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Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review
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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/s00500-018-3628-5openalex-snapshot
A comparison of random forest based algorithms: random credal random forest versus oblique random forest
inferredcites →https://doi.org/10.1007/s10115-022-01672-xopenalex-snapshot
Applications of deep learning for phishing detection: a systematic literature review
inferredcites →https://doi.org/10.1007/s10586-022-03604-4openalex-snapshot
An intelligent cyber security phishing detection system using deep learning techniques
inferredcites →https://doi.org/10.1007/s40747-022-00760-3openalex-snapshot
Cloud-based email phishing attack using machine and deep learning algorithm
inferredcites →https://doi.org/10.1016/j.comnet.2022.108826openalex-snapshot
Efficient spam and phishing emails filtering based on deep learning
inferredcites →https://doi.org/10.1016/j.heliyon.2019.e01802openalex-snapshot
Machine learning for email spam filtering: review, approaches and open research problems
inferredcites →https://doi.org/10.1016/j.ins.2014.02.114openalex-snapshot
Study on the effectiveness of anomaly detection for spam filtering
inferredcites →https://doi.org/10.1016/j.jcss.2014.02.005openalex-snapshot
A survey of emerging threats in cybersecurity
inferredcites →https://doi.org/10.1016/j.jnca.2022.103545openalex-snapshot
HELPHED: Hybrid Ensemble Learning PHishing Email Detection
inferredcites →https://doi.org/10.1016/j.knosys.2011.10.001openalex-snapshot
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
inferredcites →https://doi.org/10.1038/s41598-022-10841-5openalex-snapshot
An effective detection approach for phishing websites using URL and HTML features
inferredcites →https://doi.org/10.1088/1755-1315/252/4/042051openalex-snapshot
Phishing Email Detection Based on Hybrid Features
inferredcites →https://doi.org/10.1108/jfc-02-2020-0026openalex-snapshot
Exploiting trust for financial gain: an overview of business email compromise (BEC) fraud
inferredcites →https://doi.org/10.1108/joic-02-2017-0001openalex-snapshot
Business email compromise and executive impersonation: are financial institutions exposed?
inferredcites →https://doi.org/10.1109/access.2019.2954791openalex-snapshot
A Comprehensive Survey for Intelligent Spam Email Detection
inferredcites →https://doi.org/10.1109/access.2022.3183083openalex-snapshot
A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques
inferredcites →https://doi.org/10.1109/acmi53878.2021.9528204openalex-snapshot
The Emergence Threat of Phishing Attack and The Detection Techniques Using Machine Learning Models
inferredcites →https://doi.org/10.1109/cisda.2015.7208641openalex-snapshot
Detection of compromised email accounts used for spamming in correlation with mail user agent access activities extracted from metadata
inferredcites →https://doi.org/10.1109/csci49370.2019.00071openalex-snapshot
Detection of Phishing Attacks with Machine Learning Techniques in Cognitive Security Architecture
inferredcites →https://doi.org/10.1109/csnet47905.2019.9108961openalex-snapshot
Phishing Attacks: Detecting and Preventing Infected E-mails Using Machine Learning Methods
inferredcites →https://doi.org/10.1109/cybersecpods.2017.8074848openalex-snapshot
Identifying email threats using predictive analysis
inferredcites →https://doi.org/10.1109/cybersecpods.2019.8885143openalex-snapshot
Classifying Phishing Email Using Machine Learning and Deep Learning
inferredcites →https://doi.org/10.1109/ecrime51433.2020.9493250openalex-snapshot
Empirical Analysis of Losses from Business-Email Compromise
inferredcites →https://doi.org/10.1109/gucon.2018.8674973openalex-snapshot
Email Spam Classification by Support Vector Machine
inferredcites →https://doi.org/10.1109/gucon50781.2021.9573797openalex-snapshot
Phishing URL Classification Analysis Using ANN Algorithm
inferredcites →https://doi.org/10.1109/icacite51222.2021.9404587openalex-snapshot
Business E-mail Compromise — Techniques and Countermeasures
inferredcites →https://doi.org/10.1109/icaict.2015.7338516openalex-snapshot
Translation of natural language queries to structured data sources
inferredcites →https://doi.org/10.1109/iccais48893.2020.9096821openalex-snapshot
Mitigating Email Phishing Attacks using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/iccsp.2015.7322709openalex-snapshot
Content based spam detection in email using Bayesian classifier
inferredcites →https://doi.org/10.1109/icctct.2018.8550910openalex-snapshot
Data mining a way to solve Phishing Attacks
inferredcites →https://doi.org/10.1109/icdm.2013.131openalex-snapshot
Classifying Spam Emails Using Text and Readability Features
inferredcites →https://doi.org/10.1109/icprime.2013.6496446openalex-snapshot
A study on e-mail image spam filtering techniques
