Static Analysis Methods Research Articles

Research on investment project evaluation: Comparative analysis based on NPV and IRR

Whether a project is viable, determining whether a project is viable requires some metrics. The indicators that are most frequently utilized are the Net Present Value (NPV) and the internal rate of return (IRR). They are very representative and scientific. The same decision can be made by applying both in independent and non-independent projects When there are many contradictions between NPV and IRR, it is necessary to introduce new indicators to continue to judge the project, such as △IRR, incremental IRR method, modified IRR (MIRR) method, selective IRR method, payback period method, Equivalent Annual Annuity (EAA) method and static analysis method. The investment is selected by comparing the internal rate of return △IRR with the benchmark rate of return (ic) or the social discount rate. The MIRR approach determines the IRR of each scheme, compares it to ic, chooses the scheme that have IRR is larger than or the same as ic, and then determines the incremental IRR of the two neighboring schemes to make the choice. In addition to the investment payback period approach and the selective internal rate of return method. EAA method is in the comparison of investment projects, investors should choose the project with higher EAA when comparing. In the process of investment projects, different enterprises can adopt static analysis method to carry out project investment, but there are two forms of static analysis method, such as payback period method and basic income method. Using reasonable indicators and methods to evaluate the investment project, so as to select the best project.

STUDY OF THE GENERAL SITUATION OF IT IN UKRAINE IN DETERMINING THE FACTORS INFLUENCING THE ECONOMIC PERFORMANCE OF THE SEGMENT

The article is devoted to analyzing and researching Ukraine's technology segment. This study was conducted due to the importance of the segment for developing countries and the importance of understanding the main factors of influence that allow for improving the economic performance of the segment's entities. The article analyzes the general situation of the technology sector in Ukraine. Various methods of analysis were used. One was the comparison method, which included indicators for the technology and information segments since these segments form the country's ITC sector. The article reflects trends and analyzes specific segment indicators. Hence, an upward trend was analyzed and derived to indicate the number of units involved in the technology segment. The predominance of micro and mini entities in the segment was confirmed, which confirms the previously conducted tests for Central and Eastern Europe when reviewing certain countries. It is noted that the central economic units representing Ukraine's IT segment are individual entrepreneurs. The study demonstrates the dynamics and analysis of economic performance indicators of the segment. Three main groups of factors of possible impact on the performance of economic entities were presented. Hence, the tax burden on the basic taxes in the form of income, profit, and social tax was analyzed. Since individual entrepreneurs represent most entities in the IT segment, the tax base was studied at the general rates and when deriving separate data for the IT segment. The educational component was demonstrated to familiarize and understand the growth trend in the number of students majoring in IT. The third group of factors was represented by the costs of specialists involved in the segment's work, such as the total segment's personnel costs and the average monthly salary. In general, the trend in the indicators in the article is demonstrated as an upward trend. The general analysis presented in the article allowed to derive a model of dependence of the net profit of IT segment entities in Ukraine on the factors of influence when determining only specific indicators of influence using the methods of static base analysis. The final model was obtained by conducting a panel least squares modeling with the test for omitted variables.

A Comprehensive Study of Efficient and Accurate Malware Detection Using Static and Dynamic Analysis Methods.

A Comprehensive Study of Efficient and Accurate Malware Detection Using Static and Dynamic Analysis Methods.

Measuring the Security of Indonesian Local Goverment Mobile Apps

Mobile apps are critical in supporting the Indonesian provincial government during daily operations. Regarding their important role, these apps are the potential to become a target of malicious activity to gain access to government information. Hence, in this research, we conduct a security measurement of mobile apps affiliated with the provincial governments in Indonesia, aiming to provide a source of information for the app's developer to make further security improvements. To the best of our knowledge, this is the first study to conduct an empirical analysis related to this area, expecting to become a cornerstone for further research. In total, we found 162 mobile apps available on the Google Play Store and correlated to 31 province governments in Indonesia. We conducted standard mobile apps static analysis method, starting from metadata scraping of each provincial government-affiliated app available at the Google Play Store and measured their security based on several indicators, including access to sensitive permission, unprotected exported components, the adoption of binary protection mechanism, and the appearance of third-party tracker libraries. We found that 27.1% (44) government apps request at least two dangerous permission, over 5% of the apps embed the Advertisement library, 10% of the apps have more than five exported Activities, and 44 (25.94%) Indonesian government apps did not adopt any binary protection mechanism. Hence, these results can be tailored as a reference for the provincial government in determining the level of effectiveness and impact of an app when developing mobile-based software in the future.

