User Browsing Research Articles

MIC: Memory analysis of IndexedDB data on Chromium-based applications

As Chromium-based applications continue to gain popularity, it is necessary for forensic investigators to obtain a comprehensive understanding of how they store and manage browsing artifacts from both filesystem and memory perspectives. In particular, the incognito mode developed in the current version of Chromium uses only physical memory to manage data related to active sessions. Therefore, handling physical memory is essential for tracking a user's browsing behaviour in incognito mode. This paper provides an in-depth examination of LevelDB, a lightweight key-value database utilized as Chromium's implementation for IndexedDB. In particular, we delve into the details of how IndexedDB data is managed through LevelDB, taking into account its low-level database file format. Furthermore, we thoroughly explore the possibility of residual data, both complete and incomplete, being retained as applications create and initialize IndexedDB-related data. Based on our research findings, we propose a systematic methodology for inspecting the internal structures of LevelDB-related C++ classes, carving these structures from binary streams, and interpreting the data for forensic analysis. In addition, we develop a proof-of-concept tool based on our approach and demonstrate its performance and effectiveness through case studies.

What Does It Mean to Be Creepy? Responses to Visualizations of Personal Browsing Activity, Online Tracking, and Targeted Ads

Internet companies routinely follow users around the web, building profiles for ad targeting based on inferred attributes. Prior work has shown that these practices, generally, are creepy—but what does that mean? To help answer this question, we substantially revised an open-source browser extension built to observe a user's browsing behavior and present them with a tracker's perspective of that behavior. Our updated extension models possible interest inferences far more accurately, integrates data scraped from the user's Google ad dashboard, and summarizes ads the user was shown. Most critically, it introduces ten novel visualizations that show implications of the collected data, both the mundane (e.g., total number of ads you've been served) and the provocative (e.g., your interest in reproductive health, a potentially sensitive topic). We use our extension as a design probe in a week-long field study with 200 participants. We find that users do perceive online tracking as creepy—but that the meaning of creepiness is far from universal. Participants felt differently about creepiness even when their data presented similar visualizations, and even when responding to the most potentially provocative visualizations—in no case did more than 66% of participants agree that any one visualization was creepy.

TIN-X version 3: update with expanded dataset and modernized architecture for enhanced illumination of understudied targets.

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

Browser‐in‐the‐middle attacks: A comprehensive analysis and countermeasures

AbstractA browser‐in‐the‐middle (BITM) attack occurs when an attacker intercepts and manipulates communication between a user's web browser and the website they are attempting to visit. This approach is risky, as it allows the attacker to eavesdrop on crucial information, such as login passwords or financial details, leading to unauthorized access and potential data breaches. Moreover, the attacker can inject malicious content into the conversation, compromising the integrity of the data shared between the user's browser and the website. The article delves into the BITM attack as a strategy employed in phishing assaults. The article emphasizes precautionary measures to safeguard against BITM attacks. Additionally, it offers insights into how users can defend themselves and respond to an attack. The significance of user awareness in avoiding phishing attempts is a key highlight of the article.

Recommender System in E-Commerce

In the realm of e-commerce, recommendation systems play a pivotal role in guiding users towards relevant products. However, existing systems often grapple with inefficiencies in handling large datasets and fail to deliver personalized recommendations tailored to individual preferences. Addressing these challenges, the study introduces an innovative approach leveraging graph databases to enhance the performance of e- commerce recommendation systems. Through comprehensive analysis, the study delves into four critical aspects: database comparison, user exploration frequency across product categories, the diversity of available category types, and user browsing history analysis. This investigation unveils Neo4j's superior efficiency over MySQL in managing extensive datasets, laying the groundwork for more robust recommendation engines. By scrutinizing user behaviour patterns, the recommender system predicts preferences with precision, promising a tailored and gratifying shopping experience for users. Moreover, with support for a diverse array of category types, users gain flexibility in exploring products based on varied criteria, addressing a crucial need in the market for personalized shopping experiences. Leveraging insights gleaned from user browsing history, the system delivers refined recommendations, poised to elevate user satisfaction and engagement within the competitive landscape of e- commerce. In conclusion, the study highlights the significance of recommendation systems in enhancing the e- commerce experience. By leveraging graph databases, particularly Neo4j, over traditional systems like MySQL, significant improvements in managing extensive datasets are demonstrated.

