Flow Graph Research Articles

Proposed Test Case Generation Model using Fuzzy Logic (TCGMFL)

This research addresses the pressing need to enhance software testing, specifically focusing on white-box testing and basis path generation. Software testing is a linchpin in the software development process, ensuring software operates flawlessly and aligns with its intended objectives. However, this phase often incurs substantial time and resource investments. The primary aim of this study is to introduce an efficient and automated approach for basis path generation, a crucial component of white-box testing. The model commences by transforming source code into a tailored control flow graph (CFG), streamlining the automated generation of test paths. Central to this model is an algorithm for generating test paths (AGTP), meticulously traversing CFG nodes from source to destination. The algorithm’s design aims to comprehensively cover all test paths within the CFG. To enhance testing process efficiency, the model employs k-means clustering to generate and cluster inputs. Path coverage is rigorously assessed for each cluster, and fuzzy logic is used to determine the optimal path. The overarching goals of this research are to reduce time and financial costs associated with software testing while maintaining precision and efficiency. The model’s effectiveness in generating test cases is confirmed through the examination of multiple examples, underscoring its valuable contribution to software testing. This study marks a significant advancement toward more effective and cost-efficient software testing methodologies.

Detecting security vulnerabilities with vulnerability nets

Detecting security vulnerabilities is a crucial part in secure software development. Many static analysis tools have proved to be effective in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape from detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some approaches incorporate the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents a representation of source code called a vulnerability net, which is a special Petri net that integrates with data dependence graphs and control flow graphs. The combined representation can facilitate the detection of taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. We test the proposed approach on Securibench Micro and demonstrate that it has the capability to identify a variety of vulnerabilities while keeping the rates of false negatives and positives low.

Malware detection system based on static and dynamic analysis and using machine learning


 Cyber wars and cyber attacks are an urgent problem in the global digital environment. Based on existing popular detection methods, malware authors are creating ever more advanced and sophisticated malware. Therefore, this study aims to create a malware analysis system that uses both dynamic and static analysis. Our system is based on a machine learning method - support vector machine. The set of data used was collected from various Internet sources. It consists of 257 executable files in .exe format, 178 of which are malicious and 79 are benign. We use 5 different types of data representation: binary information, trace instructions, control flow graph, information obtained from the dynamic operation of the file, and file metadata. Then, using multiple kernel learning, we combine all data views and create one summative machine learning model.

Graph-Indexed kNN Query Optimization on Road Network

The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm.

Analysis of Age of Information in Dual Updating Systems

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process through two independent channels. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that when the two servers have the same service rate, compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system decrease by 33.3% and 37.5%, respectively, and those of the M-D system decrease by 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.

Triplet-trained graph transformer with control flow graph for few-shot malware classification

Triplet-trained graph transformer with control flow graph for few-shot malware classification

An Inclusive Report on Robust Malware Detection and Analysis for Cross-Version Binary Code Optimizations

Numerous practices exist for binary code similarity detection (BCSD), such as Control Flow Graph, Semantics Scrutiny, Code Obfuscation, Malware Detection and Analysis, vulnerability search, etc. On the basis of professional knowledge, existing solutions often compare particular syntactic aspects retrieved from binary code. They either have substantial performance overheads or have inaccurate detection. Furthermore, there aren't many tools available for comparing cross-version binaries, which may differ not only in programming with proper syntax but also marginally in semantics. This Binary code similarity detection is existing for past 10 years, but this research area is not yet systematically analysed. The paper presents a comprehensive analysis on existing Cross-version Binary Code Optimization techniques on four characteristics: 1. Structural analysis, 2. Semantic Analysis, 3. Syntactic Analysis, 4. Validation Metrics. It helps the researchers to best select the suitable tool for their necessary implementation on binary code analysis. Furthermore, this paper presents scope of the area along with future directions of the research.

최소비용 최대유량 알고리즘에 기반한 구급차의 환자이송 병원 배정 기법의 설계

This paper presents a design and analyzes the performance of a hospital assignment and reassignment scheme for emergency rescue services based on minimum cost maximum flow algorithm. It consists of flow graph building, link capacity updating, and allocation discovering steps. The efficiency of the algorithm makes it possible to reallocate hospitals even in case of dynamic changes in the number of patients or hospitals. The performance measurement result obtained from a prototype implementation shows that the proposed scheme can reduce the transport time requirement miss by up to 24%.

