Static Analysis Methods Research Articles

Wing optimization for static aeroelastic effect

Abstract An aeroelastic analysis module based on the VLM model and FE model with non-linear static analysis was developed to calculate the lift and deformation of a wing in a cruise case. The traditional static aeroelastic analysis method focuses on compensation of loss of lift by adjusting wing root AoA (angle of attack). To improve the aeroelastic property, integrated static aeroelastic optimization was developed, which compensates for the loss lift by adjusting a series of sectional AoA instead of only wing root AoA. Compared with traditional static aeroelastic analysis, the manufacture wing optimized by integrated static aeroelastic optimization is closer to the initial design wing.

Novel Multi-Classification Dynamic Detection Model for Android Malware Based on Improved Zebra Optimization Algorithm and LightGBM.

With the increasing popularity of Android smartphones, malware targeting the Android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning algorithms for detection. However, static analysis methods can be less effective when faced with Android malware that employs sophisticated obfuscation techniques such as altering code structure. In order to effectively detect Android malware and improve the detection accuracy, this paper proposes a dynamic detection model for Android malware based on the combination of an Improved Zebra Optimization Algorithm (IZOA) and Light Gradient Boosting Machine (LightGBM) model, called IZOA-LightGBM. By introducing elite opposition-based learning and firefly perturbation strategies, IZOA enhances the convergence speed and search capability of the traditional zebra optimization algorithm. Then, the IZOA is employed to optimize the LightGBM model hyperparameters for the dynamic detection of Android malware multi-classification. The results from experiments indicate that the overall accuracy of the proposed IZOA-LightGBM model on the CICMalDroid-2020, CCCS-CIC-AndMal-2020, and CIC-AAGM-2017 datasets is 99.75%, 98.86%, and 97.95%, respectively, which are higher than the other comparative models.

Malware classification through Abstract Syntax Trees and L-moments

The ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation of code, enabling a deeper understanding of its inherent behaviors and patterns. This approach aims to detect not only known malware but also uncover new, previously unidentified threats. A comprehensive comparison with traditional static analysis methods shows that this approach excels in key performance metrics such as accuracy, precision, recall, and F1 score. These results demonstrate the significant potential of combining L-moments derived from network analysis with ASTs in enhancing malware detection. While static analysis remains an essential tool in cybersecurity, the integration of L-moments and advanced network analysis offers a more effective and efficient response to the dynamic landscape of cyber threats. This study paves the way for future research, particularly in extending the use of L-moments and network analysis into additional areas.

The dynamic reconfiguration of the functional network during episodic memory task predicts the memory performance

Episodic memory is essential for forming and retaining personal experiences, representing a fundamental aspect of human cognition. Traditional studies of episodic memory have typically used static analysis methods, viewing the brain as an unchanging entity and overlooking its dynamic properties over time. In this study, we utilized dynamic functional connectivity analysis on fMRI data from healthy adults performing an episodic memory task. We quantified integration and recruitment metrics and examined their correlation with memory performance using Pearson correlation. During encoding, integration across the entire brain, especially within the frontoparietal subnetwork, was significantly correlated with memory performance. During retrieval, recruitment becomes significantly associated with memory performance in visual subnetwork, somatomotor subnetwork, and ventral attention subnetwork. At the nodal level, a significant negative correlation was observed between memory scores and integration of the anterior cingulate gyrus, precentral gyrus, and inferior frontal gyrus within the frontoparietal network during encoding task. During retrieval task, a significant negative correlation was found between memory scores and recruitment in the left progranular cortex and right transverse gyral ventral, whereas positive correlations were seen in the right posterior inferior temporal, left middle temporal, right frontal operculum, and left operculum nodes. Moreover, the dynamic reconfiguration of the functional network was predictive of predict memory performance, as demonstrated by a significant correlation between actual and predicted memory scores. These findings advance our understanding network mechanisms underlying memory processes and developing intervention approaches for memory-related disorders as they shed light on critical factors involved in cognitive processes and provide a deeper understanding of the underlying mechanisms driving cognitive function.

