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Entropy-Driven Visualization in GView: Unveiling the Unknown in Binary File Formats

Entropy-Driven Visualization in GView: Unveiling the Unknown...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Andrada-Livia Antoneac Gheorghiţă Mutu Dragoş-Teodor Gavriluţ Al. I. Cuza University Bitdefender Iaşi România

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

the number of known malicious samples has increased exponentially in the last decade, making it more complicated for a security researcher to identify and cluster them quickly. While for most scenarios, most data viewers relate to the file type (either binary or textual) to extract meaningful data, the protective measurements of different malicious payloads may make this task difficult. Our research introduces an approach that develops an enhanced visualization mode within the open-source framework GView to address this challenge. It harnesses established entropy-based analytical principles to facilitate the identification of anomalies and intrinsic properties in binary data, irrespective of specific file formats. this methodology streamlines threat evaluation and contributes to the broader field of cybersecurity by offering a building block for a scalable solution for analyzing the everexpanding volumes of digital information.

关键词： scientific computing Data visualization Data mining Computer security Payloads Digital information

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Concepts Involved in Creating an Interactive Viewer for Disassembly

Concepts Involved in Creating an Interactive Viewer for Disa...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Raul Zaharia Al. I. Cuza University & Bitdefender Iasi Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Disassembly is the process that translates machine code into a higher-level and more human-readable form. From the security analysis and malware detection perspective, it is an essential technique. However, traditional disassembly tools often display the information in a textual format, thus making it difficult to navigate and understand. In this paper, we explore the concepts involved in creating a viewer for disassembly that focuses on static analysis and boosts the user experience like clearly displaying the code, showcasing relevant parts, guiding the user, offering an interactive experience, map data structures and seamless integration with other tools. Furthermore, we emphasize the importance of having both a user-friendly and also customizable interface without having to concede on overall speed and performance. In this manner, even a novice in security analysis could have a positive experience. Considering all these features, the final result leads to a more productive and efficient analysis.

关键词： Codes Navigation scientific computing Static analysis Data structures User experience Malware Security

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Control Flow Graphs Against Malware: Methods of Analysis and Detection

Control Flow Graphs Against Malware: Methods of Analysis and...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Prejban Mircea-George Ciprian Pungila Faculty of Mathematics and Computer Science West University of Timisoara Timisoara Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

One key component of a cyberattack is malware. If a mal ware program is detected and blocked the cyberattack may stagger or fail, thus bad actors design their mal ware programs with additional characteristics and functionalities that harden the analysis of the mal ware program and make the malware undetectable by antivirus solutions. With the appearance of more advanced malware new detection methods are needed. With the help of reverse engineering techniques and software engineering concepts, one model that analysts can work with is Control Flow Graphs. Used for software optimization, control-flow-graphs offer the advantage of graph properties for analysts to detect malicious particularities in malware samples. this paper explores some methods of detection and analysis based on control flow graphs, categorizes them in four categories and highlights different particularities in these approaches.

关键词： Analytical models scientific computing Reverse engineering Focusing Market research Malware Flow graphs Cyberattack Optimization Software engineering

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Novel Data Adaptation Techniques for Enhanced Lung Cancer Detection in CT Scans

Novel Data Adaptation Techniques for Enhanced Lung Cancer De...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Cătălin-Mihail Chiru Mihai Nan Mihai Trăscău Adina Magda Florea Computer Science Department National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Lung cancer persists as a global leader in cancer-related deaths, highlighting the critical need for precise and efficient detection methods. this paper investigates the use of the Medical Segmentation Decathlon dataset to train neural networks for lung cancer segmentation in CT scans via semantic segmentation. We propose and evaluate four new data adaptation techniques specifically designed for this dataset, with each technique being assessed using U-Net-based architectures. Our approach incorporates a thorough exploratory data analysis to uncover the dataset's strengths and weaknesses, which in turn guided our data preprocessing and augmentation strategies.

关键词： Training Accuracy scientific computing Lungs Computed tomography Semantic segmentation Lung cancer Medical diagnostic imaging Standards Tumors

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the NLP for Employee Review Sentiment Analysis: An Explainable Perspective

The NLP for Employee Review Sentiment Analysis: An Explainab...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Lepa Babic Ruxandra Stoean Jelena Cadjenovic Luka Jovanovic Miodrag Zivkovic Nebojsa Bacanin Department of Business Economics Singidunum University Belgrade Serbia Faculty of Sciences University of Craiova Craiova Romania Faculty of Technical Sciences Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Main goal of the research presented in this manuscript is to analyze employees satisfaction by employing hybrid methods between machine learning and metaheuristics. Furthermore, the proposed work utilizes term frequency-inverse document frequency (TF-IDF), natural language processing (NLP) method, for the identification of important segments of the employees' statements towards their workplaces, while the extreme gradient boosting (XGBoost) model is used for predicting the outcomes. Since each machine learning technique should be calibrated to particular problem, the XGBoost has been optimized by a modified swarm metaheuristics based on red fox optimizer (RFO) algorithm and its performance is evaluated against other high-performing swarm intelligence approaches. the results indicate the superiority of proposed solution, which has been further evaluated with the Shapley additive explanations (SHAP) analysis.