Stochastic Dynamic Information Flow Tracking game using supervised learning for detecting advanced persistent threats

Advanced persistent threats (APTs) are organized prolonged cyberattacks by sophisticated attackers with the intent of stealing critical information. Although APT activities are stealthy and evade detection by traditional detection tools, they interact with the system components to make progress in the attack. These interactions lead to information flows that are recorded in the form of a system log. Dynamic Information Flow Tracking (DIFT) has been shown to be an effective way to detect APTs using information flows. A DIFT-based detection mechanism dynamically performs security analysis on the information flows to detect possible attacks. However, wide range security analysis using DIFT results in a significant increase in performance overhead and high rates of false-positives and false-negatives. In this paper, we model the strategic interaction between APT and DIFT as a non-cooperative stochastic game. The game unfolds on a state space constructed from an information flow graph (IFG) that is extracted from the system log. The objective of the APT in the game is to choose transitions in the IFG to find an optimal path in the IFG from an entry point of the attack to an attack target. On the other hand, the objective of DIFT is to dynamically select nodes in the IFG to perform security analysis for detecting APT. Our game model has imperfect information as the players are unaware of the actions of the opponent. We consider two scenarios of the game (i) the false-positive and false-negative rates of DIFT (i.e., transition probabilities of the game) are known and (ii) the false-positive and false-negative rates are unknown. For case (i), we propose a value iteration-based algorithm and prove that the solution converges to the optimal solution (Nash equilibrium). Case (ii) translates to an incomplete information game with unknown transition probabilities. For case (ii), we propose a supervised learning-based algorithm, referred to as Hierarchical Supervised Learning (HSL) algorithm. HSL integrates a neural network, to predict the value vector of the game, with a policy iteration algorithm to compute an approximate equilibrium. We implemented our algorithms for cases (i) and (ii) on real attack datasets for nation state and ransomware attacks and validated the performance of our approach. We compared the performance of the HSL algorithm when the transition probabilities are unknown with instances with known transition probabilities and demonstrated that HSL algorithm converges to a solution close to optimal (i.e., optimal value vector) while the value vector obtained using greedy does not converge to optimal for 44.4% of the states and the mean absolute error is almost 200 times that of the HSL.

A Static Detection Method for SQL Injection Vulnerability Based on Program Transformation

Static analysis is popular for detecting SQL injection vulnerabilities. However, due to the lack of accurate modeling of object-oriented database extensions, current methods fail to accurately detect SQL injection vulnerabilities in applications that use object-oriented database extensions. We propose a program transformation-based SQL injection vulnerability detection method to address this issue. This method consists of two stages: program transformation and vulnerability detection. In the first stage, object-oriented database extensions are automatically transformed into semantically equivalent procedural database extensions through the identification of key statements, call relation verification, and program transformation. In the second stage, application programs are automatically scanned using a combination of control flow graph construction and taint analysis techniques to detect SQL injection vulnerabilities. Based on the proposed method, we have implemented the OODBE-SCAN prototype system and performed experimental analysis on eight modern PHP applications. We compare OODBE-SCAN with two related static analysis tools, RIPS and Seay. The results show that OODBE-SCAN can detect more real-world vulnerabilities and has higher accuracy than existing methods.

Model-Based Design Optimization using CDFG for Image Processing on FPGA

As the automotive industry moves toward autonomous driving and ADAS (Advanced Driver Assistance Systems), Model-Based Design (MBD) is a practical design methodology. It can be used to develop rapid prototyping by using MATLAB and Simulink. The MBD method still has limitations for handling complex models. This paper uses the Control Data Flow Graph (CDFG), an intermediate representation for analyzing complex algorithms, so that suitable optimizations for image processing applications can be implemented on an FPGA. The experimental results show that the proposed CDFG method improved both the area and speed of the edge detection case study compared with the MathWorks Vision HDL toolbox.

Semantic-Enriched Code Knowledge Graph to Reveal Unknowns in Smart Contract Code Reuse

