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Diverse Datasets and a Customizable Benchmarking Framework for Phishing

https://doi.org/10.1145/3375708.3380313
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-17140-6openalex-snapshot
Computer Security – ESORICS 2022
inferredcites →https://doi.org/10.1007/978-3-319-12160-4_27openalex-snapshot
Semantic Feature Selection for Text with Application to Phishing Email Detection
inferredcites →https://doi.org/10.1007/978-3-642-33167-1_47openalex-snapshot
Detecting Phishing Emails the Natural Language Way
inferredcites →https://doi.org/10.1016/j.eswa.2014.03.019openalex-snapshot
Phishing detection based Associative Classification data mining
inferredcites →https://doi.org/10.1016/j.neucom.2017.03.077openalex-snapshot
SOL: A library for scalable online learning algorithms
inferredcites →https://doi.org/10.1109/access.2020.2969780openalex-snapshot
An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs
inferredcites →https://doi.org/10.1109/asonam.2010.56openalex-snapshot
Profiling Phishing Emails Based on Hyperlink Information
inferredcites →https://doi.org/10.1109/comst.2019.2957750openalex-snapshot
SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective
inferredcites →https://doi.org/10.1109/isi.2016.7745439openalex-snapshot
PhishMonger: A free and open source public archive of real-world phishing websites
inferredcites →https://doi.org/10.1109/trustcom.2011.126openalex-snapshot
Phishing Email Feature Selection Approach
inferredcites →https://doi.org/10.1109/trustcom.2013.76openalex-snapshot
Profiling Phishing Email Based on Clustering Approach
inferredcites →https://doi.org/10.1137/1.9781611972801.67openalex-snapshot
A Robust Decision Tree Algorithm for Imbalanced Data Sets
inferredcites →https://doi.org/10.1145/1242572.1242660openalex-snapshot
Learning to detect phishing emails
inferredcites →https://doi.org/10.1145/1541880.1541883openalex-snapshot
Methodologies for data quality assessment and improvement
inferredcites →https://doi.org/10.1145/2851613.2851801openalex-snapshot
Accurate spear phishing campaign attribution and early detection
inferredcites →https://doi.org/10.1145/2908812.2908918openalex-snapshot
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
inferredcites →https://doi.org/10.1145/3052973.3053017openalex-snapshot
Gossip
inferredcites →https://doi.org/10.1145/3319535.3363267openalex-snapshot
Data Quality for Security Challenges
inferredcites →https://doi.org/10.1155/2014/425731openalex-snapshot
Classification of Phishing Email Using Random Forest Machine Learning Technique
inferredcites →https://doi.org/10.17632/h3cgnj8hft.1openalex-snapshot
Phishing Dataset for Machine Learning: Feature Evaluation
inferredcites →https://doi.org/10.3233/jcs-2010-0371openalex-snapshot
New filtering approaches for phishing email
inferredcites →https://doi.org/10.48550/arxiv.1412.7197openalex-snapshot
Robust Topological Inference: Distance To a Measure and Kernel Distance
inferredcites →https://doi.org/10.48550/arxiv.1609.06570openalex-snapshot
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1802.03162openalex-snapshot
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
inferredcites →https://doi.org/10.48550/arxiv.1808.06492openalex-snapshot
Benchmarking Automatic Machine Learning Frameworks
inferredcites →https://doi.org/10.48550/arxiv.1907.00909openalex-snapshot
An Open Source AutoML Benchmark
inferredcites →https://doi.org/10.5220/0005574304270434openalex-snapshot
Phish-IDetector: Message-Id Based Automatic Phishing Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3306192openalex-extracted
Information Disclosure and Security Vulnerability Awareness: A Large-Scale Randomized Field Experiment in Pan-Asia
inferred← citeshttps://doi.org/10.1007/978-3-030-55692-1_4openalex-snapshot
Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN Model
