Reverse Engineering Problem Research Articles

Exploring gender dynamics in cybersecurity education: A self-determination theory and social cognitive theory perspective

The cybersecurity industry is facing two major challenges as cyber threats get more serious: a shortage of skilled workers to defend against these attacks and a noticeable underrepresentation of women in the field, underscoring the critical need for diversity and skill development. This research explores variations in cybersecurity learning based on gender using the frameworks of self-determination and social cognitive theories. Conducted over two years, the research utilized a Capture The Flag (CTF) platform to facilitate learning and assess outcomes. This study explores the gender disparities in key areas such as intrinsic and extrinsic motivation, self-efficacy, conceptual grasp, and performance across five distinct cybersecurity subdomains: web exploitation, cryptography, cyber forensics, binary exploitation, and reverse engineering. The comprehensive evaluation included participant interaction with learning materials, their adeptness in practical exercises, and their achievements in a comprehensive contest covering five essential cross-domain cybersecurity concepts. Pre- and post-study surveys were instrumental in tracking shifts in motivation and self-efficacy levels. Findings pointed to notable gender-based differences in motivational aspects and self-belief following the learning phase, with conceptual understanding and the ability to replicate challenges also varying significantly between genders, highlighting disparities in educational outcomes. Domain-based analysis reveals notable gender differences: male participants excelled in solving reverse engineering and cryptography problems, whereas female participants performed better in cyber forensics and web exploitation challenges. These findings underscore the importance of tailored educational strategies in cybersecurity training to bridge the gender gap and foster a more diverse and capable workforce, addressing both the skill shortage and the need for broader representation in the field.

Analysis of ChatGPT's capabilities for solving problems of reverse-engineering of software

The article presents the results of the analysis and evaluation of the capabilities of the open version of the chatbot with artificial intelligence ChatGPT 3.5 for solving typical problems of software reverse engineering. Three classes of reverse engineering tasks were selected for analysis: source code analysis, binary code analysis, and data models analysis. In each class of tasks, the most typical tasks were selected taking into account the limitations of ChatGPT regarding the processing of graphical models and the amount of input and output data, and sets of test tasks were developed for each task. As an approach to assessment, an approach similar to the assessment of competencies of higher education applicants after studying the relevant discipline was chosen. The following were considered as criteria for evaluating answers to test tasks: correctness (rightness, coincidence with expectations); completeness (obtaining the final result); accuracy (solving the task without additional questions); reasonableness (availability of explanations and answers to questions). The following scale was used and described for evaluations: excellent, very good, good, satisfactory, sufficient, unsatisfactory. During the testing for each test task, tasks statement and all necessary data were entered through the ChatGPT interface. As a result of the analysis, it was determined that ChatGPT better solves the problems of source code analysis (excellent and very good grades were obtained for semantic and structural analysis, restoration of mathematical support, quality assessment, security audit and refactoring, as well as for conversion to another programming language), tasks of decompilation of IDA pseudocode into complete C source code, tasks of reverse engineering of relational databases, and tasks of generating YARA rules for recognizing file formats. Unsatisfactory grades were obtained for dynamic analysis of assembly code and determination of binary file structures of non-standard formats. ChatGPT solves the rest of the problems well and satisfactorily, but requires checking the results, clarifying queries and prompts, as well as manual error correction in some cases. ChatGPT errors were observed when analyzing binary data represented by hexadecimal symbols, as well as errors in generated scripts for programming in IDA. On the basis of the set grades, conclusions were made regarding the expediency, possibility, or impracticality of using ChatGPT to solve each type of software reverse engineering problems, and appropriate recommendations were also provided. Prospects for further research include testing new versions of ChatGPT and other similar artificial intelligence systems regarding the capabilities of analyzing and synthesizing graphical models of software.

Robust Reconstruction of Closed Parametric Curves by Topological Understanding with Persistent Homology

Curve reconstruction is a fundamental problem in reverse engineering, which has intrigued researchers for decades. In this paper, we propose a topological understanding based method for reconstructing parametric curves robustly from unorganized point clouds. Given a point cloud, we firstly understand the number of closed curves which need to be reconstructed using persistent homology. Then, by calculating the persistent 1-cycles of the point cloud, the initial shapes of the reconstructed parametric curves are generated. Finally, the closed parametric curves are reconstructed with the weighted least-squares progressive iterative approximation (W-LSPIA) method. Due to the topological understanding, the reconstructed parametric curves are faithful to the salient topological structure of the point cloud. Moreover, the developed reconstruction method is robust, and the reconstructed curve is much less affected by noise points and outliers, compared with the conventional parametric curve reconstruction algorithms. Experimental results demonstrated in this paper show the effectiveness of the developed curve reconstruction method.