WCET Analysis Based on Micro-Architecture Modeling for Embedded System Security

To ensure the timely execution of hard real-time applications, scheduling analysis techniques must consider safe upper bounds on the possible execution durations of tasks or runnables, which are referred to as Worst-Case Execution Times (WCET). Bounding WCET requires not only program path analysis but also modeling the impact of micro-architectural features present in modern processors. In this paper, we model the ARMv8 ISA and micro-architecture including instruction cache, branch predictor, instruction prefetching strategies, out-of-order pipeline. We also consider the complex interactions between these features (e.g., cache misses caused by branch predictions and branch misses caused by instruction pipelines) and estimate the WCET of the program using the Implicit Path Enumeration Technique (IPET) static WCET analysis method. We compare the estimated WCET of benchmarks with the observed WCET on two ARMv8 boards. The ratio of estimated to observed WCET values for all benchmarks is greater than 1, demonstrating the security of the analysis.

A modified quasi-3D theory and mixed beam element method for static behaviour analysis of functionally graded beams

This paper develops a modified quasi-3D theory and the mixed beam element model for static analysis of functionally graded beams. The modified quasi-3D theory encompasses two innovations. Firstly, the accurate distribution of transverse shear stress is derived from the differential equilibrium equation that describes the relationship between stresses, rather than the traditional one derived from geometric relations and constitutive equations. Secondly, the effect of distributed loads associated with transverse stretching deformation, a factor typically overlooked in the existing quasi-3D theory-based beam models, is involved. In the development of beam finite element model, the mixed variational principle is firstly utilized to formulate the model of a quasi-3D theory-based beam element, where generalized displacements and internal forces are treated as two types of independent field quantities. Two numerical examples are conducted to investigate the validity and accuracy of the proposed theory and beam element. Numerical results indicate that the proposed mixed beam element can accurately predict the stress distributions over the cross-section and produce accurate displacement solutions. Meanwhile, it is noted that the effect of distributed loads related to the transverse stretching deformation should be correctly considered in the analysis of functionally graded beams based on quasi-3D theory.

Correction: Static Modal Analysis: A Review of Static Structural Analysis Methods Through a New Modal Paradigm

Correction: Static Modal Analysis: A Review of Static Structural Analysis Methods Through a New Modal Paradigm

Study on Multi-Index Evaluation Technology of Seismic Performance of Green Ecological Building Structure

Traditional evaluation methods based on static elastic-plastic analysis usually produce results with low fitting degree compared with actual data. In this paper, a new seismic performance evaluation method is proposed. Firstly, several evaluation indexes of green ecological building structure are calculated. Subsequently, a cumulative damage model is established based on fatigue life curve and Miner criterion, which allows to determine the total dissipated strain energy and damage index. In order to verify the effectiveness of this method, it is compared with the traditional static elastic-plastic analysis method through experimental tests. The results show that compared with the traditional method of 50% to 60%, the proposed method achieves a significantly higher fitting degree with the actual data, ranging from 70% to 92%. This emphasizes the superiority and reliability of the proposed method in evaluating the seismic performance of green ecological building structures, and provides insights for safer and more flexible building practices.

Comparative Study on Mechanical Response in Rigid Pavement Structures of Static and Dynamic Finite Element Models

The safety of airport runways is important to guarantee aircraft taking-off, landing, and taxiing, and the comparison of the mechanical response of pavement structures under dynamic and static loading by LS-DYNA has rarely been studied. The purpose of this work is to separate two analysis methods to investigate the mechanical response of rigid airport pavements. Firstly, a tire–road coupling model of an airfield was established to evaluate the suitability of dynamic and static analyses. Then, the effects of landing pitch angles, sinking speeds, and tire pressures on the effective stress, effective strain, and z-displacement of the runway were investigated for both dynamic and static analysis. Finally, the significance of influence factors was analyzed by regression analysis in Statistical Product and Service Solutions (SPSS). The results indicated that the effective stress, effective strain, and z-displacement of the runway increased with a decrease in the landing pitch angle, which also increased with an increase in the sinking speed and tire pressure. It was demonstrated that the difference in pavement mechanical response between dynamic and static analyses progressively widened at high tire pressure and sinking speed. In other words, the static analysis method can be adopted to assess the dynamic mechanical behavior when the landing pitch angle is large and the tire pressure is small. Among the various factors of mechanical response, the effect of tire pressure was the most obvious, followed by sinking speed and landing pitch angle. The work proposes a new approach to understanding the mechanical behavior of runways under complicated and varied conditions, evaluates the applicability of the dynamic and static mechanical analysis methods, identifies key factors in the dynamic and static mechanical analysis of rigid runways, and provides technical support for improving and maintaining the impact resistance of pavement facilities.

