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Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-19353-9_3openalex-snapshot
Dynamic Recognition of Phishing URLs Using Deep Learning Techniques
inferredcites →https://doi.org/10.1007/978-3-030-39303-8_17openalex-snapshot
Catching the Phish: Detecting Phishing Attacks Using Recurrent Neural Networks (RNNs)
inferredcites →https://doi.org/10.1007/978-3-030-41579-2_43openalex-snapshot
A Character-Level BiGRU-Attention for Phishing Classification
inferredcites →https://doi.org/10.1007/978-3-030-62582-5_21openalex-snapshot
DURLD: Malicious URL Detection Using Deep Learning-Based Character Level Representations
inferredcites →https://doi.org/10.1007/978-3-030-92905-3_45openalex-snapshot
Multidimensional Features Driven Phishing Detection Based on Deep Learning
inferredcites →https://doi.org/10.1007/978-981-13-9155-2_5openalex-snapshot
Classification of Phishing Attack Solutions by Employing Deep Learning Techniques: A Systematic Literature Review
inferredcites →https://doi.org/10.1007/978-981-15-0058-9_57openalex-snapshot
Comparison of Ensemble Simple Feedforward Neural Network and Deep Learning Neural Network on Phishing Detection
inferredcites →https://doi.org/10.1007/s00521-016-2275-yopenalex-snapshot
Fighting against phishing attacks: state of the art and future challenges
inferredcites →https://doi.org/10.1007/s00521-017-3305-0openalex-snapshot
Detection of phishing websites using an efficient feature-based machine learning framework
inferredcites →https://doi.org/10.1007/s00521-019-04331-5openalex-snapshot
Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks
inferredcites →https://doi.org/10.1007/s00521-020-04830-wopenalex-snapshot
DeNNeS: deep embedded neural network expert system for detecting cyber attacks
inferredcites →https://doi.org/10.1007/s00521-020-05041-zopenalex-snapshot
Online meta-learning firewall to prevent phishing attacks
inferredcites →https://doi.org/10.1007/s10462-020-09814-9openalex-snapshot
Applicability of machine learning in spam and phishing email filtering: review and approaches
inferredcites →https://doi.org/10.1007/s10489-018-1161-yopenalex-snapshot
Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks
inferredcites →https://doi.org/10.1007/s10489-019-01433-4openalex-snapshot
Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network
inferredcites →https://doi.org/10.1007/s11235-017-0334-zopenalex-snapshot
Defending against phishing attacks: taxonomy of methods, current issues and future directions
inferredcites →https://doi.org/10.1007/s11235-020-00733-2openalex-snapshot
A comprehensive survey of AI-enabled phishing attacks detection techniques
inferredcites →https://doi.org/10.1007/s11831-020-09478-2openalex-snapshot
A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security
inferredcites →https://doi.org/10.1007/s12046-020-01392-4openalex-snapshot
Efficient deep learning techniques for the detection of phishing websites
inferredcites →https://doi.org/10.1007/s12652-020-02630-7openalex-snapshot
Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives
inferredcites →https://doi.org/10.1007/s42979-021-00535-6openalex-snapshot
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
inferredcites →https://doi.org/10.1016/j.cogsys.2018.08.023openalex-snapshot
Deep learning: Evolution and expansion
inferredcites →https://doi.org/10.1016/j.comcom.2020.01.016openalex-snapshot
Deep learning and big data technologies for IoT security
inferredcites →https://doi.org/10.1016/j.comcom.2020.02.008openalex-snapshot
Machine Learning Models for Secure Data Analytics: A taxonomy and threat model
inferredcites →https://doi.org/10.1016/j.comnet.2020.107275openalex-snapshot
Accurate and fast URL phishing detector: A convolutional neural network approach
inferredcites →https://doi.org/10.1016/j.cose.2013.04.004openalex-snapshot
From information security to cyber security
inferredcites →https://doi.org/10.1016/j.cose.2017.04.006openalex-snapshot
Phishing environments, techniques, and countermeasures: A survey
inferredcites →https://doi.org/10.1016/j.cose.2017.12.006openalex-snapshot
Mobile phishing attacks and defence mechanisms: State of art and open research challenges
inferredcites →https://doi.org/10.1016/j.cose.2019.03.018openalex-snapshot
A keyword-based combination approach for detecting phishing webpages
inferredcites →https://doi.org/10.1016/j.cosrev.2018.05.003openalex-snapshot
A recent review of conventional vs. automated cybersecurity anti-phishing techniques
inferredcites →https://doi.org/10.1016/j.cosrev.2020.100317openalex-snapshot
Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review
inferredcites →https://doi.org/10.1016/j.eswa.2018.03.050openalex-snapshot
A survey of phishing attacks: Their types, vectors and technical approaches
inferredcites →https://doi.org/10.1016/j.eswa.2018.09.029openalex-snapshot
Machine learning based phishing detection from URLs
inferredcites →https://doi.org/10.1016/j.eswa.2020.114170openalex-snapshot
Deep belief network based intrusion detection techniques: A survey
inferredcites →https://doi.org/10.1016/j.future.2019.03.007openalex-snapshot
Mobile malware attacks: Review, taxonomy & future directions
inferredcites →https://doi.org/10.1016/j.infsof.2008.09.009openalex-snapshot
Systematic literature reviews in software engineering – A systematic literature review
inferredcites →https://doi.org/10.1016/j.iot.2021.100365openalex-snapshot
Machine learning approaches to IoT security: A systematic literature review
inferredcites →https://doi.org/10.1016/j.ipm.2020.102250openalex-snapshot
Detection of Bots in Social Media: A Systematic Review
inferredcites →https://doi.org/10.1016/j.jnca.2021.102985openalex-snapshot
DISTILLER: Encrypted traffic classification via multimodal multitask deep learning
inferredcites →https://doi.org/10.1016/j.knosys.2019.105124openalex-snapshot
Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues
inferredcites →https://doi.org/10.1016/j.neucom.2015.09.116openalex-snapshot
Deep learning for visual understanding: A review
inferredcites →https://doi.org/10.1016/j.neucom.2019.02.056openalex-snapshot
Application of deep learning to cybersecurity: A survey
inferredcites →https://doi.org/10.1016/j.neunet.2020.02.013openalex-snapshot
CNN–MHSA: A Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites
inferredcites →https://doi.org/10.1016/j.pmcj.2019.101084openalex-snapshot
PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices
inferredcites →https://doi.org/10.1016/j.scs.2020.102655openalex-snapshot
Cyber security in smart cities: A review of deep learning-based applications and case studies
inferredcites →https://doi.org/10.1016/s1353-4858(17)30039-9openalex-snapshot
Artificial intelligence – the next frontier in IT security?
