure its security. To this end, this article proposes a novel refactoring correction detection approach to ensure software security. Control flow analysis and data flow analysis are used to detect code changes before and after refactoring, and synchronization dependency analysis is used to detect changes in synchronization dependency. Three detection algorithms are designed to detect refactoring correctness. Four real-world benchmark applications are used to evaluate our approach. The experimental results show that our proposed approach can ensure correctness of Internet of Things software refactoring.

АНАЛІЗ ЧАСОВИХ ПОСЛІДОВНИХ ПОТОКІВ ДАНИХ МЕРЕЖЕВОГО ТРАФІКУ НА ОСНОВІ ВЕЙВЛЕТ-ПЕРЕТВОРЕННЯ

В даний час для вивчення властивостей в мережевих системах і їх процесів широко застосовується підходи, засновані на аналізі їх вихідних сигналів. Тому аналіз систем і процесів, особливо при експериментальних дослідженнях, часто реалізується за допомогою обробки реєстрованих сигналів. Майже в кожній предметній області існують явища, які необхідно вивчати в їх динаміці, а сукупність реєстрованих сигналів подібного роду за певний період часу і є часові послідовності потоків даних. Для аналізу часових послідовностей, які є стаціонарними або нестаціонарними випадковими процесами, використовують традиційні методи статистичного аналізу випадкових величин і функцій. Найбільш поширеними з них є кореляційний і спектральний аналізи, згладжування і фільтрація даних, моделі авторегресії і прогнозування. Поряд з традиційними методами, в останні роки набувають поширення способи обробки сигналів, засновані на вейвлет-перетворенні. Особливість цієї технології в тому, що вона дозволяє розкрити особливості локальної структури складного сигналу і виявити різні його властивості, невидимі в режимі реального часу. В області вейвлет-перетворення виділяється додаткова інформація за допомогою подання до частотно-часового зображення сигналу, недоступного в початковому вигляді. На сьогоднішній момент часу посилюються вимоги до якіснішого виявлення внутрішніх закономірностей в поведінці часових послідовностей і прогнозом періодів стійкості досліджуваних процесів. Тому виникає необхідність в розробці нових і модифікації існуючих алгоритмів аналізу часових послідовностей в мережевих системах. У даній роботі досліджено застосування вейвлет-перетворення для виявлення вторгнень в комп’ютерні мережі. Пропонується аналіз останніх досліджень по даній задачі, де розглянуті вже існуючі алгоритми та методи виявлення атак за допомогою вейвлет-перетворення. Важливим пунктом у цій роботі є обґрунтування застосування вейвлет–функції та алгоритму вейвлет-перетворення для аналізу часових послідовних потоків даних мережевого трафіку. З використанням вейвлет-функції пропонується усунення шуму з мережевого трафіку та з використанням пакетного вейвлет-перетворення для аналізу мережевого трафіку і отримання інформації про можливі атаки. Використання вейвлет-функції має важливий характер, бо вибір оптимального вейвлет-базису дозволить підняти ймовірність виявлення як на початковому етапі, так і при реконструкції сигналу.

Lightweight and Efficient Hypervisor-Based Dynamic Binary Instrumentation and Analysis Method

At present, various vulnerabilities and malicious programs are still constantly threatening the system security, and in-depth analysis of legitimate applications and malicious code is an important link of security defense under the current security situation. Dynamic binary analysis is the important method in the field of binary analysis, and after years of research and development, many valuable results have been achieved, and some mature tools generated have been widely used in practice. But it still faces multiple challenges in terms of analysis efficiency, deployment, and the impact on the target program. Based on the existing research, this article proposes a new lightweight hypervisor-based dynamic binary instrumentation method, which uses the virtualization features of new processors to perform transparent and efficient execution interception of the target program, so that it can instrument the target program and redirect the original execution. To take full advantage of the interception solution, we further present a compact inline dynamic taint analysis method that enables fine-grained data flow analysis of the target program with multiple processes and kernel modules. Experiments in the real environment show that the proposed method is effective and efficient in the binary instrumentation and analysis, it can achieve a good balance in analysis performance, availability and stealthiness, and can be applied to the practical analysis scenarios.

