Machine Learning Applications in Phishing Detection
Summary
Phishing remains a pervasive cyber-threat in which adversaries masquerade as legitimate entities to deceive users and harvest credentials or sensitive data. Machine learning has emerged as a vital defence, enabling automated analysis of vast volumes of suspicious content. Traditional approaches rely on handcrafted features extracted from email headers, body text or URL structures, feeding classifiers such as logistic regression or random forests. More recent techniques employ deep learning architectures that ingest raw URL strings or rendered page snapshots, learning hierarchical representations without manual feature engineering. Hybrid and ensemble methods further bolster robustness, combining distinct model types to mitigate false positives and track evolving phishing tactics. Explainability frameworks are now being introduced to clarify model decisions, fostering trust and facilitating compliance. Collectively, these advances have driven detection accuracies above 95 per cent and sub-millisecond latency in online settings, underlining the global significance of ML-driven defences in safeguarding financial, governmental and personal assets.
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Machine Learning Applications in Phishing Detection publication trend
The graph below shows the total number of articles in machine learning applications in phishing detection across all publications each year (not limited to Nature Index journals).
Technical terms
Phishing: A fraudulent technique in which attackers impersonate trusted entities to obtain sensitive information from users.
Machine learning: A branch of artificial intelligence in which algorithms learn predictive patterns from data without explicit programming of rules.
Feature engineering: The process of extracting informative attributes from raw data to improve model performance.
Convolutional neural network (CNN): A deep-learning architecture that excels at recognising local patterns through convolutional filters.
Long short-term memory (LSTM): A recurrent neural network variant designed to capture long-range dependencies in sequential data.
Mutual information: A statistical measure of the dependency between variables, used to select features that carry the most predictive power.
Ensemble learning: A methodology that combines multiple models to produce more accurate and stable predictions than any individual model.
References
- Mutual information based logistic regression for phishing URL detection. Cyber Security and Applications (2024).
- A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics (2023).
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