inferredcites →https://doi.org/10.1080/19393555.2018.1456577openalex-snapshot
Deep belief network based detection and categorization of malicious URLs
inferredcites →https://doi.org/10.1088/1742-6596/1502/1/012033openalex-snapshot
Comparison of simple feedforward neural network, recurrent neural network and ensemble neural networks in phishing detection
inferredcites →https://doi.org/10.1088/1755-1315/602/1/012001openalex-snapshot
Phishing Websites Detection Based on Hybrid Model of Deep Belief Network and Support Vector Machine
inferredcites →https://doi.org/10.1108/dta-02-2020-0043openalex-snapshot
An optimization-based deep belief network for the detection of phishing e-mails
inferredcites →https://doi.org/10.1108/jeim-01-2020-0036openalex-snapshot
Intelligent phishing detection scheme using deep learning algorithms
inferredcites →https://doi.org/10.1109/access.2018.2830661openalex-snapshot
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
inferredcites →https://doi.org/10.1109/access.2019.2892066openalex-snapshot
Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning
inferredcites →https://doi.org/10.1109/access.2019.2895751openalex-snapshot
Detecting Malicious URLs via a Keyword-Based Convolutional Gated-Recurrent-Unit Neural Network
inferredcites →https://doi.org/10.1109/access.2019.2913705openalex-snapshot
Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism
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/access.2020.3043188openalex-snapshot
Web2Vec: Phishing Webpage Detection Method Based on Multidimensional Features Driven by Deep Learning
inferredcites →https://doi.org/10.1109/apsec.2016.031openalex-snapshot
A Map of Threats to Validity of Systematic Literature Reviews in Software Engineering
inferredcites →https://doi.org/10.1109/bigdata47090.2019.9006352openalex-snapshot
Detection of Phishing websites using Generative Adversarial Network
inferredcites →https://doi.org/10.1109/ccwc51732.2021.9375997openalex-snapshot
Machine LearningTechniquesfor Detection of Website Phishing: A Review for Promises and Challenges
inferredcites →https://doi.org/10.1109/cds49703.2020.00009openalex-snapshot
Deep Learning for Cybersecurity: A Review
inferredcites →https://doi.org/10.1109/compsac.2019.10211openalex-snapshot
Detecting Phishing Websites through Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/comst.2017.2752087openalex-snapshot
Systematization of Knowledge (SoK): A Systematic Review of Software-Based Web Phishing Detection
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/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/globecom42002.2020.9348167openalex-snapshot
A Hierarchical Hybrid Intrusion Detection Approach in IoT Scenarios
inferredcites →https://doi.org/10.1109/icaccs.2019.8728356openalex-snapshot
A Methodical Overview on Phishing Detection along with an Organized Way to Construct an Anti-Phishing Framework
inferredcites →https://doi.org/10.1109/iccais48893.2020.9096821openalex-snapshot
Mitigating Email Phishing Attacks using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/iccais48893.2020.9096869openalex-snapshot
High Accuracy Phishing Detection Based on Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/icccs49678.2020.9277459openalex-snapshot
Phishing Detection from URLs Using Deep Learning Approach
inferredcites →https://doi.org/10.1109/iccisc52257.2021.9484929openalex-snapshot
Survey and Analysis on AI Based Phishing Detection Techniques
inferredcites →https://doi.org/10.1109/icdmw.2017.9openalex-snapshot
A Big Data Analysis Framework Using Apache Spark and Deep Learning
inferredcites →https://doi.org/10.1109/icea.2019.8858300openalex-snapshot
Malicious Websites Detection via CNN based Screenshot Recognition
inferredcites →https://doi.org/10.1109/iceca49313.2020.9297395openalex-snapshot
Analysis of Phishing Website Detection Using CNN and Bidirectional LSTM
inferredcites →https://doi.org/10.1109/icics49469.2020.239536openalex-snapshot
A Convolutional Neural Network Model to Detect Illegitimate URLs
inferredcites →https://doi.org/10.1109/icssit48917.2020.9214132openalex-snapshot
Phishing Attacks Detection using Deep Learning Approach
inferredcites →https://doi.org/10.1109/ictcs.2019.8923069openalex-snapshot
Review: Phishing Detection Approaches
inferredcites →https://doi.org/10.1109/ijcnn.2019.8852416openalex-snapshot
NeuralAS: Deep Word-Based Spoofed URLs Detection Against Strong Similar Samples
inferredcites →https://doi.org/10.1109/infcom.2010.5462216openalex-snapshot
PhishNet: Predictive Blacklisting to Detect Phishing Attacks
inferredcites →https://doi.org/10.1109/inocon50539.2020.9298298openalex-snapshot
Anti-phishing System using LSTM and CNN
inferredcites →https://doi.org/10.1109/itnec48623.2020.9084799openalex-snapshot
Research on Website Phishing Detection Based on LSTM RNN
inferredcites →https://doi.org/10.1109/kst.2019.8687615openalex-snapshot
Image Based Phishing Detection Using Transfer Learning
inferredcites →https://doi.org/10.1109/lcn48667.2020.9314775openalex-snapshot
Improved Phishing Detection Algorithms using Adversarial Autoencoder Synthesized Data
inferredcites →https://doi.org/10.1109/skima47702.2019.8982427openalex-snapshot
Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection
inferredcites →https://doi.org/10.1109/snams52053.2020.9336559openalex-snapshot
Survey paper: Taxonomy of website anti-phishing solutions
inferredcites →https://doi.org/10.1109/surv.2013.032213.00009openalex-snapshot
Phishing Detection: A Literature Survey
inferredcites →https://doi.org/10.1109/taffc.2018.2828819openalex-snapshot
Visually Interpretable Representation Learning for Depression Recognition from Facial Images
inferredcites →https://doi.org/10.1109/tbdata.2020.2978915openalex-snapshot
LSTM Based Phishing Detection for Big Email Data
inferredcites →https://doi.org/10.1109/tdsc.2019.2956035openalex-snapshot