Soundness of a Dataflow Analysis for Memory Monitoring

An important concern addressed by runtime verification tools for C code is related to detecting memory errors. It requires to monitor some properties of memory locations (e.g., their validity and initialization) along the whole program execution. Static analysis based optimizations have been shown to significantly improve the performances of such tools by reducing the monitoring of irrelevant locations. However, soundness of the verdict of the whole tool strongly depends on the soundness of the underlying static analysis technique. This paper tackles this issue for the dataflow analysis used to optimize the E-ACSL runtime assertion checking tool.We formally define the core dataflow analysis used by E-ACSL and prove its soundness.

What does the software requirement specification for local E-Government of citizen database information system? An analysis using ISO/IEC/IEEE 29148 – 2011

The management citizen database is Improper and not up-to-date will results decisions that are not strategic for the village development process. Inappropriate citizen data also results in long-term ineffective local government planning processes. Such as the problem of forecasting or predicting the level of growth of citizens as well as the need for development services which are part of the medium-term long-term plans of local governments. Therefore they need requires a citizen database information system. In connection with the needs of citizen database information systems, this study provides a solution of analysing documents, namely the requirement specification software for citizen database information systems using the ISO / IEC / IEEE 29148 edition of 2011. The study was conducted with case studies of 6781 management data of residents of the village of Sumbersekar, Malang Regency, East Java. The method to execute the research consisted of data gathering stages, data flow and decision analysis, and the last is create software requirement specification. the specific requirements of the SRS documents ISO / IEC / IEEE 29148 2011 edition are external interfaces, functions, usability requirements, performance requirements, logical database requirements, design constraints, software system attributes, and supporting information.

FlowSpec: A declarative specification language for intra-procedural flow-Sensitive data-flow analysis

Abstract Data-flow analysis is the static analysis of programs to estimate their approximate run-time behavior or approximate intermediate run-time values. It is an integral part of modern language specifications and compilers. In the specification of static semantics of programming languages, the concept of data-flow allows the description of well-formedness such as definite assignment of a local variable before its first use. In the implementation of compiler back-ends, data-flow analyses inform optimizations. Data-flow analysis has an established theoretical foundation. What lags behind is implementations of data-flow analysis in compilers, which are usually ad-hoc. This makes such implementations difficult to extend and maintain. In previous work researchers have proposed higher-level formalisms suitable for whole-program analysis in a separate tool, incremental analysis within editors, or bound to a specific intermediate representation. In this paper, we present FlowSpec , an executable formalism for specification of data-flow analysis. FlowSpec is a domain-specific language that enables direct and concise specification of data-flow analysis for programming languages, designed to express flow-sensitive, intra-procedural analyses. We define the formal semantics of FlowSpec in terms of monotone frameworks. We describe the design of FlowSpec using examples of standard analyses. We also include a description of our implementation of FlowSpec . In a case study we evaluate FlowSpec with the static analyses for Green-Marl , a domain-specific programming language for graph analytics.

TAP: A static analysis model for PHP vulnerabilities based on token and deep learning technology.

With the widespread usage of Web applications, the security issues of source code are increasing. The exposed vulnerabilities seriously endanger the interests of service providers and customers. There are some models for solving this problem. However, most of them rely on complex graphs generated from source code or regex patterns based on expert experience. In this paper, TAP, which is based on token mechanism and deep learning technology, was proposed as an analysis model to discover the vulnerabilities of PHP: Hypertext Preprocessor (PHP) Web programs conveniently and easily. Based on the token mechanism of PHP language, a custom tokenizer was designed, and it unifies tokens, supports some features of PHP and optimizes the parsing. Besides, the tokenizer also implements parameter iteration to achieve data flow analysis. On the Software Assurance Reference Dataset(SARD) and SQLI-LABS dataset, we trained the deep learning model of TAP by combining the word2vec model with Long Short-Term Memory (LSTM) network algorithm. According to the experiment on the dataset of CWE-89, TAP not only achieves the 0.9941 Area Under the Curve(AUC), which is better than other models, but also achieves the highest accuracy: 0.9787. Further, compared with RIPS, TAP shows much better in multiclass classification with 0.8319 Kappa and 0.0840 hamming distance.