Recent advances of ECG monitoring and webserver health monitoring applications: A review

In response to recent improvements in mobile technology and cloud computing-based real-time monitoring systems, numerous cloud-based healthcare services and devices have been developed. In the cloud system, it is feasible to collect and distribute medical data immediately from anywhere to medical specialists, and patients can receive network-based feedback. This paper reviews the most recent developments and trends in wearable technology and biomedical equipment (mostly ECG), as well as the development of a cloud-based solution for mobile device and web browser users. The system contains ECG enhancement, ECG quality grading, and ECG parameter extraction methods. A use case was utilized to showcase the system, in which a web server processed ECG data from a mobile phone in real-time. The system has been proved to be functional, accurate, and efficient. We review the primary principles underlying the operation of the ECG and its fundamental operation, as well as the recording and transmission paradigm for ECG signals. Detailed processes were also provided for the system components, their configuration, and the employed technologies. In conclusion, we examine the complete study and the project's future scope.

Syn-CpG-Spacer: A Panel web app for synonymous recoding of viral genomes with CpG dinucleotides.

Vertebrate genomes contain lower than expected frequencies of the CpG dinucleotide. Consequently, many vertebrate viruses have evolved to mimic this composition, possibly in order to evade host antiviral defences (Greenbaum et al., 2008). For example, the antiviral protein ZAP binds CpGs in viral single stranded RNA with specific spacing requirements (Gonçalves-Carneiro et al., 2022), though CpGs are also likely depleted in viral genomes due to other selective pressures (Forni et al., 2023). Increasing CpG abundance by synonymous recoding could facilitate attenuation of viruses without compromising their epitope antigenicity by changing non-CpG codons to alternatives containing CpG without changing the overall amino acid sequence (Gonçalves-Carneiro et al., 2022; Le Nouën et al., 2019; Sharp et al., 2023). There are three ways CpGs can be synonymously introduced in codons: at positions 1-2 for arginine (e.g. AGA → CGA), 2-3 for several amino acids (e.g. ACA → ACG), or in a 3-1 split configuration, if a subsequent codon begins with a G (e.g. ATA-GCA → ATC-GCA). Syn-CpG-Spacer is a Python progressive web app (PWA) (MDN Web Docs, 2023) made with the Panel library (Panel Development Team, 2024) that allows for consistent recoding of viral sequences and applying biologically relevant constraints. These include setting a minimum gap between CpG's, optimising for an average CpG gap, protecting cis-acting regulatory signals from modification, and modulating the A-content in the overall sequence. The app features a sequence viewer made with the Bokeh library (Bokeh Development Team, 2024) that highlights CpG dinucleotides, allowing for efficient analysis of the resulting distribution of CpGs. This is complemented by a statistical data table. Utilising Biopython (Cock et al., 2009) modules, the user can load their sequence as a FASTA file and download the outputs as an alignment in the same format. As a PWA running on Pyodide (The Pyodide development team, 2023), the code is only executed in the user's browser and they can install the app onto their machine for offline use.

Analysis of web design visual element attention based on user educational background

The evolution of Internet technology has led to an increase in online users. This study focuses on the pivotal role of visual elements in web content conveyance and their impact on user browsing behavior. Therefore, the use of visual elements in web design based on big data has aroused widespread concern among web designers, they apply visual elements to their web design works to make the web more attractive. This study examines the composition and distribution characteristics of key visual elements identified through user behavior data in a big data environment and discusses the use of visual elements in web design in the era of network economy. In addition, this paper issued 200 questionnaires to investigate the degree of attention to visual elements in web pages for users of different occupations and different educational backgrounds. Our survey indicated that visual elements captured the attention of 41% of corporate employees, whereas a mere 1% of social welfare workers focused on web content; 36% of undergraduates pay attention to visual elements of web pages, but only 5% and 4% of postgraduates and doctoral degrees and above. Therefore, the visual elements of the designed web page need to conform to the user's cultural background and professional background.

An Enhanced Mechanism for Protecting Web Applications from Cross Site Request Forgery (CSRF)

Cross-Site Request Forgery (CSRF) is considered one of the top vulnerabilities in today’s web, where an untrusted website can force the user browser to send an unauthorized valid request to the trusted site. Legitimate users will lose their integrity over the website when the CSRF takes place. So far, many solutions have been proposed for the CSRF attacks such as the referrer HTTP header, custom HTTP header, origin header, client site proxy, a browser plug-in, and random token validation, but in this research, the use of random token validation to solve the problem of CSRF attacks was implemented. The proposed solution in the study used a concept known as Nonce which is a type of random token attached to requests sent over a server. This solution proved to be effective enough to protect and prevent web applications from CSRF attacks. Although no system can be secured against attacks, this study recommends that the random token validation (Nonce) approach of protecting web applications should be adopted by all web applications developers and users to improve on the already established solutions to reduce the chances of attacks on web applications to a very slim percentage, if not eliminated.