GUI-Squatting Attack: Automated Generation of Android Phishing Apps
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00024openalex-snapshot
Phishing URL Detection via CNN and Attention-Based Hierarchical RNN
inferredcites →https://doi.org/10.1142/s021821301960008xopenalex-snapshot
Parameter Setting for Deep Neural Networks Using Swarm Intelligence on Phishing Websites Classification
inferredcites →https://doi.org/10.1145/2601248.2601268openalex-snapshot
Guidelines for snowballing in systematic literature studies and a replication in software engineering
inferredcites →https://doi.org/10.1145/3270101.3270105openalex-snapshot
Hunting Malicious TLS Certificates with Deep Neural Networks
inferredcites →https://doi.org/10.1145/3381991.3395602openalex-snapshot
Visualizing and Interpreting RNN Models in URL-based Phishing Detection
inferredcites →https://doi.org/10.1155/2018/4678746openalex-snapshot
Web Phishing Detection Using a Deep Learning Framework
inferredcites →https://doi.org/10.1155/2019/2595794openalex-snapshot
PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks
inferredcites →https://doi.org/10.1155/2020/8694796openalex-snapshot
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers
inferredcites →https://doi.org/10.1155/2020/8872923openalex-snapshot
Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey
inferredcites →https://doi.org/10.1184/r1/6469805openalex-snapshot
An Empirical Analysis of Phishing Blacklists
inferredcites →https://doi.org/10.1287/isre.2020.0973openalex-snapshot
The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites
inferredcites →https://doi.org/10.1371/journal.pmed.1000097openalex-snapshot
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
inferredcites →https://doi.org/10.14722/ndss.2018.23191openalex-snapshot
Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations
inferredcites →https://doi.org/10.20532/cit.2019.1004702openalex-snapshot
A Phishing Webpage Detection Method Based on Stacked Autoencoder and Correlation Coefficients
inferredcites →https://doi.org/10.2139/ssrn.3922446openalex-snapshot
PDMLP: Phishing Detection Using Multilayer Perceptron
inferredcites →https://doi.org/10.2197/ipsjjip.28.222openalex-snapshot
Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach
inferredcites →https://doi.org/10.22364/bjmc.2020.8.3.06openalex-snapshot
Detection of Phishing URLs by Using Deep Learning Approach and Multiple Features Combinations
inferredcites →https://doi.org/10.23919/cycon.2018.8405026openalex-snapshot
On the effectiveness of machine and deep learning for cyber security
inferredcites →https://doi.org/10.3390/app9204396openalex-snapshot
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
inferredcites →https://doi.org/10.3390/electronics9071177openalex-snapshot
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions
inferredcites →https://doi.org/10.3390/electronics9091514openalex-snapshot
An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL
inferredcites →https://doi.org/10.3390/info10040122openalex-snapshot
A Survey of Deep Learning Methods for Cyber Security
inferredcites →https://doi.org/10.3390/make3030034openalex-snapshot
A Survey of Machine Learning-Based Solutions for Phishing Website Detection
inferredcites →https://doi.org/10.3991/ijim.v14i11.13903openalex-snapshot
Efficient Detection of Phishing Websites Using Multilayer Perceptron
inferredcites →https://doi.org/10.5121/csit.2018.81705openalex-snapshot
PHISHING DETECTION FROM URLS BY USING NEURAL NETWORKS
inferredcites →https://doi.org/10.5121/ijcnc.2020.12503openalex-snapshot
A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Enhancing Mobile Cybersecurity: Smishing Detection Using Ensemble Learning and Smote
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Enhanced Phishing Website Detection Through Effective Feature Selection With Time‐Varying Mirrored S‐Shaped Transfer Function
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AI-Powered Cyber Sentinel: Advanced Phishing Website Detection for Enhanced Online Security
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Unveiling the Shadows: Anticipating Future Cyber Risks and Their Impacts on Businesses
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LLM-structured expert feedback for knowledge calibration of deep learning model to detect phishing URL
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AI-based real-time anomaly detection in industrial engineering: A structured literature review, taxonomy, and research agenda
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A comprehensive review of malicious URLs: Detection techniques, features and datasets
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The applicability of a hybrid framework for automated phishing detection
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Malicious SMS detection using ensemble learning and SMOTE to improve mobile cybersecurity
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An explainable feature selection framework for web phishing detection with machine learning
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ICSA: Intelligent chatbot security assistant using Text-CNN and multi-phase real-time defense against SNS phishing attacks
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Psychology of phishing emails: Quantifying persuasion principles and simulating detection with large language models
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Ensemble transfer learning for cross-dataset phishing detection
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Phishing Website Detection Using a Stacking Ensemble Classifier of Random Forest, LightGBM, and CatBoost
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Explainable phishing website detection for secure and sustainable cyber infrastructure