IoT malware detection using static and dynamic analysis techniques: A systematic literature review

AbstractThe Internet of Things (IoT) is reshaping the world with its potential to support new and evolving applications in areas, such as healthcare, automation, remote monitoring, and so on. This rapid popularity and growth of IoT‐based applications coincides with a significant surge in threats and malware attacks on IoT devices. Furthermore, the widespread usage of Linux‐based systems in IoT devices makes malware detection a challenging task. Researchers and practitioners have proposed a variety of techniques to address these threats in the IoT ecosystem. Both researchers and practitioners have proposed a range of techniques to counter these threats within the IoT ecosystem. However, despite the multitude of proposed techniques, there remains a notable absence of a comprehensive and systematic review assessing the efficacy of static and dynamic analysis methods in detecting IoT malware. This research work is a systematic literature review (SLR) that aims to offer a concise summary of the latest advancements in the field of IoT malware detection, specifically focusing on the utilization of static and dynamic analytic techniques. The SLR focuses on examining the present status of research, methodology, and trends in the area of IoT malware detection. It accomplishes this by synthesizing the findings from a wide range of scholarly works that have been published in well‐regarded academic journals and conferences. Additionally, the SLR highlights the significance of the empirical process that includes the role of selecting datasets, accurate feature selection and the utilization of machine learning algorithms in enhancing the detection accuracy. The study also evaluates the capability of different analysis techniques to detect malware and compares the performance of various models for IoT malware detection. Furthermore, the review concluded by addressing several open issues and challenges that the research community as a whole must address.

Evaluating Stakeholders’ Decisions in A Blockchain-Based Recycling Construction Waste Project: A Hybrid Evolutionary Game and System Dynamics Approach

To promote efficient construction waste recycling and reuse, a novel waste management approach based on blockchain technology was introduced to the industry. However, adopting blockchain platforms in construction waste recycling and reuse may impact the behavioral strategies of stakeholders and impede the prediction of the specific impacts of stakeholders’ decisions. Accordingly, this study addresses two primary questions: (1) What are the collaborative framework and the behavioral evolution trends of multiple stakeholders within the context of blockchain? (2) How can the behavioral strategies of multiple stakeholders be systematically coordinated to achieve efficient construction waste recycling and reuse driven by blockchain? To answer these questions, a tripartite game model combined with system dynamics was constructed. In this model, we aimed to elucidate the internal organizational framework, analyze the dynamic evolution process, and assess the influence of decisions made by multiple stakeholders at the individual level. It also offers corresponding policy recommendations for efficient construction waste recycling and reuse driven by blockchain at the system level. This study offers three innovations. First, it considers the decision-making of multiple stakeholders as an interdependent and coevolutionary process to overcome the defects of analyzing only one type of participant. Second, in contrast to the static analysis method, it employs a dynamic system approach to deeply analyze the evolving structures of blockchain-based projects. Third, it provides a theoretical framework for the practical implementation of blockchain-driven platforms in managing construction waste recycling and reuse, thus fostering effective policy development and management practices. This framework aims to promote sustainable development in construction waste recycling and reuse projects in China as well as globally.

A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENHANCING CYBERSECURITY THREAT DETECTION AND RESPONSE MECHANISMS

This literature review explores the transformative impact of artificial intelligence (AI) on enhancing cybersecurity measures across various domains. The study systematically examines the integration of AI in Intrusion Detection Systems (IDS), malware detection, phishing detection, threat intelligence, network security, and endpoint protection. Key findings reveal that AI-driven techniques significantly outperform traditional methods, particularly in real-time threat detection, accuracy, and adaptive response capabilities. Network-based IDS benefit from supervised and unsupervised learning algorithms, improving the identification of malicious network traffic and novel attack patterns. In malware detection, AI-enhanced static and dynamic analysis methods surpass signature-based approaches by detecting previously unknown malware and complex behaviors. Phishing detection has seen substantial improvements with AI applications in email filtering and URL analysis, reducing phishing incidents despite challenges like false positives. AI's role in threat intelligence is critical, automating data analysis to uncover hidden threats and employing predictive analytics to anticipate and mitigate cyber attacks. AI techniques in network security and endpoint protection enhance real-time monitoring and authentication processes, providing robust defenses against cyber intrusions. Despite these advancements, challenges such as handling high data volumes and the need for continuous learning to adapt to emerging threats remain. This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.