Searching for explanations of black-box classifiers in the space of semantic queries

Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.

A Conceptual Alternative Machine Learning-Based Method for Mesh Sensitivities Calculation in a Turbomachinery Blades Optimisation Framework

In gradient-based aerodynamic optimisation, the functional gradient required by the optimiser can be obtained as a product of adjoint-based functional sensitivities to volume grid nodes and the volume mesh sensitivities to the design parameters. For turbomachinery applications, it is desirable to use the actual blade design variables as degrees of freedom for the optimisation process, but this can lead to tedious programming tasks. As an alternative, a Machine Learning (ML) model is created to mimic and differentiate the blade geometry and the mesh generation processes. The typical ML forward pass is followed by a back differentiation operation enabling the computation of the volume mesh derivatives with respect to design parameters. The model is tested by comparing the ML-predicted and reference grid, and the modelled sensitivities are verified through algorithmic differentiation. The modelled mesh sensitivities are successfully employed for adjoint-based reverse engineering and design optimisation problems on a turbine blade.

Physically unclonable functions taggant for universal steganographic prints

Counterfeiting of financial cards and marketable securities is a major social problem globally. Electronic identification and image recognition are common anti-counterfeiting techniques, yet they can be overcome by understanding the corresponding algorithms and analysis methods. The present work describes a physically unclonable functions taggant, in an aqueous-soluble ink, based on surface-enhanced Raman scattering of discrete self-assemblies of Au nanoparticles. Using this stealth nanobeacon, we detected a fingerprint-type Raman spectroscopy signal that we clearly identified even on a business card with a pigment mask such as copper-phthalocyanine printed on it. Accordingly, we have overcome the reverse engineering problem that is otherwise inherent to analogous anti-counterfeiting techniques. One can readily tailor the ink to various information needs and application requirements. Our stealth nanobeacon printing will be particularly useful for steganography and provide a sensitive fingerprint for anti-counterfeiting.

COMPARISON OF ALGORITHMS FOR DETERMINING THE THICKNESS OF OPTICAL COATING LAYERS BASED ON THE MONOCHROMATIC MONITORING DATA

The reverse engineering problem of determining the layer thicknesses of deposited optical coatings from on-line monochromatic measurements is considered. To solve this inverse problem, non-local algorithms are proposed that use all the data accumulated during the deposition process. For the proposed algorithms, the accuracy of solving the inverse problem is compared in the presence of random and systematic errors. It is shown that in the case when the measured data contains only random errors, the best accuracy is provided by the algorithm based on minimizing the discrepancy functional. In the case of systematic errors, the advantage of one the algorithms based on minimizing the variance functionals is demonstrated. Key words: inverse problems, reverse engineering, optical coatings, thin films.

Estimation of Correct Long-Seam Mismatch Using FEA to Compare the Measured Strain in a Non-Destructive Testing of a Pressurant Tank

This paper discusses the design criterion of a pressurant steel tank made of HSLA 15CDV6 and proof pressure test (PPT) as a non-destructive examination. An inverse Ramberg-Osgood relation is used to represent the stress-strain curve of the material. Elasto-plastic finite element analysis (FEA) has been carried out to examine the adequacy of the design. Experimental stress analysis has been carried out from the measured strains and found maximum effective stress is at LS joint (max. measured strain location). Strain obtained from FEA is compared reasonably well with the proof pressure test (PPT) data at most of the strain gauge locations except at one long-seam (LS) joint. So, to explain the causes of difference in strains near one LS, parametric studies have been performed in a 3D FEA with varying LS mismatch to find the correct mismatch as a reverse engineering problem. It is found that a mismatch value of 0.9 mm will give the required strain at PPT, which is measured only 0.4 mm. The failure pressure estimated through nonlinear FEA/analytical expressions found to meet the design.

Hybrid Obfuscation Technique for Reverse Engineering Problems

Hybrid Obfuscation Technique for Reverse Engineering Problems

Modeling-Guided Design of Paper Microfluidic Networks: A Case Study of Sequential Fluid Delivery.