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Detecting Phishing Emails Using GloVe Word Embeddings and Ensemble Machine Learning Model
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Browsafe: Approach to Enhance Trustworthiness for Safe and Secure Web Browsing
inferred← citeshttps://doi.org/10.1063/5.0262913openalex-snapshot
Comprehensive phishing detection with GeoIP integration
inferred← citeshttps://doi.org/10.1063/5.0317813openalex-snapshot
Advancing web security: Analysis of machine learning algorithms in detection of phishing websites
inferred← citeshttps://doi.org/10.1080/19361610.2025.2508726openalex-snapshot
A Survey on Phishing Attack Taxonomy, Detection Techniques, Datasets, and Security Measures
inferred← citeshttps://doi.org/10.1093/jigpal/jzaf023openalex-snapshot
Contrastive learning of prototypical network for detecting phishing URL
inferred← citeshttps://doi.org/10.1108/ics-01-2025-0024openalex-snapshot
Exploring transparent communication for organisational cyber-resilience to sophisticated phishing attacks
inferred← citeshttps://doi.org/10.1108/ics-12-2024-0321openalex-snapshot
Unmasking persuasion in phishing: a content analysis of principles of persuasion in emails and subject lines
inferred← citeshttps://doi.org/10.1109/access.2022.3224781openalex-snapshot
Sufficiency of Ensemble Machine Learning Methods for Phishing Websites Detection
inferred← citeshttps://doi.org/10.1109/access.2023.3237798openalex-snapshot
A Survey of Intelligent Detection Designs of HTML URL Phishing Attacks
inferred← citeshttps://doi.org/10.1109/access.2023.3293063openalex-snapshot
Uncovering the Cloak: A Systematic Review of Techniques Used to Conceal Phishing Websites
inferred← citeshttps://doi.org/10.1109/access.2024.3351946openalex-snapshot
Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics
inferred← citeshttps://doi.org/10.1109/access.2024.3352629openalex-snapshot
DEPHIDES: Deep Learning Based Phishing Detection System
inferred← citeshttps://doi.org/10.1109/access.2024.3375882openalex-snapshot
Devising and Detecting Phishing Emails Using Large Language Models
inferred← citeshttps://doi.org/10.1109/access.2024.3403197openalex-snapshot
A Comprehensive Taxonomy of Social Engineering Attacks and Defense Mechanisms: Toward Effective Mitigation Strategies
inferred← citeshttps://doi.org/10.1109/access.2024.3463871openalex-snapshot
Enhancing Smishing Detection: A Deep Learning Approach for Improved Accuracy and Reduced False Positives
inferred← citeshttps://doi.org/10.1109/access.2024.3514972openalex-snapshot
A State-of-the-Art Review on Phishing Website Detection Techniques
inferred← citeshttps://doi.org/10.1109/access.2025.3561521openalex-snapshot
Toward a Better Understanding of IoT Domain Names: A Study of IoT Backend
inferred← citeshttps://doi.org/10.1109/access.2025.3591843openalex-snapshot
Enhancing Generalization in Phishing URL Detection via a Fine-Tuned BERT-Based Multimodal Approach
inferred← citeshttps://doi.org/10.1109/access.2025.3599454openalex-snapshot
Machine Learning for Early Detection of Phishing URLs in Parked Domains: An Approach Applied to a Financial Institution
inferred← citeshttps://doi.org/10.1109/access.2025.3614126openalex-snapshot
PhishOFE: A Novel Machine Learning Framework for Real-Time Phishing URL Detection With Optimized Feature Engineering
inferred← citeshttps://doi.org/10.1109/access.2025.3649636openalex-snapshot
A Novel Hybrid Approach for Identification of Discriminative Features in Phishing Emails
inferred← citeshttps://doi.org/10.1109/aibthings66987.2025.11296217openalex-snapshot
A Novel Approach to Fine-tune BERT using Non-Text Features for Enhanced Ransomware Detection
inferred← citeshttps://doi.org/10.1109/aimla67915.2026.11522436openalex-snapshot
Efficient Phishing Email Classification Using TinyBERT Model with OSINT-Based Features
inferred← citeshttps://doi.org/10.1109/aimv66517.2025.11203416openalex-snapshot
Adaptive Firewall System to Predict Phishing Websites using Machine Learning Model
inferred← citeshttps://doi.org/10.1109/amict65811.2025.11402780openalex-snapshot
Enhancing Phishing Detection Through Machine Learning
inferred← citeshttps://doi.org/10.1109/asiancon58793.2023.10270550openalex-snapshot
Phishing Detection Using Machine Learning Techniques
inferred← citeshttps://doi.org/10.1109/bigdata59044.2023.10386224openalex-snapshot
Sorting the Digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection
inferred← citeshttps://doi.org/10.1109/bigdata62323.2024.10825007openalex-snapshot
Generating Phishing Attacks and Novel Detection Algorithms in the Era of Large Language Models
inferred← citeshttps://doi.org/10.1109/cict59886.2023.10455653openalex-snapshot
Discerning Phishing Websites Using Deep Learning For Cyber Security
inferred← citeshttps://doi.org/10.1109/cisct55310.2022.10046632openalex-snapshot
Machine Learning Approach for Malware Analysis and Detection
inferred← citeshttps://doi.org/10.1109/compas67506.2025.11381774openalex-snapshot
Real-Time Incremental Transformer with Continual Learning for Adaptive Phishing Email Detection
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/confluence60223.2024.10463281openalex-snapshot
CyberGuard - Guarding Against Deceptive Websites
inferred← citeshttps://doi.org/10.1109/dasa68193.2025.11499007openalex-snapshot
An Efficient Framework for Phishing Websites Detection using Machine Learning
inferred← citeshttps://doi.org/10.1109/dasc/picom/cbdcom/cy59711.2023.10361457openalex-snapshot
Phishing Detection Using Deep Learning and Machine Learning Algorithms: Comparative Analysis
inferred← citeshttps://doi.org/10.1109/dessert65323.2024.11122124openalex-snapshot
Social Engineering Attacks Models
inferred← citeshttps://doi.org/10.1109/ecrime66200.2024.00008openalex-snapshot
Owned, Pwned or Rented: Whose Domain Is It?