A Survey on Malware Detection and Analysis

Malware, or malicious software, poses a significant threat to the security and functionality of computer systems globally. This survey provides a comprehensive analysis of current malware detection and analysis methods, focusing on data mining methodologies. The study categorizes malware detection techniques into signature-based and behaviour-based approaches, highlighting their respective strengths and weaknesses. It explores heuristic techniques enhanced by artificial intelligence, including neural networks and genetic algorithms, to improve detection accuracy. The literature review examines host-based and network-based intrusion detection systems, hybrid systems, and virtual machine introspection. The paper also discusses static and dynamic analysis methods, emphasizing the importance of analysing malware in controlled environments. Through detailed examination, this survey aims to present a thorough understanding of contemporary malware detection strategies and their applications, offering insights for future advancements in the field.

A comparison of rolling element bearing stiffness calculation methods

PurposeThis study examines two distinct bearing stiffness calculation methods, both of which are based on the displacement-load function. Previous research typically incorporated one type of bearing stiffness into their system mechanics or vibration analysis. However, these two methods of calculating stiffness lead to different vibration models. This implies that the choice for vibration investigation is not merely about selecting one of the two types of stiffness, but also about how to appropriately implement that chosen stiffness within a model. The primary objective of this work is to compare these two methods of bearing calculation and to discuss the suitable applications of each method in both static and dynamic analyses.Design/methodology/approachThis study compares two distinct methods for calculating bearing stiffness. It explores the relationships between varying bearing stiffnesses, their internal structures, and contact features. Furthermore, it examines the impact of external loads on the static properties and dynamic characteristics of different bearing stiffnesses. Finally, based on the outcomes observed under various operating conditions, the study discusses the suitability of each method for static and dynamic analysis.FindingsMean stiffness is more suitable for calculating load transmissibility in a static state or capturing the delivery performance at instantaneous equilibrium positions in a dynamic state. Since the variation of the equilibrium positions is ignored, the alternating stiffness model is better suited for capturing the fluctuating properties of the vibration behaviors, especially under variable external load conditions.Originality/valueWe compare the two bearing calculation methods and discuss the appropriate applications of each method for static and dynamic analysis.

Abnormal static and dynamic brain network connectivity associated with chronic tinnitus

In order to comprehensively understand the changes of brain networks in patients with chronic tinnitus, this study combined static and dynamic analysis methods to explore the abnormalities of brain networks. Thirty-two patients with chronic tinnitus and 30 age-, sex- and education-matched healthy controls (HC) were recruited. Independent component analysis was used to identify resting-state networks (RSNs). Static and dynamic functional network connectivity (FNC) were performed. The temporal properties of brain network including mean dwell time (MDT), fraction time (FT) and numbers of transitions (NT) were calculated. Two-sample t test and Spearman’s correlation were used for group compares and correlation analysis. Four RSNs showed abnormal FNC including auditory network (AUN), default mode network (DMN), attention network (AN) and sensorimotor network (SMN). For static analysis, tinnitus patients showed significantly decreased FNC in AUN-DMN, AUN-AN, DMN-AN, and DMN-SMN than HC [p < 0.05, false discovery rate (FDR) corrected]. For dynamic analysis, tinnitus patients showed significantly decreased FNC in DMN-AN in state 3 (p < 0.05, FDR corrected). MDT in state 3 was significantly decreased in tinnitus patients (t = 2.039, P = 0.046). In the tinnitus group, the score of tinnitus functional index (TFI) was negatively correlated with MDT and FT in state 4, and the duration of tinnitus was positively correlated with FT in state 1 and NT. Chronic tinnitus causes abnormal brain network connectivity. These abnormal brain networks help to clarify the mechanism of tinnitus generation and chronicity, and provide a potential basis for the treatment of tinnitus.

