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Advancing Web Browser Forensics: Critical Evaluation of Emerging Tools and Techniques

SN Computer Science 2025年第4期6卷 1-28页

作者： Chand, Rishal Ravikesh Sharma, Neeraj Anand Kabir, Muhammad Ashad Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji School of Computing Mathematics and Engineering Charles Sturt University Bathurst 2795 NSW Australia

As the use of web browsers continues to grow, the potential for cybercrime and web-related criminal activities also increases. Digital forensic investigators must understand how different browsers function and the critical areas to consider during web forensic analysis. Web forensics, a subfield of digital forensics, involves collecting and analyzing browser artifacts, such as browser history, search keywords, and downloads, which serve as potential evidence. While existing research has provided valuable insights, many studies focus on individual browsing modes or limited forensic scenarios, leaving gaps in understanding the full scope of data retention and recovery across different modes and browsers. This paper addresses these gaps by defining four browsing scenarios and critically analyzing browser artifacts across normal, private, and portable modes using various forensic tools. We define four browsing scenarios to perform a comprehensive evaluation of popular browsers—Google Chrome, Mozilla Firefox, Brave, Tor, and Microsoft Edge—by monitoring changes in key data storage areas such as cache files, cookies, browsing history, and local storage across different browsing modes. Overall, this paper contributes to a deeper understanding of browser forensic analysis and identifies key areas for enhancing privacy protection and forensic methodologies. © The Author(s) 2025.

关键词： Browser artifacts Browser forensics Cybercrime Digital forensics Forensics tool Web browser

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A comparative study of potential-flow-based numerical models to experimental tests of a semi-submersible floating wind turbine platform 33rd

A comparative study of potential-flow-based numerical models...

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33rd International Ocean and Polar engineering Conference, ISOPE 2023

作者： Holcombe, Anna Edwards, Emma Tosdevin, Tom Greaves, Deborah Hann, Martyn School of Engineering Computing and Mathematics University of Plymouth United Kingdom

ISBN: (纸本)9781880653807

This contribution to the CCP-WSI FOWT comparative study presents numerical results from three popular potential-flow-based models, including OpenFAST, Orcaflex and WEC-Sim. The subject of the study is the VolturnUS-S semi-submersible platform with the IEA 15MW reference wind turbine. Decay tests and focused waves are simulated in the numerical models, and these are compared to experimental results, using a 1:70 scale model of the system. Comparisons between the numerical models and physical experiments are made. Results of surge motion response and line tension are presented for the decay tests and focused wave tests. The numerical models are seen to underpredict surge response in the low frequency regions of the extreme focused wave test for all three numerical models and of the operational focused wave test for WEC-Sim. Adding an empirically tuned coefficient proportional to the relative velocity between the platform and the water particles in WEC-Sim is seen to improve results in surge motion response significantly, resulting in better agreement than any of the other numerical models. Mooring line tensions are also compared in this study, and an underprediction is observed during the extreme focused wave test for the fore and aft mooring lines, including in the wave-frequency region. © 2023 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Semisubmersibles

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A fast and accurate model for the creation of explosion fragments with improved fragment shape and dimensions

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Defence Technology（防务技术） 2022年第2期18卷 159-169页

作者： David Felix Ian Colwill Paul Harris School of Computing Engineering&MathematicsUniversity of BrightonBrightonUK Engineering and Design(Alumni) University of SussexBrightonUK

Explosion models based on Finite Element Analysis(FEA)can be used to simulate how a warhead *** their execution times are *** protection systems need to make very fast predictions,before a fast attacking weapon hits the *** execution times are also needed in real time simulations where the impact of many different computer models is being ***,FEA explosion models are not appropriate for these real-time *** research presented in this paper delivers a fast simulation model based on Mott’s equation that calculates the number and weight of fragments created by an *** addition,the size and shape of fragments,unavailable in Mott’s equation,are calculated using photographic evidence and a distribution of a fragment’s length to its *** model also identifies the origin of fragments on the warhead’s *** results are verified against experimental data and a fast execution time is achieved using uncomplicated simulation *** developed model then can be made available for real-time simulation and fast computation.