Paper-based microfluidic devices are popular for their ability to automate multistep assays for chemical or biological sensing at a low cost, but the design of paper microfluidic networks has largely relied on experimental trial and error. A few mathematical models of flow through paper microfluidic devices have been developed and have succeeded in explaining experimental flow behavior. However, the reverse engineering problem of designing complex paper networks guided by appropriate mathematical models is largely unsolved. In this article, we demonstrate that a two-dimensional paper network (2DPN) designed to sequentially deliver three fluids to a test zone on the device can be computationally designed and experimentally implemented without experimental trial and error. This was accomplished by three new developments in modeling flow through paper networks: (i) coupling of the Richards equation of flow through porous media to the species transport equation, (ii) modeling flow through assemblies of multiple paper materials (test membrane and wicking pad), and (iii) incorporating limited-volume fluid sources. We demonstrate the application of this model in the optimal design of a paper-based signal-enhanced immunoassay for a malaria protein, PfHRP2. This work lays the foundation for the development of a computational design toolbox to aid in the design of paper microfluidic networks.

Neural reverse engineering of stripped binaries using augmented control flow graphs

We address the problem of reverse engineering of stripped executables, which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code patterns arising from compiler optimizations. We present a novel approach for predicting procedure names in stripped executables. Our approach combines static analysis with neural models. The main idea is to use static analysis to obtain augmented representations of call sites; encode the structure of these call sites using the control-flow graph (CFG) and finally, generate a target name while attending to these call sites. We use our representation to drive graph-based, LSTM-based and Transformer-based architectures. Our evaluation shows that our models produce predictions that are difficult and time consuming for humans, while improving on existing methods by 28% and by 100% over state-of-the-art neural textual models that do not use any static analysis. Code and data for this evaluation are available at https://github.com/tech-srl/Nero.

Reconstruction of Curve Networks from Unorganized Spatial Points

Curve network reconstruction from a set of unorganized points is an important problem in reverse engineering and computer graphics. In this paper, we propose an automatic method to extract curve segments and reconstruct curve networks from unorganized spatial points. Our proposed method divides reconstruction of curve networks into two steps: 1) detecting nodes of curve segments and 2) reconstructing curve segments. For detection of nodes of curve segments, we present a principal component analysis-based algorithm to obtain candidate nodes from unorganized spatial points and a Euclidean distance-based iterative algorithm to remove peripheral nodes and find the actual nodes. For reconstruction of curve segments, we propose an extraction algorithm to obtain the points on each of curve segments. We present quite a number of examples which use our proposed method to reconstruct curve networks from unorganized spatial points. The results demonstrate the effectiveness of our proposed method and its advantages of good automation and high reconstruction efficiency.

Classical system approach to reverse engineering

The paper presents one of the possible solutions of the reverse engineering problem from the point of view of the classical system approach, as well as highlights the direction for the development of a complex software product. This approach will make it possible to change the modern practice of reverse engineering, which is applied everywhere. This practice requires expert evaluation by operator teams on non-acceptance basis. The approach proposed by the authors will significantly minimize human participation in this process.

The Inerter: A Retrospective

This article provides an introduction and overview of the inerter concept and device. Careful attention is given to the distinction between the inerter as an ideal modeling element and devices that approximate the ideal behavior. The background is given to the formal definition of the inerter as a mechanical one-port with terminal forces proportional to the relative acceleration between them. Four major methods of construction are described and modeled. The discussion focuses particularly on the notion of terminals, the distinction between a device and an effect, sign reversals, back driving in geared systems, the conceptual aspects of the modeling step for inerter embodiments, and the problem of reverse engineering to discover a purpose. The article includes an analysis and discussion of the rotational inerter, a brief review of the ideas of passive network synthesis that led to the inerter concept, and an analysis and discussion of several examples of integrated mechanical devices. It concludes with an imaginary dialogue between the author and an interlocutor on the understanding and purpose of the inerter.

To the Development of Open Source Software for the Reconstruction of CAD Models

In this paper, we describe an open source software package aimed at solving reverse engineering problems for CAD models defined in polygonal form. We briefly discuss the main principles behind the new software, its architecture, and directions for its further development. The use of the software is illustrated by examples of a turbine blade. In the first example, the turbine blade is reconstructed automatically from a structured point cloud. Another example is the interactive reconstruction of the turbine blade from an unstructured surface triangulation. In both the cases, we use a surface skinning strategy enhanced by a curve fairing operator. We show that the modified skinning operator does not minimize the total bending energy of the surface, but yields a smooth patch where input inaccuracies are compensated for. The reconstruction result is a parametric model of the turbine blade where the design variables are the coordinates of the poles for each profile curve. The proposed software architecture can be used for partial or complete parameterization of reconstructed CAD models with the aim of their subsequent optimization.