inferred← citeshttps://doi.org/10.1109/emergin63207.2024.10961019openalex-snapshot
Advanced Threat Detection: Leveraging Data Analytics for Cybersecurity Enhancement
inferred← citeshttps://doi.org/10.1109/i2ct57861.2023.10126165openalex-snapshot
Soft Computing and the Applications based on Machine Learning for the Healthcare Organization
inferred← citeshttps://doi.org/10.1109/ic3se62002.2024.10593139openalex-snapshot
Examination of Phishing Attempts on Web-Based Business Applications and Their Preventions
inferred← citeshttps://doi.org/10.1109/icaic60265.2024.10433827openalex-snapshot
Deep Reinforcement Learning-based Malicious URL Detection with Feature Selection
inferred← citeshttps://doi.org/10.1109/icaic67076.2026.11395765openalex-snapshot
TrustNet: A Hybrid Machine Learning and LLM-Based Multi-Agent System for Scam Website Detection
inferred← citeshttps://doi.org/10.1109/icaiic68212.2026.11454243openalex-snapshot
Enhancing Email Security: Adaptive Detection Systems for Sophisticated Threats
inferred← citeshttps://doi.org/10.1109/icaiihi57871.2023.10489233openalex-snapshot
Detection of Healthcare Phishing Websites Using Machine Learning
inferred← citeshttps://doi.org/10.1109/icaiii69475.2026.11521362openalex-snapshot
AI-Based Phishing Detection: Comparative Study of Random Forest and 1D CNN Models with Practical Deployment
inferred← citeshttps://doi.org/10.1109/icccnt56998.2023.10306583openalex-snapshot
A Comprehensive Study of Web Phishing Detection Using Machine and Deep Learning Architecture
inferred← citeshttps://doi.org/10.1109/icce59016.2024.10444468openalex-snapshot
Enhancing Email Security in Consumer Electronics with a Hybrid Deep Learning Approach
inferred← citeshttps://doi.org/10.1109/icce67443.2026.11449789openalex-snapshot
Sustainable Delta-Attention Based Transformer for Efficient Phishing URL Detection
inferred← citeshttps://doi.org/10.1109/iccirt59484.2024.10921870openalex-snapshot
Optimized Phishing Detection through URL Analysis By a Gradient Boosting-RNN Ensemble
inferred← citeshttps://doi.org/10.1109/iccst63435.2025.11293936openalex-snapshot
Features Analysis of All Email Components for Phishing Detection
inferred← citeshttps://doi.org/10.1109/icdsaai65575.2025.11011857openalex-snapshot
Artificial Intelligence in Phishing Detection and Analysis: A Foundational Review
inferred← citeshttps://doi.org/10.1109/icec2nt65402.2025.11380065openalex-snapshot
Phishing Detection Using Deep Learning: Evaluating ANN, RNN, LSTM, and GAN Models
inferred← citeshttps://doi.org/10.1109/icecet63943.2025.11472036openalex-snapshot
Investigating the application of CNN’s deep learning models to improve phishing detection
inferred← citeshttps://doi.org/10.1109/iceei68459.2025.11330458openalex-snapshot
Deep Learning for Web Phishing Detection: A Comparative Evaluation of CNN, LSTM, and GRU
inferred← citeshttps://doi.org/10.1109/iceeict61591.2024.10718484openalex-snapshot
Retracted: Phishing Detection System Through Hybrid Machine Learning Based on URL
inferred← citeshttps://doi.org/10.1109/icercs65898.2025.11580482openalex-snapshot
Multimodal Phishing Detection Using Graph Neural Networks, BERT, and CLIP
inferred← citeshttps://doi.org/10.1109/icetci67340.2025.11257903openalex-snapshot
Phishing Attack Detection in Ethereum Transactions with PCA-Enhanced Machine Learning
inferred← citeshttps://doi.org/10.1109/icicv62344.2024.00064openalex-snapshot
Next-Gen Phishing Detection System Based on Federated Learning Integrated CNN-LSTM for SMS Communication
inferred← citeshttps://doi.org/10.1109/iciics67880.2026.11483626openalex-snapshot
Zero-Day Phishing Detection: A Resilient Ensemble Strategy with Explainable Insights
inferred← citeshttps://doi.org/10.1109/icimtech67074.2025.11265173openalex-snapshot
Phishing Website Classification Using Tree-based and Neural Networks Model
inferred← citeshttps://doi.org/10.1109/icint65528.2025.11030883openalex-snapshot
Quantum Machine Learning for Phishing URLs Detection: Implementation and Evaluation with Qiskit
inferred← citeshttps://doi.org/10.1109/icitsif69060.2026.11609138openalex-snapshot
Client-Side Detection of Phishing Websites Using Deep Learning
inferred← citeshttps://doi.org/10.1109/icmcsi64620.2025.10883447openalex-snapshot
Phish Check: Content-Based Phishing Website Detection Web App
inferred← citeshttps://doi.org/10.1109/icnas68168.2025.11298114openalex-snapshot
Cybersecurity in E-Learning: A Literature Review on Phishing Detection Using ML and DL Techniques
inferred← citeshttps://doi.org/10.1109/icnpcv69434.2026.11708813openalex-snapshot
An Explainable Deep Learning Framework with Permutation Importance-Based Feature Selection for Phishing Website Detection
inferred← citeshttps://doi.org/10.1109/icoco67189.2025.11334132openalex-snapshot
The Efficacy of Feature Selection for Machine Learning-Based Detection of Phishing Attacks
inferred← citeshttps://doi.org/10.1109/icodsa62899.2024.10651836openalex-snapshot
Optimizing Phishing Site Detection: Analyzing the Impact of Feature Quantity in Random Forest Model