CNN-LSTM Hybrid Model for Enhanced Malware Analysis and Detection

The ever-evolving tactics employed by malware authors to avoid detection pose challenges to the conventional static analysis method, which entails examining the malware code. These challenges arise from the authors’ capacity to obfuscate their code. To address this matter and enhance the identification of malware, integrating dynamic detection and machine learning has emerged as a highly promising approach. This methodology has demonstrated efficacy in identifying malware specifically engineered to circumvent established detection techniques. Behavioural analysis is a crucial component in ensuring endpoints’ security, with the CNN-LSTM algorithm being particularly notable for its effectiveness in identifying Zero-Day malware. This type of malware poses a substantial obstacle to conventional signature-based approaches. This paper aims to assess the efficacy of the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, emphasising its significance in tackling the continuously evolving realm of cybersecurity obstacles. The research highlights the significance of transitioning from traditional signature-based detection methods to behavioural analysis techniques. It suggests utilising deep learning approaches such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to improve the ability to detect malware in an environment where threats constantly evolve. The malware detection system that has been developed encompasses a log parser analyser, API monitoring, and an extension checker module. The CNN-LSTM model demonstrates a commendable ability to accurately identify malicious behaviour, achieving a validation accuracy of 96%. This study demonstrates the efficacy of employing behavioural analysis and deep learning techniques to enhance cybersecurity, particularly in addressing sophisticated, evasive, and previously unknown malware risks.

VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis

Smart contracts have gained immense popularity in recent years as self-executing programs that operate on a blockchain. However, they are not immune to security flaws, which can result in significant financial losses. These flaws can be detected using dynamic analysis methods that extract various aspects from smart contract bytecode. Methods currently used for identifying vulnerabilities in smart contracts mostly rely on static analysis methods that search for predefined vulnerability patterns. However, these patterns often fail to capture complex vulnerabilities, leading to a high rate of false negatives. To overcome this limitation, researchers have explored machine learning-based methods. However, the accurate interpretation of complex logic and structural information in smart contract code remains a challenge. In this study, we present a technique that combines real-time runtime batch normalization and data augmentation for data preprocessing, along with n-grams and one-hot encoding for feature extraction of opcode sequence information from the bytecode. We then combined bidirectional long short-term memory (BiLSTM), convolutional neural network, and the attention mechanism for vulnerability detection and classification. Additionally, our model includes a gated recurrent units memory module that enhances efficiency using historical execution data from the contract. Our results demonstrate that our proposed model effectively identifies smart contract vulnerabilities.

A function method for static load-sharing analysis of multiple-branch power-split gearing system

A function method was proposed for the static load-sharing analysis of the power-split gearing system. Taking a two-stage three-branch star gearing system used in a certain type of aircraft engine as the object, based on the deformation compatibility condition that considers the torsional deformation of the dual star gear shaft, the function that describes the changing rule of the load-sharing coefficient was established, which could calculate the load-sharing coefficient without the truncation error. Through analysing the load-sharing susceptibility to gear eccentricity errors with the traditional graphic and function method, respectively, the correctness and advantages of the proposed method were verified. Finally, considering the randomness of eccentricity errors in gear manufacturing and installation, the load-sharing coefficient function matrix was established to analyse the load-sharing characteristics when the errors were generated randomly. The result shows that the function method can avoid possible truncation errors in the load-sharing coefficient calculation and make the static load-sharing analysis more efficient; the randomness of the error value has a greater influence on the load-sharing than the randomness of the error direction; the randomness of the error direction may affect the susceptibility of the load-sharing to installation errors, which should be considered in the analysis of load-sharing susceptibility to the error with a fixed direction.