ANALYSIS OF POTENTIAL PERSONAL DATA LEAKS IN WEB BROWSERS

The distribution of the vast majority of web browsers is actively encouraged by their free use. This is a common practice of web browser developers, as it provides them with great opportunities for their distribution. The flip side of this process is the collection of personal data by web browser developers that the user does not control. The collected data is automatically transferred to leading IT companies such as Google, Microsoft, and Cloudflare, which collect, accumulate, process, and monetize the users’ data in an automated manner. This leads to the fact that any web browser user is profiled in the services of leading IT companies, which receive complete information about the user's actions on the Internet. This state of affairs contradicts Article 32 of the Constitution of Ukraine, which guarantees the right to privacy and the basic provisions of the Law of Ukraine "On Personal Data Protection". The study involved long-term recording and subsequent analysis of the network traffic of Ukraine's most popular web browsers: Google Chrome, Microsoft Edge, Mozilla Firefox, and Opera. The peculiarity of the study was to obtain network traffic initiated by web browsers that have been active for a long time. To increase the reliability, the data on network connections of web browsers were obtained using two independent software tools for monitoring traffic on the network interface of a communication device. The analysis of network connections of web browsers made it possible to establish close ties between companies developing free web browsers and leading IT companies that monopolistically control the actions of users in the Internet space. This state of affairs contradicts the legal norms on ensuring the privacy of web browser users in the context of using their data without their knowledge and consent. This can be prevented using network screens operating at Layers 3, 4, and 7 of the TCP/IP stack OSI model.

CoStricTor: Collaborative HTTP Strict Transport Security in Tor Browser

HTTP Strict Transport Security (HSTS) is a widely-deployed security feature in modern web browsing. It is also, however, a potential vector for user tracking and surveillance. Tor Browser, a web browser primarily concerned with online anonymity, disables HSTS as a result of this tracking potential. We present the CoStricTor protocol which crowdsources HSTS data among Tor Browser clients. It gives Tor Browser users increased resistance to man-in-the-middle attacks without exposing them to HSTS tracking. Our protocol adapts other privacy-preserving data aggregation algorithms to share data effectively among users with strong local differential privacy guarantees. The CoStricTor protocol resists denial of service attacks by design through our innovative use of Bloom filters to represent complementary data. Our simulations show our protocol can model up to 150,000 websites, providing 10,000 upgrades to HSTS for users.

Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.

Detecting Risky Authentication Using the OpenID Connect Token Exchange Time.

With the rise in sophisticated cyber threats, traditional authentication methods are no longer sufficient. Risk-based authentication (RBA) plays a critical role in the context of the zero trust framework-a paradigm shift that assumes no trust within or outside the network. This research introduces a novel proposal as its core: utilization of the time required by OpenID Connect (OIDC) token exchanges as a new RBA feature. This innovative approach enables the detection of tunneled connections without any intervention from the user's browser or device. By analyzing the duration of OIDC token exchanges, the system can identify any irregularities that may signify unauthorized access attempts. This approach not only improves upon existing RBA frameworks but is also in alignment with the broader movement toward intelligent and responsive security systems.

DEAD FORENSIC ANALYSIS OF QUTEBROWSER AND LIBREWOLF BROWSERS USING THE NIST 800-86 METHOD

A browser is software used to access web pages to obtain clear and readable information. Information resources are identified by a Uniform Resource Identifier (URI) and can be web pages, images, videos, or other content. When a browser user engages in online activities, they usually leave traces on the device such as history, cookies, cache files, and even emails and passwords. Such traces can usually help users access a website or input something, such as emails and passwords. The purpose of this research is to obtain digital evidence in the form of a cache on the hard disk in the Librewolf and Qutebrowser browsers. In this study, researchers used the National Institute of Standards and Technology (NIST) 800-86 method which consists of four stages, namely collection, examination, analysis, and reporting. which focuses on the Qutebrowser and LibreWolf browsers. The results obtained from this study were found to be 21 caches, 2 Sessions, 6 Cookies, 8 Network Persistent State, 9 QuotaManager, 11 IndexedDB, 24 LevelDB, 48 Cache Storage, 14 Favicons, 3 History, 6 Database, 3 StartupCache, 4 Alternate Services, 6 Content-Pref, Notification amounted to 1, Permission amounted to 7, Service Worker amounted to 6, SiteSecuristyServiceState amounted to 7, Webappstore amounted to 8, Sessionstore-Backups amounted to 5, Storage amounted to 47 NIST 800-86 method can be properly used in the acquisition of digital evidence and the most crucial data obtained in the Librewolf browser on the telegram and whatsapp sites.