Faster Algorithms for Dynamic Algebraic Queries in Basic RSMs with Constant Treewidth

Interprocedural analysis is at the heart of numerous applications in programming languages, such as alias analysis, constant propagation, and so on. Recursive state machines (RSMs) are standard models for interprocedural analysis. We consider a general framework with RSMs where the transitions are labeled from a semiring and path properties are algebraic with semiring operations. RSMs with algebraic path properties can model interprocedural dataflow analysis problems, the shortest path problem, the most probable path problem, and so on. The traditional algorithms for interprocedural analysis focus on path properties where the starting point is fixed as the entry point of a specific method. In this work, we consider possible multiple queries as required in many applications such as in alias analysis. The study of multiple queries allows us to bring in an important algorithmic distinction between the resource usage of the one-time preprocessing vs for each individual query. The second aspect we consider is that the control flow graphs for most programs have constant treewidth. Our main contributions are simple and implementable algorithms that support multiple queries for algebraic path properties for RSMs that have constant treewidth. Our theoretical results show that our algorithms have small additional one-time preprocessing but can answer subsequent queries significantly faster as compared to the current algorithmic solutions for interprocedural dataflow analysis. We have also implemented our algorithms and evaluated their performance for performing on-demand interprocedural dataflow analysis on various domains, such as for live variable analysis and reaching definitions, on a standard benchmark set. Our experimental results align with our theoretical statements and show that after a lightweight preprocessing, on-demand queries are answered much faster than the standard existing algorithmic approaches.

SoProtector: Safeguard Privacy for Native SO Files in Evolving Mobile IoT Applications

Android Apps have become the most important mobile applications in the evolving mobile IoT systems, whose security and privacy are confronted with ever more challenges, since such mobile devices as smartphones involve too much personal privacy information. Meanwhile, the developers prefer to put core functions (e.g., encryption function and T9 search function) of Android applications in the native layer for execution efficiency. However, there are no automated security analysis tools to protect the security and privacy of the Android native layer, especially for those dynamically loaded third-party SO libraries. In order to solve the previous problem, which is confusing, we propose a novel and scalable system, called SoProtector, to prevent privacy from leaking via the analysis of data flow between the Java and native layers. For detection of the malicious function implanted in the SO libraries, SoProtector realizes a real-time engine. We derive the malware features via three steps: 1) present binary files in native family as a grayscale image; 2) with use of the ARM instructions set reversely obtain the code of the SO file and using Python to obtain the opcode sequence; and 3) each file is transformed as the form of assembly language by IDA Pro, which includes a gdl file as an accompaniment. Our experiment, which involved 3400 applications, demonstrates that SoProtector is able to detect more sinks, sources, and smudges. It effectively inspects and blocks at least 82% of the applications that are loading malicious third-party SO dynamically, and it has relatively low overhead in the meantime, compared to most of the existing static analysis tools (e.g., FlowDroid and AndroidLeaks).

Static analysis with demand-driven value refinement

Static analysis tools for JavaScript must strike a delicate balance, achieving the level of precision required by the most complex features of target programs without incurring prohibitively high analysis time. For example, reasoning about dynamic property accesses sometimes requires precise relational information connecting the object, the dynamically-computed property name, and the property value. Even a minor precision loss at such critical program locations can result in a proliferation of spurious dataflow that renders the analysis results useless. We present a technique by which a conventional non-relational static dataflow analysis can be combined soundly with a value refinement mechanism to increase precision on demand at critical locations. Crucially, our technique is able to incorporate relational information from the value refinement mechanism into the non-relational domain of the dataflow analysis. We demonstrate the feasibility of this approach by extending an existing JavaScript static analysis with a demand-driven value refinement mechanism that relies on backwards abstract interpretation. Our evaluation finds that precise analysis of widely used JavaScript utility libraries depends heavily on the precision at a small number of critical locations that can be identified heuristically, and that backwards abstract interpretation is an effective mechanism to provide that precision on demand.

