Buchumschlag

NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Thaqi, Leand, Halili, Arbnor, Vishi, Kamer, Rexha, Blerim
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Cryptography and Security Artificial Intelligence Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML
von: Selimi, Synim, et al.
Veröffentlicht: (2024)

Unveiling the Digital Fingerprints: Analysis of Internet attacks based on website fingerprints
von: Rexha, Blerim, et al.
Veröffentlicht: (2024)

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features
von: Faisal, Aseer Al
Veröffentlicht: (2025)

PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark
von: Dalton, Thomas, et al.
Veröffentlicht: (2025)

KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection
von: Li, Yuexin, et al.
Veröffentlicht: (2024)

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
von: Kuikel, Shova, et al.
Veröffentlicht: (2025)

Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
von: Thapa, Jikesh, et al.
Veröffentlicht: (2025)

Explainable Transformer-Based Email Phishing Classification with Adversarial Robustness
von: P, Sajad U
Veröffentlicht: (2025)

An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach
von: Uddin, Mohammad Amaz, et al.
Veröffentlicht: (2024)

Every Character Counts: From Vulnerability to Defense in Phishing Detection
von: Chiper, Maria, et al.
Veröffentlicht: (2025)

PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT
von: Roy, Sayak Saha, et al.
Veröffentlicht: (2024)

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

An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey
von: Ige, Tosin, et al.
Veröffentlicht: (2024)

PhishNet: A Phishing Website Detection Tool using XGBoost
von: Kumar, Prashant, et al.
Veröffentlicht: (2024)

PhishKey: A Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction
von: Castaño, Felipe, et al.
Veröffentlicht: (2025)

Phishing the Phishers with SpecularNet: Hierarchical Graph Autoencoding for Reference-Free Web Phishing Detection
von: Song, Tailai, et al.
Veröffentlicht: (2026)

MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection
von: Xue, Yinuo, et al.
Veröffentlicht: (2025)

Web Phishing Net (WPN): A scalable machine learning approach for real-time phishing campaign detection
von: Zia, Muhammad Fahad, et al.
Veröffentlicht: (2025)

CIC-Trap4Phish: A Unified Multi-Format Dataset for Phishing and Quishing Attachment Detection
von: Nejati, Fatemeh, et al.
Veröffentlicht: (2026)

Teach LLMs to Phish: Stealing Private Information from Language Models
von: Panda, Ashwinee, et al.
Veröffentlicht: (2024)

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection
von: Fajar, Abdullah, et al.
Veröffentlicht: (2024)

AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection
von: Aslam, Saba, et al.
Veröffentlicht: (2024)

Phishing URL Detection using Bi-LSTM
von: Baskota, Sneha
Veröffentlicht: (2025)

PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis
von: Islam, Md Robiul, et al.
Veröffentlicht: (2024)

PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier
von: Shahriyar, Md. Farhan, et al.
Veröffentlicht: (2025)

PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing
von: Minhaz, Md Abdul Ahad, et al.
Veröffentlicht: (2025)

A Sophisticated Framework for the Accurate Detection of Phishing Websites
von: Newaz, Asif, et al.
Veröffentlicht: (2024)

Eliciting Least-to-Most Reasoning for Phishing URL Detection
von: Trikilis, Holly, et al.
Veröffentlicht: (2026)

Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions
von: Alghuried, Ahod, et al.
Veröffentlicht: (2025)

Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models
von: Ganiuly, Daniyal, et al.
Veröffentlicht: (2025)

Multimodal Large Language Models for Phishing Webpage Detection and Identification
von: Lee, Jehyun, et al.
Veröffentlicht: (2024)

The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs
von: Gopali, Saroj, et al.
Veröffentlicht: (2024)

SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration
von: Firdos, Rumman, et al.
Veröffentlicht: (2026)

Phishing Website Detection through Multi-Model Analysis of HTML Content
von: Çolhak, Furkan, et al.
Veröffentlicht: (2024)

Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework
von: Ige, Tosin, et al.
Veröffentlicht: (2024)

EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
von: Lim, Bryan, et al.
Veröffentlicht: (2025)

LLM-Powered Intent-Based Categorization of Phishing Emails
von: Eilertsen, Even, et al.
Veröffentlicht: (2025)

Enhancing Phishing Email Identification with Large Language Models
von: Lee, Catherine
Veröffentlicht: (2025)

Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis
von: Alghureid, Ahod, et al.
Veröffentlicht: (2024)

Small Language Models for Phishing Website Detection: Cost, Performance, and Privacy Trade-Offs
von: Goldenits, Georg, et al.
Veröffentlicht: (2025)