BIGaze: An eye-gaze action-guided Bayesian information gain framework for information exploration

Bayesian information gain (BIG) framework-based information exploration studies have generally focused on explicit interactions, such as user browsing via keyboard and mouse. However, if implicit information, such as eye-gaze data, is implemented in the BIG framework alongside explicit interactions, the system can accurately respond and proactively provide target information to the user. We, therefore, propose the BIGaze system, which uses real-time eye-gaze data to predict and recommend target information by analyzing the browsing and gazing action patterns corresponding to information searched by the user. To validate the performance of the proposed system within the information exploration process, we performed comparative user experiments with BIGaze, BIGbase (a BIG system without gaze data), and a non-BIG system. The experimental results reveal that BIGaze proactively captures user exploration targets, confidently generates user-intended recommendations, and assists a wide range of user exploration patterns.

Certificate Transparency With Enhanced Privacy

Digital certificates play an important role in the authentication of communicating parties for transport layer security. Recently, however, frequent incidents such as the illegal issuance of fake certificates by a compromised certificate authority have raised concerns about the legacy certificate system. Certificate Transparency (CT) mitigates such issues by employing a log server to audit issued certificates publicly, making the certificate issuance and verification processes transparent. Unfortunately, the legacy CT ecosystem suffers from log server compromises and user browsing information leakage. Furthermore, the data structure for the certificate management in the legacy CT system incurs computation overhead linear to the number of registered certificates in the log. In this paper, we propose a secure CT scheme by leveraging a shared value tree (SVT), a novel log structure specifically designed to address the log server compromise and browsing information leakage problems. The verification time of SVT remains constant regardless of the number of registered certificates in the log. We analyze our scheme on the legacy CT system to demonstrate its incremental deployability, guaranteeing a smooth transition toward a more secure web ecosystem.

A Study on User Behavior of E-commerce Platforms based on Data Analysis

With the popularity of the Internet and the rapid development of mobile technology, e-commerce platforms have become one of the main channels for people's shopping and transactions. On e-commerce platforms, users' behavioral data are widely recorded and stored, which provides a rich data resource for studying user behavior. Studying data analysis of user behavior on e-commerce platforms helps to understand users' behavior patterns and provides a scientific basis for e-commerce platforms to optimize marketing strategies, improve user experience and realize personalized recommendations. The article elaborates on the data collection and processing work from three aspects: data source selection and acquisition, pre-processing and cleaning, data feature extraction and variable selection, and studies the user behavior of e-commerce platform from three perspectives: user purchasing behavior, user browsing behavior, and user commenting behavior, to promote the development and enhance the competitiveness of e-commerce platform.

Design of a linear regression model-based Internet exit anomaly detection method

Abstract Anomaly detection for Internet egress is to enhance the user experience of browsing the Internet. Firstly, the five functional modules of the system are described, and the pre-processing data module is used to extract the Internet topology data for Internet anomaly detection. The linear regression algorithm is also introduced in detail, including the definition of linear regression and its parameter estimation method and the optimization of linear regression parameters by variance and squared error. Finally, the performance evaluation of the anomaly detection system proposed in this paper is carried out to verify the system’s feasibility. From the performance evaluation, the detection rate of the system in this paper is 2.93 and 5.33 percentage points higher than that of SVM and SNN detection methods, respectively, and the false alarm rate is 2.85%. Regarding the impact of different packet lengths, the system in this paper is relatively stable when the packet length is 600, with an accuracy rate of 99.94% and a false alarm rate of only 1.93%. The above data show that the Internet egress anomaly detection system proposed in this paper can effectively detect the anomalies existing in the Internet egress and accurately grasp the data can timely deal with the abnormal nodes, thus improving the user browsing experience.

PlantMetSuite: A User-Friendly Web-Based Tool for Metabolomics Analysis and Visualisation.

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored to plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

Perception based User Profiles for Web Personalization

Personalized web services reduce the burden of information overload by collecting facts that match the needs of the user. An important aspect of personalized web services is the creation of user profiles that contain user information and settings. This article introduces a unique method called Perception-Based User Profiles (PUP) based on perception and browsing order, develops and updates user profiles. User profiles include perceptions and relationships, which can help guarantee that user interests are represented semantically. Second, when calculating the perception and duration of the relationship, for each site in a session, the user's browsing order is considered. Third, cognitive psychometric memory model is used to update the user profile's perceptions and relationships at the end of each session, ensuring the user profile's dynamics. The results of the tests suggest that this strategy works well for building and updating user profiles.