Optimization of swift protocols

Swift, an increasingly-popular programming language, advocates the use of protocols, which define a set of required methods and properties for conforming types. Protocols are commonly used in Swift programs for abstracting away implementation details; e.g., in a large industrial app from Uber, they are heavily used to enable mock objects for unit testing. Unfortunately, heavy use of protocols can result in significant performance overhead. Beyond the dynamic dispatch often associated with such a feature, Swift allows for both value and reference types to conform to a protocol, leading to significant boxing and unboxing overheads. In this paper, we describe three new optimizations and transformations to reduce the overhead of Swift protocols. Within a procedure, we define LocalVar, a dataflow analysis and transformation to remove both dynamic dispatch and boxing overheads. We also describe Param, which optimizes the case of protocol-typed method parameters using specialization. Finally, we describe SoleType, a transformation that injects casts when a global analysis (like type-hierarchy analysis) discovers some protocol variable must have some concrete type. We also describe how these optimizations work fruitfully together and with existing Swift optimizations to deliver further speedups. We perform elaborate experimentation and demonstrate that our optimizations deliver an average 1.56x speedup on a suite of Swift benchmarks that use protocols. Further, we applied the optimizations to a production iOS Swift application from Uber used by millions of customers daily. For a set of performance spans defined by the developers of the application, the optimized version showed speedups ranging from 6.9% to 55.49%. A version of our optimizations has been accepted as part of the official Swift compiler distribution.

The Weakness Examination of Wireless Network Security at the Hospital Using QoS

Computer networks today are a much needed service. Computer networks enable the shared use of data, software and tools. So that the working group can be communicate more effectively and efficiently. This study aims to determine the existing QoS in Surya Asih Hospital and analyze the deficiencies. (Bahasa: Hospital is Rumah Sakit (RS)) In this research started from collecting data that exist in location either that support hardware or connection internet RS Surya Asih have used Wireless Local Area Network (WLAN) network service. WLAN is a network where can be exchanged data and resources between computers in one building. To know the performance of WLAN network (Wireless Local Area Network In RS Surya Asih, it should be done a measurement analysis of network performance parameters QoS (Quality of Services) is the ability to guarantee the delivery of important data flow Analysis of the performance of WLAN (Wireless Local Area Network) Surya Asih stressed the process of monitoring and measuring network parameters on the network infrastructure such as access speed and transmission capacity, from the point of delivery to the point of receiver that became the demands, the parameters used Bandwidth, Throughput, Delay and Packet Loss. Superior LAN on RS Surya Asih has reliability sufficient maintenance and good network availability Analysis of network performance is seen through Wireshark, Uptime and Downtime results are not bad, The availability of complete devices and fulfilled according to reliability and maintenance is not difficult. QoS parameter test results that bandwidth management shows the use of more bandwidth and evenly distributed to every user of RS Surya Asih Pringsewu network.

DroidRista: a highly precise static data flow analysis framework for android applications

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

Communication-based attacks detection in android applications

The Android operating system provides a rich Inter-Component Communication (ICC) method that brings enormous convenience. However, the Android ICC also increases security risks. To address this problem, a formal method is proposed to model and detect inter-component communication behavior in Android applications. Firstly, we generate data flow graphs and data facts for each component through component-level data flow analysis. Secondly, our approach treats ICC just like method calls. After analyzing the fields and data dependencies of the intent, we identify the ICC caller and callee, track the data flow between them, and construct the ICC model. Thirdly, the behavior model of Android applications is constructed by a formal mapping method for component data flow graph based on Pi calculus. The runtime sensitive path trigger detection algorithm is then given. Communication-based attacks are detected by analyzing intent abnormity. Finally, we analyze the modeling and detection efficiency, and compare it with relevant methods. Analysis of 57 real-world applications partly verifies the effectiveness of the proposed method.

A Transformational Approach to Resource Analysis with Typed-norms Inference

AbstractIn order to automatically infer the resource consumption of programs, analyzers track how data sizes change along program’s execution. Typically, analyzers measure the sizes of data by applying norms which are mappings from data to natural numbers that represent the sizes of the corresponding data. When norms are defined by taking type information into account, they are named typed-norms. This article presents a transformational approach to resource analysis with typed-norms that are inferred by a data-flow analysis. The analysis is based on a transformation of the program into an intermediate abstract program in which each variable is abstracted with respect to all considered norms which are valid for its type. We also present the data-flow analysis to automatically infer the required, useful, typed-norms from programs. Our analysis is formalized on a simple rule-based representation to which programs written in different programming paradigms (e.g., functional, logic, and imperative) can be automatically translated. Experimental results on standard benchmarks used by other type-based analyzers show that our approach is both efficient and accurate in practice.