inferred← citeshttps://doi.org/10.1109/icoici62503.2024.10696243openalex-snapshot
Phishing URL Detection Using Deep Learning with CNN Models
inferred← citeshttps://doi.org/10.1109/icoici65217.2025.11253528openalex-snapshot
A Hybrid Deep Learning and Ensemble Framework for Real-Time Phishing Website Detection Using Optimized Feature Selection
inferred← citeshttps://doi.org/10.1109/iconstem65670.2025.11374838openalex-snapshot
Adaptive Phishing Threat Identification and Classification Using Hybrid ML Techniques in Cyberspace
inferred← citeshttps://doi.org/10.1109/icpcsn65854.2025.11035429openalex-snapshot
Phishing URL Detection using Machine Learning
inferred← citeshttps://doi.org/10.1109/icspc63060.2024.10862013openalex-snapshot
Enhancing Phishing Universal Resource Locator Detection Systems Using Hybrid Machine Learning Classifiers and Data Balancing Techniques
inferred← citeshttps://doi.org/10.1109/icsse58758.2023.10227205openalex-snapshot
A Consensus Protocol for Incentivizing Contribution from Decentralized Community for Machine Learning-based Scamming Website Detection
inferred← citeshttps://doi.org/10.1109/ies71033.2026.11702583openalex-snapshot
Efficient Phishing URL Detection for Trustworthy Digital Systems Using Ensemble Feature Selection
inferred← citeshttps://doi.org/10.1109/incet64471.2025.11140784openalex-snapshot
Machine Learning and Security Fake Email Detection and Intrusion Detection
inferred← citeshttps://doi.org/10.1109/isacc65211.2025.10969411openalex-snapshot
Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity
inferred← citeshttps://doi.org/10.1109/iscc61673.2024.10733689openalex-snapshot
Enhancing Phishing Detection: A Multi-Layer Ensemble Approach Integrating Machine Learning for Robust Cybersecurity
inferred← citeshttps://doi.org/10.1109/iscon57294.2023.10112184openalex-snapshot
URL based Phishing Detection using Machine Learning
inferred← citeshttps://doi.org/10.1109/isct62336.2024.10791190openalex-snapshot
Securing Smartphone from Mobile Phishing Attacks Using GoogLeNet Model
inferred← citeshttps://doi.org/10.1109/lnet.2023.3347724openalex-snapshot
A Network Model to Study the Underlying Characteristics of Phishing Sites
inferred← citeshttps://doi.org/10.1109/mipro65660.2025.11131976openalex-snapshot
Phishing Attacks in the Age of Embeddings: A Word-Vector Approach to Cyber Threats
inferred← citeshttps://doi.org/10.1109/mysurucon59703.2023.10397016openalex-snapshot
Social Engineering Threat: Phishing Detection using Machine Learning Approach
inferred← citeshttps://doi.org/10.1109/nana66698.2025.00080openalex-snapshot
ADAM: An Adversarial Domain Adaptation Architecture for Zero-Day Phishing Threats
inferred← citeshttps://doi.org/10.1109/ojcoms.2024.3462503openalex-snapshot
Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection
inferred← citeshttps://doi.org/10.1109/picc67314.2025.11291322openalex-snapshot
Phishing URL Detection with Deep Learning: Trends, Challenges, and Future Directions
inferred← citeshttps://doi.org/10.1109/punecon55413.2022.10014876openalex-snapshot
Phishing Attack Detection on Text Messages Using Machine Learning Techniques
inferred← citeshttps://doi.org/10.1109/sceecs68810.2026.11430134openalex-snapshot
Secure Phishing Analysis and Robust Training Algorithm (SPARTA) for Phishing Website Detection
inferred← citeshttps://doi.org/10.1109/sist61555.2024.10629358openalex-snapshot
CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban Area
inferred← citeshttps://doi.org/10.1109/smartgencon60755.2023.10442844openalex-snapshot
Phishing URL Classification Using K-Nearest Neighbour and Logistic Regression Machine Learning Approaches
inferred← citeshttps://doi.org/10.1109/smartgencon60755.2023.10442866openalex-snapshot
Phishing Site Detection Using Logistic Regression and Fine Tuning It Using Various Optimization Parameters
inferred← citeshttps://doi.org/10.1109/snams60348.2023.10375397openalex-snapshot
AI Methods Used for Spam Detection in Social Systems - An Overview
inferred← citeshttps://doi.org/10.1109/snams64316.2024.10883775openalex-snapshot
An Interpretable Fine-Tuned BERT Approach for Phishing URLs Detection: A Superior Alternative to Feature Engineering
inferred← citeshttps://doi.org/10.1109/trustcom63139.2024.00234openalex-snapshot
DMPA: A Compact and Effective Pipeline for Detecting Multiple Phishing Attacks
inferred← citeshttps://doi.org/10.1109/wconf61366.2024.10692034openalex-snapshot
A Comparative Study of Malware Detection in Enterprise Networks
inferred← citeshttps://doi.org/10.1109/wi-iat55865.2022.00028openalex-snapshot
LMs go Phishing: Adapting Pre-trained Language Models to Detect Phishing Emails
inferred← citeshttps://doi.org/10.1145/3633598.3633605openalex-snapshot
Deep Learning Approaches for Cyber Threat Detection and Mitigation
inferred← citeshttps://doi.org/10.1145/3706598.3714267openalex-snapshot
Judging Phishing Under Uncertainty: How Do Users Handle Inaccurate Automated Advice?