Fault-seal analysis in the Greater Bay du Nord area, Flemish Pass Basin, offshore Newfoundland

A 3D subsurface structural model was built in a zone of the Greater Bay du Nord area, Flemish Pass Basin, offshore Newfoundland and Labrador, to carry out a post-drilled, fault-seal analysis in a multi-rift, geological complex setting; this aimed to test fault-seal predictions, calibrate computed static fault-zone attributes and estimate hydrocarbon contact depths. Hydrocarbon exploration campaigns in the Greater Bay du Nord area have primarily targeted rotated fault blocks that often exhibit structural segmentation and compartmentalization. A comprehensive approach that combines empirical and deterministic methods for static fault-seal analysis has been implemented. This approach provides insights into open, base and tight fault-seal scenarios, aiding prospect evaluation in this region. Notably, shale gouge ratios (SGRs) in the range 16–25% serve as a crucial indicator of the transition between fault-rock sealing and non-sealing fault segments. Furthermore, it emphasizes the critical role of hydrodynamics when calibrating or evaluating fault-sealing properties. In areas like the Greater Bay du Nord region, characterized by complex geology, it is imperative to regularly update fault-seal models. These updates should align with the availability of new subsurface data, comprehensive analyses and an improved understanding of the petroleum system. Thematic collection: This article is part of the Fault and top seals 2022 collection available at: https://www.lyellcollection.org/topic/collections/fault-and-top-seals-2022

Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.

Application of 3D acoustic scene analysis using sound arrival direction at Noi Bai International Airport in Vietnam

This paper discusses improvement of 3D acoustic scene analysis based on sound arrival direction for airport noise monitoring as well as illustrates a result of application using noise observations of unattended noise monitoring at four noise monitors installed near the both ends of two closed-parallel runways at Noi Bai International Airport in Vietnam. The static analysis method visualized the noise level distribution in the focused direction by varying the analysis time. Estimation of sound source movement trajectories not only for landing paths but also for takeoff paths was also realized. The result suggests that the proposed method is useful for identifying noise contributions of various sound sources including aircraft take-off and landing operations aircraft ground operation on the apron and taxiway.

SHFuzz: Service handler-aware fuzzing for detecting multi-type vulnerabilities in embedded devices

Embedded devices in IoT are of great convenience to our daily lives and industries, but they also introduce multi-type vulnerabilities. Most vulnerabilities reside in various handlers of service program. However, existing fuzzing methods existing fuzzing methods can neither efficiently nor effectively to discover these vulnerabilities: they can not extract the service handler alone and properly pool resources for testing them.In this study, we propose a novel service handlers aware fuzzing method to efficiently discover multi-type vulnerabilities in embedded devices. Our key observation is that service program dispatches the service handlers (the code snippet which implements the desired work of user request) in two general ways. Meanwhile, multi-type vulnerabilities usually happen at sensitive APIs during the execution of service handler. So in our method, we first design a static analysis method to extract the info of service handlers and sensitive APIs. Then during fuzzing, the service handler coverage strategy guides the fuzzer to cover more service handlers; after the sensitive APIs are reached, the sensitive APIs exploitation strategy is proposed to strengthen the ability and efficiency of discovering vulnerabilities. Based on our method, we implement a prototype system, named SHFuzz. Experiments on 19 target programs from popular brand devices show that, SHFuzz outperforms the state-of-art tool FIRM-AFL in the coverage of sensitive APIs, the diversity of detected vulnerabilities and efficiency. Furthermore, SHFuzz discovers 16 zero-day vulnerabilities and reports them to vendors.

Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques

Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.

Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure

The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human Interface Infrastructure). In practice, HII provides a mechanism that allows developers to create UI elements with HII-related protocols. In this paper, we provide a comprehensive analysis of the UEFI combined with a case study. We proposed a protocol-centered static analysis method to obtain UEFI’s password policy, using HII-related protocols to find password implementation. Existing static analyses are ineffective in detecting such password policy in stripped UEFI firmware images. By reverse-engineering the IFR (Internal Forms Representation) in HII, we located where much sensitive information is stored. Lastly, we studied hardware port configurations, using Secure Boot as a case in point. We analyzed how UEFI uses the HII protocol to set relevant information in the UEFI UI. This paper is the first to offer a reverse-engineering systematic analysis of exploring UEFI via HII, providing valuable insights into its structure and potential enhancements for firmware security.