inferredcites →https://doi.org/10.1109/icsc.2018.00056openalex-snapshot
Detecting Phishing Attacks Using Natural Language Processing and Machine Learning
inferredcites →https://doi.org/10.1109/icssit48917.2020.9214225openalex-snapshot
Phishing Attacks Detection using Machine Learning Approach
inferredcites →https://doi.org/10.1109/isdfs.2018.8355389openalex-snapshot
Detection of phishing attacks
inferredcites →https://doi.org/10.1109/ispa/iucc.2017.00160openalex-snapshot
Phishing Emails Detection Using CS-SVM
inferredcites →https://doi.org/10.1109/itnec48623.2020.9084766openalex-snapshot
Detection method of phishing email based on persuasion principle
inferredcites →https://doi.org/10.1109/qrs-c51114.2020.00036openalex-snapshot
A Practical Solution Against Business Email Compromise (BEC) Attack using Invoice Checksum
inferredcites →https://doi.org/10.1109/sai.2017.8252096openalex-snapshot
Employing machine learning techniques for detection and classification of phishing emails
inferredcites →https://doi.org/10.1109/uemcon53757.2021.9666627openalex-snapshot
Phishing Attacks Detection A Machine Learning-Based Approach
inferredcites →https://doi.org/10.1145/3384941.3409588openalex-snapshot
Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems
inferredcites →https://doi.org/10.13140/rg.2.2.35164.81288openalex-snapshot
A Behavioral Based Detection Approach for Business Email Compromises
inferredcites →https://doi.org/10.1371/journal.pone.0258361openalex-snapshot
Detecting phishing websites using machine learning technique
inferredcites →https://doi.org/10.14257/ijhit.2015.8.8.17openalex-snapshot
Hierarchical Reinforcement Learning Based on KNN Classification Algorithms
inferredcites →https://doi.org/10.3390/asi5040073openalex-snapshot
Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails
inferredcites →https://doi.org/10.3390/s20164540openalex-snapshot
Towards a Multi-Layered Phishing Detection
inferredcites →https://doi.org/10.3390/su14127375openalex-snapshot
Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)
inferredcites →https://doi.org/10.3844/jcssp.2021.610.623openalex-snapshot
Machine Learning and Deep Learning for Phishing Email Classification using One-Hot Encoding
inferredcites →https://doi.org/10.5120/7274-0435openalex-snapshot
Spam Mail Filtering Technique using Different Decision Tree Classifiers through Data Mining Approach - A Comparative Performance Analysis
inferredcites →https://doi.org/10.5121/ijnsa.2016.8405openalex-snapshot
An Intelligent Classification Model for Phishing Email Detection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-59100-6_17openalex-snapshot
A Novel Framework to Detect Business Email Compromise Through Unconsented Email Autoforwards
inferred← citeshttps://doi.org/10.1007/978-3-031-75016-8_10openalex-snapshot
Cyber Security in Hospitals: A Network-Oriented Model for Behavioural Learning of Employees During Phishing Simulations
inferred← citeshttps://doi.org/10.1007/978-3-032-07915-2_15openalex-snapshot
A Comprehensive Review of Cyber-Attack Detection in IoT Environments: Datasets and Approaches
inferred← citeshttps://doi.org/10.1007/978-3-032-23886-3_4openalex-snapshot
Computational Analysis of Employees Learning from Phishing Simulations
inferred← citeshttps://doi.org/10.1007/978-981-99-9589-9_5openalex-snapshot
An Exploratory Study of Automated Anti-phishing System
inferred← citeshttps://doi.org/10.1007/979-8-8688-1029-9_15openalex-snapshot
Case Studies in the E-commerce Industry
inferred← citeshttps://doi.org/10.1007/s10207-024-00851-xopenalex-snapshot
A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)
inferred← citeshttps://doi.org/10.1007/s10586-025-05896-8openalex-snapshot
An enhanced deep learning model for phishing detection based on bayesian optimization and hybrid CNN VGG19 model
inferred← citeshttps://doi.org/10.1007/s11416-024-00544-yopenalex-snapshot
Building a business email compromise research dataset with large language models
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109625openalex-snapshot
Novel interpretable and robust web-based AI platform for phishing email detection
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104630openalex-snapshot
Business email compromise: A systematic review of understanding, detection, and challenges
inferred← citeshttps://doi.org/10.1016/j.fsidi.2026.302108openalex-snapshot
Forensically-integrated machine learning model for multi-class email threat detection using a high-fidelity synthetic dataset
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/10.1080/23742917.2025.2498776openalex-snapshot
List-unsubscribe-sensible phishing email and ransomware identification using a novel LCRMNN and LBIME
inferred← citeshttps://doi.org/10.1108/978-1-83708-170-720261019openalex-snapshot
Cybercrime in the Digital Finance Ecosystem: Typologies, Terminology, and Economic Cost
inferred← citeshttps://doi.org/10.1109/access.2023.3327016openalex-snapshot