Programmers who work with smart contract development often encounter challenges in reusing code from repositories. This is due to the presence of two unknowns that can lead to non-functional and functional failures. These unknowns are implicit collaborations between functions and subtle differences among similar functions. Current code mining methods can extract syntax and semantic knowledge (known knowledge), but they cannot uncover these unknowns due to a significant gap between the known and the unknown. To address this issue, we formulate knowledge acquisition as a knowledge deduction task and propose an analytic flow that uses the function clone as a bridge to gradually deduce the known knowledge into the problem-solving knowledge that can reveal the unknowns. This flow comprises five methods: clone detection, co-occurrence probability calculation, function usage frequency accumulation, description propagation, and control flow graph annotation. This provides a systematic and coherent approach to knowledge deduction. We then structure all of the knowledge into a semantic-enriched code Knowledge Graph (KG) and integrate this KG into two software engineering tasks: code recommendation and crowd-scaled coding practice checking. As a proof of concept, we apply our approach to 5,140 smart contract files available on Etherscan.io and confirm high accuracy of our KG construction steps. In our experiments, our code KG effectively improved code recommendation accuracy by 6% to 45%, increased diversity by 61% to 102%, and enhanced NDCG by 1% to 21%. Furthermore, compared to traditional analysis tools and the debugging-with-the-crowd method, our KG improved time efficiency by 30 to 380 seconds, vulnerability determination accuracy by 20% to 33%, and vulnerability fixing accuracy by 24% to 40% for novice developers who identified and fixed vulnerable smart contract functions.

Fusion of powerful features of Harris Hawks with Cuckoo Search algorithm for engineering applications

The High-level synthesis (HLS) is a key step in the digital VLSI circuit design. HLS can be seen in the present study as an important matter of intelligently map a Data Flow Graph (DFG) specification of digital filters to FPGA. In this article, the powerful features of the Harris hawks optimization (HHO) and Cuckoo search (CS) algorithm have improved the performance of the proposed method. This proposed strategy is also known as the GChO algorithm. In GChO, the phase of CS helps to force the HHO to escape from stagnation, to ignore local optima and premature convergence. In addition for HLS, the three retiming-based models were also proposed in the work. The standard pipelining was employed in Model 1. The modified Min-Period Retiming on the multiplier less digital filters was incorporated by Model 2 and led to a significant improvement in clock frequency. Model 3 adopted modified Min-Area Retiming on digital filters and reduced the complexity of the circuit; however these designs therefore offer single parameter-based heuristic solution. This can be resolved by using meta-heuristic approach focused on retiming. The use of meta-heuristic methods and swarm intelligence has been considered as a highly desirable choice when searching the digital block solution space with higher frequency and reduced complexity in HLS problems. The robustness of the GChO method has been verified on IEEE CEC’ 2020 standard benchmark test suites. The paper analyzes that the performance of the proposed GChO algorithm outperforms the Harris hawks optimization (HHO), Moth Flame optimization algorithm, Particle Swarm optimization algorithm, Chimp optimization algorithm for the HLS digital filters. The experimental results show that the proposed Model 2 and 3 for retimed PDR-FIR filter is improved in term of MUF by 7.38% and utilized slices by 37.77%, PRO-DFII IIR retimed filter by 10.54%, and complexity reduced by 29.69%, PRO-LAT-ARF by 41.61% and slice by 11.17% and PRO-LATLAD-IIR improved in term of MUF, slices by 35.69% and 38.095% in relative to the standard retimed filters of various architecture. For the evolutionary GChO-retimed filter, an improvement in MUF by 58.52%, and utilized slices by 49.61%, for PDR-FIR, 48.09%, 80.76% for PRO-DFII-IIR , 52.68%, 54.33% for PRO-LAT-ARF, 27.85% and 18.46% for PRO-LATLAD-IIR are achieved in respect of Model 2 and 3 whereas an improvement in term of MUF by 34.60%, and utilized slices by 37.61%, for PDR-FIR, 40.55%, 39.66% for PRO-DFII-IIR, 40.08%, 31.33% for PRO-LAT-ARF, 26.95%, 24.41% for PRO-LATLAD-IIR are achieved with respect of other meta-heuristic methods.

Application of multithreading for protection programs

A method for identifying a program copy by means of digital watermark is presented in this article. Previously known analogues for constructing a watermark used modification of program's control flow graph, and reading was made via complex analysis of the program's trace. It is known that applying a protector or packer can neutralize such a watermark. In this paper, it is proposed to use a multi-threaded application that generates numbers with a normal distribution law. The embedding of the digital watermark is carried out by changing the dispersion of distributions. Such random variable can be found via a signature in the process memory and is used both within the program itself and by a third-party when reading the watermark. An experimental selection of the optimal parameters of the algorithm has been carried out. Overhead expenses associated with the creation of a large number of threads are estimated.

Binary code similarity analysis based on naming function and common vector space

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match.

