Cover Image

It's not Easy: Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rosenzweig, Ben, Valle, Valentino Dalla, Apruzzese, Giovanni, Fass, Aurore
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

What is in the Chrome Web Store? Investigating Security-Noteworthy Browser Extensions
by: Hsu, Sheryl, et al.
Published: (2024)

Malicious GenAI Chrome Extensions: Unpacking Data Exfiltration and Malicious Behaviours
by: Seetharam, Shresta B., et al.
Published: (2025)

Did I Vet You Before? Assessing the Chrome Web Store Vetting Process through Browser Extension Similarity
by: Moreno, José Miguel, et al.
Published: (2024)

SoK: Reshaping Research on Network Intrusion Detection Systems
by: Apruzzese, Giovanni
Published: (2026)

Identification of Malicious Posts on the Dark Web Using Supervised Machine Learning
by: Filho, Sebastião Alves de Jesus, et al.
Published: (2025)

COOKIEGUARD: Characterizing and Isolating the First-Party Cookie Jar
by: Bahrami, Pouneh Nikkhah, et al.
Published: (2024)

NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
by: Thaqi, Leand, et al.
Published: (2024)

The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems powered by Deep Learning
by: Rizvani, Advije, et al.
Published: (2025)

An Empirical Study of JavaScript Inclusion Security Issues in Chrome Extensions
by: Guan, Chong
Published: (2025)

Department-Specific Security Awareness Campaigns: A Cross-Organizational Study of HR and Accounting
by: Pfister, Matthias, et al.
Published: (2025)

Can SOC Operators Explain their Decisions while Triaging Alarms? A Real-World Study
by: Moosmann, Jessica, et al.
Published: (2026)

Detecting Malicious Accounts in Web3 through Transaction Graph
by: Li, Wenkai, et al.
Published: (2024)

A Study on Malicious Browser Extensions in 2025
by: Singh, Shreya, et al.
Published: (2025)

The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations
by: Weinz, Marie, et al.
Published: (2025)

Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading
by: Rizvani, Advije, et al.
Published: (2026)

"What is the Problem Space?" Defining Host-space Adversarial Perturbations against Network Intrusion Detection Systems
by: Verkerken, Miel, et al.
Published: (2026)

Towards Browser Controls to Protect Cookies from Malicious Extensions
by: Tyler, Liam, et al.
Published: (2024)

"Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages
by: Yuan, Ying, et al.
Published: (2024)

Towards Classifying Benign And Malicious Packages Using Machine Learning
by: Nguyen, Thanh-Cong, et al.
Published: (2025)

X-raying the arXiv: A Large-Scale Analysis of arXiv Submissions' Source Files
by: Apruzzese, Giovanni, et al.
Published: (2026)

The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries
by: Lu, Tianpei, et al.
Published: (2024)

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
by: Schröer, Saskia Laura, et al.
Published: (2025)

Misleading Large Language Models used (or misused) in Scientific Peer-Reviewing via Hidden Prompt-Injection Attacks
by: Collu, Matteo Gioele, et al.
Published: (2025)

E-PhishGen: Unlocking Novel Research in Phishing Email Detection
by: Pajola, Luca, et al.
Published: (2025)

I can't recognize (yet): Delayed Rendering to Defeat Visual Phishing Detectors
by: Yuan, Ying, et al.
Published: (2026)

Malicious Package Detection using Metadata Information
by: Halder, S., et al.
Published: (2024)

Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection
by: Rahman, Md Abdur, et al.
Published: (2024)

Towards Quantum Machine Learning for Malicious Code Analysis
by: Lopez, Jesus, et al.
Published: (2025)

Detection of Dark Web Threats Using Machine Learning and Image Processing
by: Medipelly, Swetha, et al.
Published: (2024)

Across-Platform Detection of Malicious Cryptocurrency Transactions via Account Interaction Learning
by: Che, Zheng, et al.
Published: (2024)

Robust Federated Learning for Malicious Clients using Loss Trend Deviation Detection
by: Bhaskar, Deepthy K, et al.
Published: (2026)

Advancing Malicious Website Identification: A Machine Learning Approach Using Granular Feature Analysis
by: Tran, Kinh, et al.
Published: (2024)

Attack Effect Model based Malicious Behavior Detection
by: Wang, Limin, et al.
Published: (2025)

ConCap: Practical Network Traffic Generation for (ML- and) Flow-based Intrusion Detection Systems
by: Verkerken, Miel, et al.
Published: (2025)

URL2Graph++: Unified Semantic-Structural-Character Learning for Malicious URL Detection
by: Tian, Ye, et al.
Published: (2025)

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
by: Kavuri, Likhitha Annapurna, et al.
Published: (2025)

Malicious ML Model Detection by Learning Dynamic Behaviors
by: Nambiar, Sarang, et al.
Published: (2026)

Malicious Code Detection in Smart Contracts via Opcode Vectorization
by: Zou, Huanhuan, et al.
Published: (2025)

RoBERTa-Augmented Synthesis for Detecting Malicious API Requests
by: Aharon, Udi, et al.
Published: (2024)

PhishSigma++: Malicious Email Detection with Typed Entity Relations
by: Shang, Shang, et al.
Published: (2026)