inferred← citeshttps://doi.org/10.1145/3706599.3720272openalex-snapshot
The Role of Auditory Environments and Anxiety in Detecting Phishing Emails
inferred← citeshttps://doi.org/10.1145/3711618.3711619openalex-snapshot
Meta GPT-Based Agent for Enhanced Phishing Email Detection
inferred← citeshttps://doi.org/10.1145/3716489.3728437openalex-snapshot
Evaluating Phishing Email Efficacy
inferred← citeshttps://doi.org/10.1145/3797491.3797499openalex-snapshot
A Review of Innovative Strategies for Mitigating Last-Mile Aggregation Attacks Utilizing Artificial Intelligence
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/10.11648/j.mlr.20261102.14openalex-snapshot
ERGA-Phish: Optimized Ensemble Learning Framework with Ridge Regression and Genetic Algorithm for Improved Phishing URL Detection
inferred← citeshttps://doi.org/10.1177/18724981251337385openalex-snapshot
Integrating NLP and ensemble methods for large-scale phishing email detection
inferred← citeshttps://doi.org/10.12928/telkomnika.v22i1.25516openalex-snapshot
Deep learning based phishing website detection
inferred← citeshttps://doi.org/10.17770/etr2025vol5.8504openalex-snapshot
EXPLORING THE POTENTIAL OF ARTIFICIAL INTELLIGENCE TO PREDICT CYBER ATTACKS: CREATION, EVALUATION AND COMPARATIVE ANALYSIS OF EFFECTIVE MODELS OF FINE-TUNING, RANDOM FORESTS, AND NEURAL NETWORKS
inferred← citeshttps://doi.org/10.18576/jsap/130108openalex-snapshot
Website Phishing Detection Using Machine Learning Techniques
inferred← citeshttps://doi.org/10.21015/vtse.v13i2.2129openalex-snapshot
Mitigating Cyber Threats: Machine Learning and Explainable AI for Phishing Detection
inferred← citeshttps://doi.org/10.21015/vtse.v13i4.2287openalex-snapshot
Hybrid Deep Learning Approaches Enable Intrusion Detection System for Zero-Day Phishing Detection
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2518632/v1openalex-snapshot
Intelligent Methods in Phishing Website Detection: A Systematic Literature Review
inferred← citeshttps://doi.org/10.21203/rs.3.rs-7959785/v1openalex-snapshot
A Hybrid Deep Ensemble Framework with Interpretability for Phishing URL Detection
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9519176/v1openalex-snapshot
Optimization-Driven Multi-Domain Hybrid Framework for Phishing Detection Across URL, Network, and Email Layers Using Deep Learning and Heuristic Intelligence
inferred← citeshttps://doi.org/10.2139/ssrn.5168205openalex-snapshot
Continuous Multi-Task Pre-Training for Malicious Url Detection and Webpage Classification
inferred← citeshttps://doi.org/10.2139/ssrn.5903829openalex-snapshot
Accuracy, Feature Interpretability, and Usability of LLMs in Phishing Email Detection
inferred← citeshttps://doi.org/10.2139/ssrn.6880638openalex-snapshot
PhishFusion: Domain-Adaptive Contrastive Representation Learning and Multi-Source Fusion under Distribution Shift in Phishing URL Detection
inferred← citeshttps://doi.org/10.21512/jas.v5i1.2129openalex-snapshot
Crítica y redefinición de la planeación estratégica: hacia un enfoque adaptativo
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/tma66427.2025.11096997openalex-snapshot
Do We Really Need Reference-Based Phishing Detection? Unleashing the Power of GNN
inferred← citeshttps://doi.org/10.2478/cait-2025-0028openalex-snapshot
Cybersecurity and Artificial Intelligence: Triad-Based Analysis and Attacks Review
inferred← citeshttps://doi.org/10.29304/jqcm.2023.15.2.1242openalex-snapshot
Phishing Attacks Detection by Using Support Vector Machine
inferred← citeshttps://doi.org/10.30574/gjeta.2023.15.1.0075openalex-snapshot
Assessment of existing cyber-attack detection models for web-based systems
inferred← citeshttps://doi.org/10.31185/wjps.145openalex-snapshot
Phishing Website Detection Using Machine Learning: A Review
inferred← citeshttps://doi.org/10.3233/idt-240026openalex-snapshot
Exploring GRU-based approaches with attention mechanisms for accurate phishing URL detection
inferred← citeshttps://doi.org/10.33093/ijoras.2026.8.1.1openalex-snapshot
Detection of Malicious URLs: A Deep Learning and Machine Learning Perspective
inferred← citeshttps://doi.org/10.33411/ijist/1279openalex-snapshot
An AI-Powered Browser Extension Using Roberta and XAI for Phishing Email Detection and Security Awareness
inferred← citeshttps://doi.org/10.33571/rpolitec.v22n43a5openalex-snapshot
Ethical hacking based on trust tests: A vision oriented to cybersecurity and risk mitigation
inferred← citeshttps://doi.org/10.3389/fbloc.2024.1484894openalex-snapshot