Structural Optimization of Diaphragm Spring Clutch Based on ANSYS

Taking diaphragm spring clutch as the research object, a mathematical model was established in MATLAB to calculate and select its parameters. The multi-objective optimization method was used to obtain the optimized parameters. Using UG to create a three-dimensional model of the diaphragm spring, and ANSYS Workbench to conduct finite element analysis. By using the static analysis method, the root of the separation finger was found to be the part that can withstand the maximum stress, and its value met the material’s performance requirements, verifying the feasibility of the method. Modal analysis of the diaphragm spring was carried out, and it was verified that there would be no resonance with the engine, verifying its matching with the engine, at the same time, the effectiveness of the optimization is verified.

Progressive Collapse Analysis of Reinforced Concrete Multistoried Frame Structure

Abstract: The objective of this project is to analyze the progressive collapse behavior of 10 story reinforced concrete frame structures by removing column suddenly from different locations, as per the GSA guidelines. To achieve this, a structural model of the building has been developed in ETABS version 18.1.1 and loads are applied as per GSA guidelines. The design and detailing of the structure are done in accordance with IS codes, using the special moment-resistant frame linear static analysis method. In accordance with GSA guidelines, three-column removal scenarios have been studied on the ground floor, including the removal of a Corner column, Middle column of longer and Middle column of shorter side. The Demand/Capacity ratios (DCR) for each member have been calculated using the linear static analysis method. Members with a DCR ratio greater than 2 are expected to fail in a similar column removal case. Past studies have revealed that shear in a beam and columns are not crucial in progressive collapse. However, the linear static analysis method has demonstrated that beams are prone to fail in flexure.

Correction Factor Method As A Substitute For Sequential Construction Analysis Of Buildings

Abstract: Within the sphere of structural analysis, a method of paramount importance emerges - the Correction Factor Method. Unlike conventional approaches, this method stands as a stalwart against the complexities of non-linear structural behavior. It pioneers a path that diverges from the linear norm, offering precision and insight where others falter. The structural parts of the building are analyzed in one step using linear static analysis method by assuming that the structure will be subjected to full load after the finishing of the construction completely. In reality things work in other way the dead load from each structural members and finishing materials are imposed in different stages as the structure is constructed storey by storey. Similarly the stability of frame changes at every stage of construction. Because of this changes the loads assumed in conventional analysis vary in the construction process and the results obtained in the traditional analysis will be unstable. Thus the structural frame should be analyzed in every structural construction stage by keeping variation of loads. This process is called as Construction Stage Analysis this process considers all the uncertainties precisely. This process is a time consuming process as the analysis is a complex process. Thus Correction factor method can be introduced where the analysis time can be reduced and the results will be accurate to Construction Stage Analysis. In this project multistoried reinforced concrete building frames with different number of floors are analyzed using ETABS, and then Construction Stage Analysis of each building model is done. A comparative study of Bending moments, axial forces, twisting moments and shear forces are carried out at every stage of the structural frame. These values are compared with linear static analysis and Construction Stage Analysis. From the values obtained correction factors are derived. From this method we can get results which has a maximum error of 2% and this proves that correction factor can be used as a substitute for sequential construction analysis

Resistance responses and design recommendations for driven piles in coarse-grained soil-based intermediate geomaterials

Piles driven in Intermediate GeoMaterials (IGM) pose multiple design and construction challenges because of the high uncertainty in IGM properties, lacking knowledge pertaining to pile responses in IGM, and absence of classification, static analysis (SA) methods, and design recommendations. A classification criterion is established for coarse grained soil based intermediate geomaterials (CG-IGM) using test pile data from bridge projects completed in four U.S. states. This study improves our understanding of pile resistance responses in CG-IGM and results in pile design recommendations. Unit shaft resistance (qs) of CG-IGM increases with the ratio of effective vertical stress (σv′) to the ratio of corrected N-value, (N1)60. Unit end bearing (qb) increases with the ratio of. corrected N-value, (N1)60 to the effective vertical stress (σv′). New SA methods are developed for predicting qs and qb. The proposed SA methods are compared against existing β-method developed for coarse grained soil and validated using an independent pile load test dataset. Pile setup is observed in qs of piles driven in CG-IGMs, and pile relaxation is mostly observed in qb. Statistical assessment concludes that the proposed SA methods provide more accurate and consistent qs and qb predictions than that by the β-method.