MALWARE IMAGE PREDICTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Without users' permission, malware software can infect computers or other devices. Through these loopholes, criminals commit a range of illegal and criminal offences that violate the legitimate rights and interests of the nation. Traditional malware categorization techniques fall into two categories: static analysis and dynamic analysis. It is usually not necessary to execute malware binary samples in order to perform static analysis techniques, and disassembly makes it simple to recover important data such as text lists, routines, and hash values. The static analysis methods offer a high accuracy rate and a simple operation with a low consumption time. Static analysis tools, however, are limited to analyzing malware binary samples at the surface level, where they are readily influenced by deformation and other means of confusion. Furthermore, it is challenging to identify and categorize unknown malware. Methods of dynamic analysis are not impacted by obfuscation and can operate in a virtual environment. It has the ability to recognize newly discovered malware samples and track the dynamic alterations of malware binary samples over time. Nevertheless, it's an extremely intricate and time-consuming process. Malware has become one of the largest security threats in recent years due to its rapid growth. But feature engineering makes it difficult to handle large amounts of malware and readily limits the use of standard machine learning methods for malware categorization. However, dynamic analysis methodologies are not appropriate for efficiently categorizing large amounts of malware due to their complexity and high cost. In light of this, we propose a novel static malware detection method based on the convolutional neural network (CNN) employed in this work. Unlike existing methods, we use the data enhancement method to fix the unbalanced datasets, turn every viral byte into a colour image, and provide a better design.

Galerkin boundary method for static analysis of single thin mitered bend

The problem of a single unreinforced thin-walled mitered bend under static loading (inner pressure or bending in-plane moment) is examined. Despite its age and long history, few practically efficient developments have been made for this problem. The essence of current solution consists in consideration of the stress-strain state of a straight thin cylindrical shell and projecting its inner forces onto the miter plane. All parameters of the shell are expanded into Fourier series with respect to circumferential coordinate. Analytical expressions for eigenfunctions of the shell are found using the decoupling procedure. Then, boundary conditions are satisfied by the Galerkin boundary method. Comparison with known theoretical and FEA results shows great efficiency of proposed approach.

Effects of infill wall thickness and arrangement on the seismic performance of the reinforced concrete frames

In this study, it is aimed to investigate the seismic behavior of the reinforced concrete (RC) frames with and without infill walls having different thickness and arrangement. To this, 3, 4, 5, 6, and 7 story RC frames consisting of 4 and 5 bays were studied. Infill walls were introduced into the frame models by considering four different arrangements over the height of the structure such as full infill frame (FIF), full infill frame with soft story (FI-SSF), exterior bay infill frame (EBIF), and exterior bay infill frame with soft story (EBI-SSF). For each case, four different infill wall thicknesses of 10 to 25 cm were taken into account. Thus, a total of 170 different frame models with and without infill walls were investigated thorough nonlinear static analysis method by means of SAP 2000 program. The infill panels were modeled by utilizing equivalent diagonal compression strut. As a result of the analyses, the capacity curves of the structures, maximum base shear force, initial stiffness, and mechanism of the plastic hinge formation in the structures were determined and comparatively discussed. It was observed that both wall thickness and arrangement have significant impact on the lateral load carrying capacity and hinge formation of the case studied structures, on the other hand, with their compatible choice the greatest seismic performance could be achieved.

Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Data Flows to Generate Data Flow Graphs in Dynamically-Typed Code

Data flow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically-typed programming languages like Python present implicit data flow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit data flow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit data flow problems. To further enhance the accuracy of LLMs, we design a five-step Chain of Thought (CoT) and break it down into an AI chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit data flow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.

Axial Compressive Behaviours of Coal Gangue Concrete-Filled Circular Steel Tubular Stub Columns after Chloride Salt Corrosion.

The axial compressive behaviours of coal gangue concrete-filled steel tube (GCFST) columns after chloride salt corrosion were investigated numerically. Numerical modelling was conducted through the static analysis method by finite element (FE) analysis. The failure mechanism, residual strength, and axial load-displacement curves were validated against tests of the coal gangue aggregate concrete-filled steel tube (GCFST) columns at room and natural aggregate concrete-filled steel tube (NCFST) columns after salt corrosion circumstance. According to the analysis on the stress distribution of the steel tube, the stress value of the steel tube decreased as the corrosion rate increased at the same characteristic point. A parametric analysis was carried out to determine the effect of crucial variation on residual strength. It indicated that material strength, the steel ratio, and the corrosion rate made a profound impact on the residual strength from the FE. The residual strength of the columns exposed to chloride salt was in negative correlation with the corrosion rate. The impact on the residual strength of the column was little, obvious by the replacement rate of the coal gangue. A simplified design formula for predicting the ultimate strength of GCFST columns after chloride salt corrosion exposure was proposed.