Generative API usage code recommendation with parameter concretization

Many programming languages and development frameworks have extensive libraries (e.g., JDK and Android libraries) that ease the task of software engineering if used effectively.With numerous library classes and sometimes intricate API (application programming interface) usage constraints, programmers often have difficulty remembering the library APIs and/or using them correctly.This study addresses this problem by developing an engine called DeepAPIRec, which automatically recommends the API usage code. Compared to the existing proposals, our approach distinguishes itself in two ways. First, it is based on a tree-based long short-term memory (LSTM) neural network inspired by recent developments in the machine-learning community. A tree-based LSTM neural network allows us to model and reason about variable-length, preceding and succeeding code contexts, and to make precise predictions. Second, we apply data-flow analysis to generate concrete parameters for the API usage code, which not only allows us to generate complete code recommendations but also improves the accuracy of the learning results according to the tree-based LSTM neural network. Our approach has been implemented for supporting Java programs. Our experimental studies on the JDK library show that at statement-level recommendations, DeepAPIRec can achieve a top-1 accuracy of about 37% and a top-5 accuracy of about 64%, which are significantly better than the existing approaches.Our user study further confirms that DeepAPIRec can help developers to complete a segment of code faster and more accurately as compared to IntelliJ IDEA.

DATA CONTROL IN THE DIAGNOSTICS AND FORECASTING THE STATE OF COMPLEX TECHNICAL SYSTEMS

The analysis of management methods Big Data is carried out. In order to obtain timely results of analyzing the state of complex technical systems on the basis of the list of parameters established by regulatory documentation that are ofparamount (for critical components) and minor importance in diagnosing the state of components ensuring the operation of complex technical systems, it is necessary to develop a method for managing data with high speed and losslessly separate and transfer Big Data from IIS to relational and non-relational databases. A method is proposed that ensures the distribution of data coming from information-measuring systems to relational and non-relational databases in diagnosing and predicting the state of complex technical systems. The expediency of using the concept of Data Mining in SCADA systems to control Big Data is substantiated. Algorithms for transmission, distribution and analysis of data in an information-measuring system for diagnosing and predicting the state of complex technical systems have been developed. The scheme of data transmission in devices using the CAN bus. The proposed method for managing Big Data in diagnosing and predicting the state of complex technical systems is based on ensuring the dynamic distribution of data in an information-measuring system with regard to the requirements imposed on the structure of the local-computer network. The method is based on the application of the principles of the construction of software-configured networks, allowing to manage the network by using the results of the analysis of data flows passing through the node-based network devices. A software implementation of a data distribution system in a local network has been developed using the principle ofanalyzing network packets as they arrive at the switching nodes of an information-measuring system. The system of program logic of data distribution from information-measuring systems transmitted over local networks or via CAN bus has been developed. From the conducted research, it follows that the best performance of the data separation process according to the developed method is achieved with distributed execution of computational processes by the developed program in four continuous modes, and the data separation process in non-relational bases for all experiments performed is faster than for relational data. The use of the developed Big Data management method with data distribution in relational and non-relational databases provides an increase in the speed of the information-measuring system in diagnosing and predicting the state of complex technical systems. Allows you to predict the technical condition of critical components of the systems during their short-term in an emergency condition, as well as to carry out a long-term forecast of the technical condition of the entire complex technical system. The use of software distributors of transmitted information provides an increase in the speed of the information-measuring system in diagnosing and predicting the state of complex technical systems, thereby ensuring timely assessment of the state of the critical components of complex technical systems whose failure affects the operation of the systems.

Capturing Expert Knowledge to Guide Data Flow and Structure Analysis of Large Corporate Databases

Capturing Expert Knowledge to Guide Data Flow and Structure Analysis of Large Corporate Databases