Alterations of dynamic and static brain functional activities and integration in stroke patients

ObjectiveThe study aimed to investigate the comprehensive characteristics of brain functional activity and integration in patients with subcortical stroke using dynamic and static analysis methods and to examine whether alterations in brain functional activity and integration were associated with clinical symptoms of patients.MethodsDynamic amplitude of low-frequency fluctuation (dALFF), static amplitude of low-frequency fluctuation (sALFF), dynamic degree centrality (dDC), and static degree centrality (sDC) were calculated for 19 patients with right subcortical stroke, 16 patients with left subcortical stroke, and 25 healthy controls (HC). Furthermore, correlation analysis was performed to investigate the relationships between changes in brain functional measurements of patients and clinical variables.ResultsGroup comparison results showed that significantly decreased dALFF in the left angular (ANG_L) and right inferior parietal gyrus (IPG_R), decreased sALFF in the left precuneus (PCUN_L), and decreased sDC in the left crus II of cerebellar hemisphere (CERCRU2_L) and IPG_R, while significantly increased sDC in the right lobule X of cerebellar hemisphere (CER10_R) were detected in patients with right subcortical stroke relative to HC. Patients with left subcortical stroke showed significantly decreased sALFF in the left precuneus (PCUN_L) but increased sDC in the right hippocampus (HIP_R) compared with HC. Additionally, the altered sDC values in the CER10_R of patients with right subcortical stroke and in the HIP_R of patients with left subcortical stroke were associated with the severity of stroke and lower extremities motor function. A correlation was also found between the altered sALFF values in the PCUN_L of patients with left subcortical stroke and lower extremities motor function.ConclusionThese findings suggest that time-varying brain activity analysis may supply complementary information for static brain activity analysis. Dynamic and static brain functional activity and integration analysis may contribute to a more comprehensive understanding of the underlying neuropathology of dysfunction in stroke patients.

Nonlinear Static Analysis for Seismic Evaluation of Existing RC Hospital Building

Nonlinear Static Analysis otherwise known as pushover analysis will be used in this study to evaluate the seismic performance of an existing reinforced concrete (RC) hospital structure. This method aids in determining the structure’s ability to withstand lateral loads and calculating its local and global deformation requirements. The study begins with a thorough analysis of the geometry, materials, and structural elements of the structure, followed by a review of pertinent building regulations and codes. A finite element model in three dimensions of the hospital building is created, encapsulating the main features of the structure’s behavior under seismic loading. The lateral force method of analysis and static pushover analysis is then carried out and compared, and the findings are used to pinpoint crucial weak places, potential failure mechanisms, and regions needing additional research or fortification. Recommendations are given to improve the seismic performance of the current RC hospital building based on the pushover analysis’s findings. These adjustments can be made to the structural system via retrofitting techniques or to non-structural elements. For engineers, architects, and legislators concerned with the seismic assessment and renovation of hospital buildings and other crucial infrastructure, the findings from this study are valuable.

ANALISIS DAN DETEKSI MALWARE PADA PROTOKOL JARINGAN MENGGUNAKAN METODE MALWARE ANALISIS DINAMIS DAN MALWARE ANALISIS STATIS

Network security is a crucial issue in the rapidly developing era of information technology. One of the main threats is malware, which is malicious software that harms systems and data. To solve this problem, analysis and detection of malware on network protocols need to be improved using effective methods. This research explores the application of two main methods, namely static and dynamic malware analysis. First, static malware analysis involves examining malware files without running them. This method includes analysis of signatures, heuristics, and malicious code to identify typical malware patterns in files on the network.Second, dynamic malware analysis executes malware in an isolated environment (sandbox) to monitor its behavior and impact on the system. This process allows detection of malicious changes and attempts by malware to propagate itself. By combining these two methods, a holistic and efficient approach in malware detection on network protocols can be achieved. It is hoped that the results of this research can improve network security, protect infrastructure from ever-evolving malware threats. The use of static and dynamic analysis methods will help deal with increasingly complex security challenges, enabling networks to operate more securely and reliably.