关键词： Distribution of fragments Fragment shape Real-time simulation Cylindrical explosion

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An Advanced Clustering with Toeplitz and Circulant Matrices and a Comparison Against Spectral Clustering, and K-Means 3

An Advanced Clustering with Toeplitz and Circulant Matrices ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Saragadam, Sneha Rohan, Gamidi Manideep, Thotapalli Sri Surya Jayan, Sarada Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331518592

In an effort to increase computing efficiency, this work presents an improved spectral clustering technique that approximates the affinity matrix using Toeplitz and Circulant matrices. To preserve the quality of the clustering, we take advantage of the spectral characteristics of the similarity graph's Laplacian matrix. The Iris flower, Wine Quality, and Mushroom datasets are used to assess the approach. The following six clustering methods are contrasted: PCA combined with either spectral or K-means clustering, K-means clustering (with and without matrix approximation), and spectral clustering (with and without matrix approximation). The Silhouette Score, Fl score, precision, recall, and Adj usted Rand Index (ARI) are used to evaluate performance. The suggested method produces similar results to traditional methods with a significant reduction in computational cost, according to the results. In order to fully investigate the advantages of these matrices, architectures in spectral clustering are explored. © 2024 IEEE.

关键词： K-means clustering

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Enhancing Image Representation and Compression: An Innovative Nz-Nqer Framework With Block Truncation Quantum Coding

Enhancing Image Representation and Compression: An Innovativ...

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Haque, Md Ershadul Paul, Manoranjan Tohidi, Faranak Ulhaq, Anwar Debnath, Tonmoy Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia Charles Sturt University School of Computing Mathematics and Engineering NSW2795 Australia

ISBN: (纸本)9798350382204

Efficiently representing and compressing large-scale images in quantum circuits remains a challenge due to increasing circuit complexity. This paper proposes an innovative approach called NZ-NEQR (Non-Zero Novel Quantum Representation) to tackle this issue. Eliminating zero connections from the existing NEQR approach significantly reduces the complexity of state label preparation. The proposed NZ-NEQR approach achieves remarkable improvements in both image representation and compression, leading to a substantial reduction in required bits per pixel. To further enhance compression, Block Truncation Coding (BTC) is adopted as a compression scheme, which effectively truncates images into two labels suitable for quantum compression and reconstruction. Notably, the proposed method requires fewer bits per pixel compared to existing NEQR approaches. Additionally, NZ-NEQR combined with BTC employs only 5 qubits for representing a $1024 \times 1024$ image, and compression using a $4 \times 4$ quantum BTC block represents a significant advancement. Computational results demonstrate the superior effectiveness of the proposed NZ-NEQR approach combined with BTC for quantum image representation and compression, surpassing the performance of NEQR and JPEG approaches. © 2023 IEEE.

关键词： Image representation

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Adapted Runge-Kutta Methods for Nonlinear First-Order Delay BVPs with Time-variable Delay

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Acta Mathematicae Applicatae Sinica 2025年第2期41卷 400-413页

作者： Cheng-jian ZHANG Yang WANG Hao HAN School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

This paper deals with numerical solutions for nonlinear first-order boundary value problems(BVPs) with time-variable delay. For solving this kind of delay BVPs, by combining Runge-Kutta methods with Lagrange interpolation, a class of adapted Runge-Kutta(ARK) methods are developed. Under the suitable conditions, it is proved that ARK methods are convergent of order min{p, μ+ν +1}, where p is the consistency order of ARK methods and μ, ν are two given parameters in Lagrange interpolation. Moreover, a global stability criterion is derived for ARK methods. With some numerical experiments, the computational accuracy and global stability of ARK methods are further testified.

关键词： delay boundary value problems adapted Runge-Kutta method Lagrange interpolation error analysis global stability

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Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, C.H. Hari Jayan, Sarada Amrita School of Computing Department of Computer Science Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331538538

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering. © 2025 IEEE.