关键词： Sentiment analysis Machine learning algorithms scientific computing Reviews Computational modeling Metaheuristics Employment Predictive models Prediction algorithms Particle swarm optimization

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Scriptview: BTC Embedded Plaiform® Extension for Test Case Implementation

Scriptview: BTC Embedded Plaiform® Extension for Test Case ...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Petru F. Mihancea Leif Driebold Dorian Blaj Mihai I. Bogdănescu Răzvan T. Lăpădat Roxana C. Ciorica Andrei A. Onişa Andreea M. Zamosteanu Tom Bienmüller BTC Embedded Systems Romania SRL Timişoara Romania Politehnica University of Timişoara Romania BTC Embedded Systems AG Oldenburg Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Testing and validating embedded software is paramount when it comes to safety-critical systems. BTC EMBEDDEDPlaiform@ supports these activities using various approaches including test case definition means. In this system description we present Scriptview,a domain-specific language designed to support test case implementation in a simple programmatic way. the language has been integrated in our platform and can be used in production by our customers.

关键词： scientific computing Software algorithms Production Embedded software Domain specific languages Testing

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A Study on the Topic of Crossing Minimisation

A Study on the Topic of Crossing Minimisation

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Rares-Ioan Mateiu Alexandru Popa University of Bucharest ICI - National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

In this article we undertake a rigorous study on several problems related to the topic of reordering the order of the leaves of a tree according to a specific function and we come up with our own discoveries on the topic. Our study has its roots in tanglegrams, which represent a pair of trees on the same set of leaves with the matching leaves in the two trees joined by an edge. the primary focus of our article is on two distinct approaches for transforming the initial leaf order to match the input order: One Tree Crossing Minimization (OTCM) and One Tree Drawing by Deleting Edges (OTDE). To be more precise, we get a specific order as input and the purpose is to match the initial order of the tree as much as possible with the input order. Both problems are NP Hard, and we present approximation algorithms for each. Moreover, we explore efficient solutions for the isolated case of binary trees. Our own results include providing 3 approximation algorithms for both OTDE and OTCM and displaying that OTDE is hard to approximate within a factor of 4/5 - ∊. Last but not least, we compose a literature survey, studying related topics in the field of crossing minimisation as we compare the solutions and their efficiency with OTDE and OTCM.

关键词： Surveys Humanities scientific computing Impedance matching Data visualization Binary trees Games Minimization Approximation algorithms Bioinformatics

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Frequency Generation for a Quantum Current Sensor in Smart Grids

Frequency Generation for a Quantum Current Sensor in Smart G...

引用

international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Frederik Hoffmann Ann-Sophie Bülter Ludwig Horsthemke Dennis Stiegekötter Jens Pogorzelski Peter Glösekötter Markus Gregor Department of Electrical Engineering and Computer Science FH Münster—University of Applied Sciences Steinfurt Germany Department of Engineering Physics FH Münster—University of Applied Sciences Steinfurt Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Integrating renewable energy sources, electric vehicles, and storage systems into power grids demands advanced control and monitoring systems. Precise current sensors are a critical component of these systems, essential for ensuring reliable and efficient electricity distribution. Quantum sensors, particularly those based on nitrogen vacancy (NV) centers in diamond, provide a promising solution for high-precision current measurement. this document introduces an innovative method for generating microwave signals using direct digital synthesis (DDS) and field-programmable gate arrays (FPGAs) to drive multiple signals.

关键词： Renewable energy sources Quantum computing scientific computing Sensor systems Smart grids Reliability Nitrogen Field programmable gate arrays Quantum sensing Monitoring

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MACE: Malware Analysis and Clustering Engine

MACE: Malware Analysis and Clustering Engine

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Andrei Zaborilă Mihaela-Elena Breaban Dorel Lucanu Faculty of Computer Science Alexandru Ioan Cuza University of Iasi Romania

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Analysing malicious files is a lengthy task and, given a set of hundreds, even thousands of files, a security researcher would have to spend multiple hours looking at each sample. Considering that new malware is created every day, a way to reduce the analysis time is mandatory. this paper presents the Malware Analysis and Clustering Engine (MACE), a solution implemented to address this problem. It offers the user a modular framework implementing multiple feature extraction methods and clustering algorithms, which can provide a multitude of clustering configurations. the users can experiment with differ-ent configurations and choose one that best suits their scenario, obtaining a clustering result that allows them to analyse only a single sample from each cluster and draw a conclusion both for it and all its peers. this approach significantly accelerates the analysis process. the engine was tested on four different datasets, each trying to exemplify a scenario that is likely to be encountered in real situations. the results indicate that MACE does achieve its goal, providing multiple configurations for each dataset that reach both great accuracy and a number of clusters that is close to the one that was expected.

关键词： Accuracy scientific computing Clustering algorithms Feature extraction Malware Security Flow graphs Engines

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Optimizing Deep Learning Models for Object Detection

Optimizing Deep Learning Models for Object Detection

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Calin-George Barburescu Gabriel Iuhasz West University of Timisoara Timisoara Romania

ISBN: (数字)9781728176284

ISBN: (纸本)9781728176291

Deep learning models for object detection have gotten larger and larger over the years, spanning from 3.9M trainable parameters for EfficientDet to 209M for the AmoebaNet-based NAS-FPN detector. Different strategies are currently being researched in order to improve the efficiency of deep learning models for object detection, one of which is running the training and inference of the neural network in low precision. Interesting results have been achieved by researchers, starting from the original paradigm of using operators and doing the necessary operations in IEEE single precision (FP32), to achieving similar accuracies of the models using custom minifloat formats (FP8). the results can be pushed even further by using genetic algorithms for hyperparameter tuning, in order to find specific hyperparameter for the FP8 version of the model. In this paper, we will present the results of our experiments utilizing YOLOv3 with hybrid floating-point format (HFP8). One of the experiments in this paper shows how our solution can be used for checking social distancing guidelines which is a very important topic in the current COVID-19 pandemic.

关键词： Deep learning Training scientific computing Pandemics Object detection Social factors Tuning

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