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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Jadhav, Ashvini
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Computer Science & IT Engineering

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Survey and comparative analysis of phishing detection techniques: current trends, challenges, and future directions Jadhav, Ashvini; Chandre, Pankaj R.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp853-866

Abstract

In the age of digital communication, scams such as phishing continue to be a problem, necessitating the need for ever-more-advanced detection techniques to safeguard sensitive data. Examining several methods now in use, this review article groups them according to the application (email, web server, mail server, or browser-based). It explores the advantages and disadvantages of behavior-based, heuristic-based, machine learning (ML)-based, and signature-based techniques and offers a comparative evaluation of their efficacy. The essay delves deeper into the latest developments in phishing detection research, such as ML-powered social media exploration and real-time website analysis. The evaluation goes beyond just identifying detecting techniques; it also includes a data-driven analysis. In particular, random forest and support vector machines are ML algorithms that regularly produce results with high accuracy for detecting phishing attempts. Metrics like as recall, F1-score, and precision show how well these algorithms. Furthermore, specialised techniques such as heuristic-based and cantina-based approaches provide remarkable performance, underscoring the possibility of additional research in this field. Future research explores improved phishing detection through: better accuracy with ML, integrating new technologies, analyzing user behavior. A hybrid approach combining these techniques offers a stronger defense.
A comprehensive review of interpretable machine learning techniques for phishing attack detection Chandre, Pankaj Ramchandra; Bhujbal, Pallavi; Jadhav, Ashvini; Dinesh Shendkar, Bhagyashree; Wangikar, Aditi; Sachdeo, Rajneeshkaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3022-3032

Abstract

Phishing attacks remain a significant and evolving threat in the digital landscape, demanding continual advancements in detection methodologies. This paper emphasizes the importance of interpretable machine learning models to enhance transparency and trustworthiness in phishing detection systems. It begins with an overview of phishing attacks, their increasing sophistication, and the challenges faced by conventional detection techniques. A range of interpretable machine learning approaches, including rule-based models, decision trees, and additive models like Shapley additive explanations (SHAP), are surveyed. Their applicability in phishing detection is analyzed based on computational efficiency, prediction accuracy, and interpretability. The study also explores ways to integrate these methods into existing detection systems to enhance functionality and user experience. By providing insights into the decision-making processes of detection models, interpretable machine learning facilitates human supervision and intervention, strengthening overall system reliability. The paper concludes by outlining future research directions, such as improving the scalability, accuracy, and adaptability of interpretable models to detect emerging phishing techniques. Integrating these models with real-time threat intelligence and deep learning approaches could boost accuracy while preserving transparency. Additionally, user-centric explanations and human-in-the-loop systems may further enhance trust, usability, and resilience in phishing detection frameworks.
Co-Authors Bhujbal, Pallavi Chandre, Pankaj R. Chandre, Pankaj Ramchandra Dinesh Shendkar, Bhagyashree Sachdeo, Rajneeshkaur Wangikar, Aditi