Fingerprinting Bitcoin entities using money flow representation learning

Deanonymization is one of the major research challenges in the Bitcoin blockchain, as entities are pseudonymous and cannot be identified from the on-chain data. Various approaches exist to identify multiple addresses of the same entity, i.e., address clustering. But it is known that these approaches tend to find several clusters for the same actor. In this work, we propose to assign a fingerprint to entities based on the dynamic graph of the taint flow of money originating from them, with the idea that we could identify multiple clusters of addresses belonging to the same entity as having similar fingerprints. We experiment with different configurations to generate substructure patterns from taint flows before embedding them using representation learning models. To evaluate our method, we train classification models to identify entities from their fingerprints. Experiments show that our approach can accurately classify entities on three datasets. We compare different fingerprint strategies and show that including the temporality of transactions improves classification accuracy and that following the flow for too long impairs performance. Our work demonstrates that out-flow fingerprinting is a valid approach for recognizing multiple clusters of the same entity.

145 Audit of yearly prevalence of atrial fibrillation in ischemic stroke patients admitted to a level III acute Irish hospital

Abstract Background Global AF burden in acute ischemic stroke patients is studied in some evidence at 30%, with similar results in Ireland. We therefore sought to perform an audit of prevalence of atrial fibrillation in acute ischemic stroke patients in a level III hospital in republic of Ireland, to study its comparability with available evidence nationally and internationally. Methods The study was retrospective observational audit study of all patients admitted with a diagnosis of acute ischemic stroke to the concerned hospital from January, 2022 to December, 2022. Patients were identified via their diagnosis recorded in HIPE coding and in-hospital stroke registry. All patients with the diagnosis of acute ischemic stroke were screened for presence of atrial fibrillation via 72 hours R-testing. Data was collected via charts review and R test result review, compiled via flow charts, graphs and excel sheets, and studied. Results 156 patients were admitted to the hospital’s stroke service with ischemic stroke in the set time frame. 41% (N = 65) had Atrial Fibrillation (AF) which was deemed to be the cause of their stroke. 18% (N = 29) had newly diagnosed Atrial fibrillation. 23% (N = 36) had known AF. In those with known AF (N = 36), 13.8% (N = 5) were not on anticoagulation, 58.3% (N = 21) were on the adjusted dose, and 27.7% (N = 10) were not fully compliant. All new and existing AF patients not anticoagulated, were commenced on anticoagulation. Conclusion The audit shows that total atrial fibrillation burden and rate of pick-up of new atrial fibrillation in the acute ischemic stroke patients in the concerned hospital, were comparable to international evidence. Under dosage and poor compliance were major causes of anticoagulation failure in patients who were anticoagulated with known AF.

Digital Twinning and Optimization of Manufacturing Process Flows

Abstract The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models—job flow graph and AGV traveling graph—to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace.

SCGformer: Smart contract vulnerability detection based on control flow graph and transformer

AbstractThe security of smart contract has always been one of the significant problems in blockchain. As shown in previous studies, vulnerabilities in smart contracts can lead to unpredictable losses. With the rapid growth of the number of smart contracts, more and more data driven detection technologies based on machine learning have been proposed. However, some state‐of‐the‐art approaches mainly rely on the source code of smart contract. These methods are limited by the openness of the source code and the version of the programming language. To address this problem, we propose a novel vulnerability detection method based on transformer by constructing the control flow graph (CFG) of smart contracts operation codes (opcodes), which shields the difference of various versions of program language. Extensive experiments are conducted to evaluate the effectiveness of the proposed method on the authors' own collected dataset. The experimental results show that the proposed method achieves 94.36% accuracy in vulnerability detection, which performs better than other state‐of‐the‐art methods.

Signal-Flow-Graph Analysis of Weakly Nonlinear Microwave Circuits Around a Large-Signal Operating Point

A signal flow graph (SFG) representation of small-signal responses of nonlinear microwave circuits around a large-signal operating point is developed using the X-parameters. It is shown that, unlike the SFGs for linear circuits, negative-frequency nodes need to be included explicitly. The development elucidates the circuit-operational meaning of the elusive T-type small-signal X-parameters, which represent the interaction between positive-and negative-frequency components. As an example, such an SFG is used to derive a closed-form expression of the output power of an amplifier as a function of the load reflection coefficient. It is then used to plot approximate load-pull power contours. The result is consistent with the expressions of the optimum load reflection coefficient derived by Root et al. (EuMIC 2017) and power contours derived by Peláez-Pérez et al. (TMTT 2013). SFGs provide an alternative systematic means to derive closed-form expressions in terms of X-parameters and gain illuminating insights into the workings of weakly nonlinear circuits.