Example-driven query intent discovery

Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for non-expert users, who typically lack language expertise and are unfamiliar with the details of the schema. Query by Example (QBE) methods offer an alternative mechanism: users provide examples of their intended query output and the QBE system needs to infer the intended query. However, these approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user's intent effectively. In this paper, we present SQuID, a system that performs semantic similarity-aware query intent discovery . Our work makes the following contributions: (1) We design an end-to-end system that automatically formulates select-project-join queries in an open-world setting, with optional group-by aggregation and intersection operators; a much larger class than prior QBE techniques. (2) We express the problem of query intent discovery using a probabilistic abduction model , that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user-intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state-of-the-art in the related query reverse engineering problem.

К разработке открытого программного обеспечения для реконструкции CAD-моделей

We describe an open source software package aimed at solving reverse engineering problems for CAD models defined in the polygonal form. We briefly discuss the main principles behind the new software, its architecture, and directions for further development. Examples of a turbine blade demonstrate the use of the software. In the first example, a turbine blade is reconstructed automatically from a structured point cloud. Another example is the interactive reconstruction of a turbine blade from an unstructured surface triangulation. In both cases, we use surface skinning strategy enhanced by a curve fairing operator. We illustrate by an example that the modified skinning operator does not minimize the total bending energy of the surface, but allows constructing a smooth patch where input inaccuracies are compensated. The reconstruction result is a parametric model of a turbine blade where the design variables are the coordinates of the poles of each profile curve. The presented software architecture can be used for partial or complete parameterization of the reconstructed CAD models aimed at their subsequent optimization.

Constructing 3D CSG Models from 3D Raw Point Clouds

AbstractThe Constructive Solid Geometry (CSG) tree, encoding the generative process of an object by a recursive compositional structure of bounded primitives, constitutes an important structural representation of 3D objects. Therefore, automatically recovering such a compositional structure from the raw point cloud of an object represents a high‐level reverse engineering problem, finding applications from structure and functionality analysis to creative redesign. We propose an effective method to construct CSG models and trees directly over raw point clouds. Specifically, a large number of hypothetical bounded primitive candidates are first extracted from raw scans, followed by a carefully designed pruning strategy. We then choose to approximate the target CSG model by the combination of a subset of these candidates with corresponding Boolean operations using a binary optimization technique, from which the corresponding CSG tree can be derived. Our method attempts to consider the minimal description length concept in the point cloud analysis setting, where the objective function is designed to minimize the construction error and complexity simultaneously. We demonstrate the effectiveness and robustness of our method with extensive experiments on real scan data with various complexities and styles.

Statistical Reconstruction of Class Hierarchies in Binaries

We address a fundamental problem in reverse engineering of object-oriented code: the reconstruction of a program's class hierarchy from its stripped binary. Existing approaches rely heavily on structural information that is not always available, e.g., calls to parent constructors. As a result, these approaches often leave gaps in the hierarchies they construct, or fail to construct them altogether. Our main insight is that behavioral information can be used to infer subclass/superclass relations, supplementing any missing structural information. Thus, we propose the first statistical approach for static reconstruction of class hierarchies based on behavioral similarity. We capture the behavior of each type using a statistical language model (SLM), define a metric for pairwise similarity between types based on the Kullback-Leibler divergence between their SLMs, and lift it to determine the most likely class hierarchy. We implemented our approach in a tool called ROCK and used it to automatically reconstruct the class hierarchies of several real-world stripped C++ binaries. Our results demonstrate that ROCK obtained significantly more accurate class hierarchies than those obtained using structural analysis alone.

Software Module Clustering Algorithm Using Probability Selection

Software module clustering problem is an important and challenging problem in software reverse engineering whose main goal is to obtain a good modular structure of the software system. The large complex software system can be divided into some subsystems that are easy to understand and maintain through the software module clustering. Aiming at solving the problem of slow convergence speed, the poor clustering result, and the complex algorithm, a software module clustering algorithm using probability selection is proposed. Firstly, we convert the software system into complex network diagram, and then we use the operation of merger, adjustment and optimization to get the software module clustering scheme. To evaluate the effectiveness of the algorithm, a set of experiments was performed on 5 real-world module clustering problems. The comparison of the experimental results proves the simplicity of the algorithm as well as the low time complexity and fast convergence speed. This algorithm provides a simple and effective engineering method for software module clustering problem.