inferred← citeshttps://doi.org/10.1007/978-3-030-86797-3_17openalex-snapshot
Explaining the Suspicion: Design of an XAI-Based User-Focused Anti-Phishing Measure
inferred← citeshttps://doi.org/10.1007/978-3-031-70924-1_42openalex-snapshot
Phishing Email Detection: Survey
inferred← citeshttps://doi.org/10.1007/978-3-031-82362-6_9openalex-snapshot
PhishCoder: Efficient Extraction of Contextual Information from Phishing Emails
inferred← citeshttps://doi.org/10.1007/978-3-031-86592-3_4openalex-snapshot
Cyber Security Intelligent Systems Based on Federated Learning
inferred← citeshttps://doi.org/10.1109/access.2022.3196018openalex-snapshot
Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML
inferred← citeshttps://doi.org/10.1109/access.2022.3225971openalex-snapshot
Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes
inferred← citeshttps://doi.org/10.1109/access.2024.3518923openalex-snapshot
LogiTriBlend: A Novel Hybrid Stacking Approach for Enhanced Phishing Email Detection Using ML Models and Vectorization Approach
inferred← citeshttps://doi.org/10.1109/access.2025.3615788openalex-snapshot
Securing the Weakest Link: Exploring Affective States Exploited in Phishing Emails With Large Language Models
inferred← citeshttps://doi.org/10.1109/comst.2024.3441752openalex-snapshot
Phishing Webpage Detection: Unveiling the Threat Landscape and Investigating Detection Techniques
inferred← citeshttps://doi.org/10.1109/educon60312.2024.10578588openalex-snapshot
AI and Network Security Curricula: Minding the Gap
inferred← citeshttps://doi.org/10.1109/tkde.2025.3609320openalex-snapshot
PRIME: A Phishing Detection Framework With Quantitative and Fuzzy-Based Dual Validation
inferred← citeshttps://doi.org/10.1145/3372297.3420017openalex-snapshot
PhishBench 2.0: A Versatile and Extendable Benchmarking Framework for Phishing
inferred← citeshttps://doi.org/10.1145/3442181openalex-snapshot
Machine Learning for Detecting Data Exfiltration
inferred← citeshttps://doi.org/10.1145/3508398.3519358openalex-snapshot
Does Deception Leave a Content Independent Stylistic Trace?
inferred← citeshttps://doi.org/10.1145/3626232.3653283openalex-snapshot
WikiPhish: A Diverse Wikipedia-Based Dataset for Phishing Website Detection: Data/Toolset Paper
inferred← citeshttps://doi.org/10.1145/3643651.3659898openalex-snapshot
LLMs for Explainable Few-shot Deception Detection
inferred← citeshttps://doi.org/10.1145/3704522.3704532openalex-snapshot
Can Features for Phishing URL Detection Be Trusted Across Diverse Datasets? A Case Study with Explainable AI
inferred← citeshttps://doi.org/10.11591/ijece.v13i3.pp3227-3243openalex-snapshot
Deep learning in phishing mitigation: a uniform resource locator-based predictive model
inferred← citeshttps://doi.org/10.2139/ssrn.7208251openalex-snapshot
Robustness and Generalization of Artificial Intelligence Models for Text-Based Phishing Email Detection: A Critical State-of-the-Art Review and Research Agenda
inferred← citeshttps://doi.org/10.2339/politeknik.1745083openalex-snapshot
Deep Learning and Explainable AI for Email Phishing Classification: A Comparative Study of TabNet, NODE and FT-Transformer Models
inferred← citeshttps://doi.org/10.23919/mipro57284.2023.10159904openalex-snapshot
Data Acquisition and Corpus Creation for Phishing Detection
inferred← citeshttps://doi.org/10.3390/electronics14183744openalex-snapshot
Machine Learning and Neural Networks for Phishing Detection: A Systematic Review (2017–2024)
inferred← citeshttps://doi.org/10.3390/electronics15132804openalex-snapshot
A Tri-Axis Systematic Literature Review of AI-Powered Cyber Defense: ATT&CK-Aligned Analysis of Cyberattacks, Machine Learning Methods, and Datasets
inferred← citeshttps://doi.org/10.48550/arxiv.2012.09344openalex-snapshot
Machine Learning for Detecting Data Exfiltration: A Review
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