关键词： K-means clustering

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Sherpasec: A Case Study on the Effectiveness of Cybersecurity Training by a Volunteer-Led Community-Based Professional Society in Malaysia 1

Sherpasec: A Case Study on the Effectiveness of Cybersecurit...

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1st International Conference on Cyber Security and computing, CyberComp 2024

作者： Khoo, LiJing Chin, Ji-Jian School of Computing and Creative Media University of Wollongong Malaysia Glenmarie Campus Malaysia School of Computing Engineering and Mathematics University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9798350387728

One of the ways to proliferate cybersecurity education and improve the quality of entry-level graduates in cybersecurity training would be participation in professional societies. These societies range from government-run societies like MBOT and societies with memberships based on professional certifications like SANS, ISACA and ISC2. Finally, there are volunteer-led community-based professional societies such as SherpaSec that are without pre-requisites, where only interest is required. This position paper evaluates the effectiveness in bridging public/private partnerships that such a volunteer-led society enables. We study SherpaSec, a volunteer-led cybersecurity community in Malaysia as the main case study subject. This paper aims to present an evaluation of the society's effectiveness in cultivating a sustainable cybersecurity ecosystem in a nation, namely Malaysia. As a spin-off of NanosecEdu, a platform dedicated to nurturing next-gen cybersecurity talent, SherpaSec bridges the gap between academia and industry through monthly meetups and activities. The community is integral to organizing and participating in various cybersecurity events, including the international Global Cybersecurity Camp (GCC), the Malaysian Cybersecurity Camp (MCC), and the Asian Cybersecurity Challenge (ACSC). SherpaSec also endorses SKRCTF, a Capture-The-Flag platform designed to introduce beginners to cybersecurity challenges. Through these initiatives, we state SherpaSec's contributions to building a robust cybersecurity talent pipeline, fostering industry-academia collaboration, and promoting knowledge sharing. The paper investigates how SherpaSec's multifaceted approach impacts cybersecurity education, research, and practice in Malaysia. © 2024 IEEE.

关键词： Economic and social effects

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Machine Learning for Enhanced Underwriting: Predicting Premiums in Health Insurance 4

Machine Learning for Enhanced Underwriting: Predicting Premi...

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Abdelshafy, Nada Abdelshafy, Mohamed Ghazanfar, Mustansar Ali Saglam, Rahime Belen University of East London School of Architecture Computing and Engineering London United Kingdom Coventry University School of Computing Mathematics and DataScience Coventry United Kingdom

ISBN: (纸本)9798331520038

In the evolving field of Insurance, Machine Learning (ML) has emerged as a transformative tool for enhancing the accuracy and efficiency of insurance underwriting, particularly in health insurance, a sector experiencing increased demand post-pandemic. This research develops an ML model aimed at improving premium prediction within this domain. Utilizing a health insurance database from Kaggle, we conducted a comparative analysis of several ML models - XGBoost, Random Forest, Neural Network Regression, and a baseline Linear Regression - to establish performance benchmarks. Our methodology included rigorous data analysis and application of ML model optimization techniques such as feature engineering and selection, hyperparameter tuning through Grid Search, Random Search, and Bayesian Optimization, as well as overfitting prevention strategies like Pruning and Early Stopping. The XGBoost model demonstrated superior performance, achieving a Mean Absolute Error (MAE) of 2582.3, Root Mean Square Error (RMSE) of 4625.0, and an R2 value of 86.59%. This research not only advances the application of ML in predictive premium pricing but also provides a structured approach for future studies in the insurance industry's underwriting processes. © 2024 IEEE.

关键词： Health insurance

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Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 1-16页

作者： Li, Yuerong Zhang, Yuhua Che, Jinxing School of Science Nanchang Institute of Technology Jiangxi Nanchang China Key Laboratory of Engineering Mathematics and Advanced Computing Nanchang Institute of Technology Jiangxi Nanchang China

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k ∗ N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. © 2024 - IOS Press. All rights reserved.

关键词： K-means clustering

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