Cyber Threats Classifications and Countermeasures in Banking and Financial Sector
inferred← citeshttps://doi.org/10.1109/access.2025.3552523openalex-snapshot
A Review of Tactics, Techniques, and Procedures (TTPs) of MITRE Framework for Business Email Compromise (BEC) Attacks
inferred← citeshttps://doi.org/10.1109/acroset66531.2025.11281192openalex-snapshot
Detecting Phishing Emails Through Stylometric Analysis: A Novel NLP Approach
inferred← citeshttps://doi.org/10.1109/aiccsa66935.2025.11315219openalex-snapshot
Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis
inferred← citeshttps://doi.org/10.1109/aict61888.2024.10740431openalex-snapshot
An Approach for Business Email Compromise Detection using NLP and Deep Learning
inferred← citeshttps://doi.org/10.1109/coins69995.2026.11715603openalex-snapshot
Metadata-Driven Business Email Compromise Detection Under Adversarial Evasion: Robustness Analysis and Adaptive Recovery
inferred← citeshttps://doi.org/10.1109/eit68936.2026.11670363openalex-snapshot
Picard: Phishing Intelligence Corpus for Artificial Intelligence Research and Defense
inferred← citeshttps://doi.org/10.1109/ic2pct60090.2024.10486397openalex-snapshot
Contextual Forensic Analysis of Emails Using Machine Learning Algorithms
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/icatc64549.2024.11025302openalex-snapshot
Explainable AI for Transparent Phishing Email Detection
inferred← citeshttps://doi.org/10.1109/iccp63557.2024.10793001openalex-snapshot
A Feature Engineering Approach for Detecting Phishing Emails
inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11291841openalex-snapshot
Business Email Compromise Detection Using Random Forest Classifier with Email Header Embeddings
inferred← citeshttps://doi.org/10.1109/iccsc62048.2024.10830374openalex-snapshot
Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks
inferred← citeshttps://doi.org/10.1109/icecet63943.2025.11471908openalex-snapshot
An Evaluation of Hybrid Email Spoofing Detection: Merging Rule-based Methods with Machine Learning for Enhanced Security
inferred← citeshttps://doi.org/10.1109/icidca66325.2025.11280547openalex-snapshot
Spam Shield: A Machine Learning based Phishing Detection System
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Performance of Spam Detection Models for Complex Data: Integrative and Hybrid Approaches
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Detecting Deceptive URLs: Exploring Logistic Regression and Decision Tree Models
inferred← citeshttps://doi.org/10.1109/seeda-cecnsm61561.2023.10470563openalex-snapshot
Cognitive Email Analysis with Automated Decision Support for Business Email Compromise Prevention
inferred← citeshttps://doi.org/10.1109/sti64222.2024.10951075openalex-snapshot
PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection
inferred← citeshttps://doi.org/10.1142/s0219467827500136openalex-snapshot
A Systematic Review on Website Phishing Attack Detection for Online Users
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Business Email Compromise: A Comprehensive Taxonomy for Detection and Prevention
inferred← citeshttps://doi.org/10.14569/ijacsa.2023.0140727openalex-snapshot
Anti-Spoofing in Medical Employee's Email using Machine Learning Uclassify Algorithm
inferred← citeshttps://doi.org/10.18576/jsap/130108openalex-snapshot
Website Phishing Detection Using Machine Learning Techniques
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Phishing Attacks: Unraveling Tactics, Threats, and Defenses in the Cybersecurity Landscape
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Information Extraction from Security-Related Datasets
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Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection
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Heuristic machine learning approaches for identifying phishing threats across web and email platforms
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Multimodal framework for phishing attack detection and mitigation through behavior analysis using EM-BERT and SPCA-BASED EAI-SC-LSTM
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Balancing Efficiency and Efficacy: A Contextual Bandit-Driven Framework for Multi-Tier Cyber Threat Detection
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OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
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Business Email Compromise (BEC) Attacks: Threats, Vulnerabilities and Countermeasures—A Perspective on the Greek Landscape
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The Integration of Artificial Intelligence in Business Communication Channels: Opportunities and Challenges
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SYNTHESIZING AI'S IMPACT ON CYBERSECURITY IN TELECOMMUNICATIONS: A CONCEPTUAL FRAMEWORK
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