MLPhishChain: a machine learning-based blockchain framework for reducing phishing threats
inferred← citeshttps://doi.org/10.3389/fcomp.2025.1582206openalex-snapshot
Component features based enhanced phishing website detection system using EfficientNet, FH-BERT, and SELU-CRNN methods
inferred← citeshttps://doi.org/10.3390/a18100599openalex-snapshot
Evaluating the Effectiveness of Large Language Models (LLMs) Versus Machine Learning (ML) in Identifying and Detecting Phishing Email Attempts
inferred← citeshttps://doi.org/10.3390/a19090741openalex-snapshot
HIEF: An Interpretable Evidence-Fusion Framework for Phishing Email Detection with Decomposable Decision Uncertainty and a Preliminary English–Spanish Evaluation
inferred← citeshttps://doi.org/10.3390/app12157441openalex-snapshot
Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification
inferred← citeshttps://doi.org/10.3390/app15073431openalex-snapshot
Research on the Credulity of Spear-Phishing Attacks for Lithuanian Education Institutions’ Employees
inferred← citeshttps://doi.org/10.3390/app16168140openalex-snapshot
Phishing-Safe URL Recommendation with Open Large Language Models via Exposure-Minimizing Admission Control
inferred← citeshttps://doi.org/10.3390/computers12060118openalex-snapshot
Unbalanced Web Phishing Classification through Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.3390/electronics12010232openalex-snapshot
A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN
inferred← citeshttps://doi.org/10.3390/electronics12204261openalex-snapshot
Phishing Email Detection Model Using Deep Learning
inferred← citeshttps://doi.org/10.3390/electronics12214545openalex-snapshot
A Systematic Review on Deep-Learning-Based Phishing Email Detection
inferred← citeshttps://doi.org/10.3390/electronics13101839openalex-snapshot
Analysis and Prevention of AI-Based Phishing Email Attacks
inferred← citeshttps://doi.org/10.3390/electronics14081581openalex-snapshot
PhiShield: An AI-Based Personalized Anti-Spam Solution with Third-Party Integration
inferred← citeshttps://doi.org/10.3390/electronics15020350openalex-snapshot
Hybridizing Explainable AI (XAI) for Intelligent Feature Extraction in Phishing Website Detection
inferred← citeshttps://doi.org/10.3390/electronics15122606openalex-snapshot
Explainable Multi-Agent LLM Framework for Phishing Email Detection via Role-Specialized Evidence Decomposition
inferred← citeshttps://doi.org/10.3390/electronics15194436openalex-snapshot
FNWT: A Federated Cryptographic Domain-Identity Protocol for Legally Accountable Web Origins with Freshness-Bounded Lifecycle Status and Browser Enforcement
inferred← citeshttps://doi.org/10.3390/fi16080291openalex-snapshot
Early Ransomware Detection with Deep Learning Models
inferred← citeshttps://doi.org/10.3390/fi17080331openalex-snapshot
Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
inferred← citeshttps://doi.org/10.3390/info15080425openalex-snapshot
SINNER: A Reward-Sensitive Algorithm for Imbalanced Malware Classification Using Neural Networks with Experience Replay
inferred← citeshttps://doi.org/10.3390/info16060470openalex-snapshot
Systematic Review of Graph Neural Network for Malicious Attack Detection
inferred← citeshttps://doi.org/10.3390/make6010018openalex-snapshot
Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models
inferred← citeshttps://doi.org/10.3390/make8040086openalex-snapshot
Quantum Machine Learning for Phishing Detection: A Systematic Review of Current Techniques, Challenges, and Future Directions
inferred← citeshttps://doi.org/10.3390/sci6040074openalex-snapshot
Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk
inferred← citeshttps://doi.org/10.3390/sym15010180openalex-snapshot
A Novel Phishing Website Detection Model Based on LightGBM and Domain Name Features
inferred← citeshttps://doi.org/10.37394/232018.2025.13.51openalex-snapshot
A Survey on URL Phishing Attacks Detection using Deep Learning Models
inferred← citeshttps://doi.org/10.38124/ijisrt/ijisrt24apr2271openalex-snapshot
Using Deep Learning Algorithm in Security Informatics
inferred← citeshttps://doi.org/10.4018/979-8-2600-0413-5.ch006openalex-snapshot
LLM-Based Autonomous Agents for Phishing Detection, Triage, and Response
inferred← citeshttps://doi.org/10.4018/979-8-3693-7540-2.ch008openalex-snapshot
Phishing URL Detection Using BiLSTM With Attention Mechanism
inferred← citeshttps://doi.org/10.4018/979-8-3693-8784-9.ch002openalex-snapshot
Phishing Detection Methods
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