# Phishing Attacks Detection -- A Machine Learning-Based Approach

arXiv: https://arxiv.org/abs/2201.10752 · HTML: https://mlsanity.com/paper/2201.10752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication](https://mlsanity.com/paper/2003.07308/) (2020)
- [Defending against Phishing Attacks: Taxonomy of Methods, Current Issues and Future Directions](https://mlsanity.com/paper/1705.09819/) (2017)

## Built on by (most cited first, 3 of 3)

- [Debate-Driven Multi-Agent LLMs for Phishing Email Detection](https://mlsanity.com/paper/2503.22038/) (2025)
- [Cyri: A Conversational AI-based Assistant for Supporting the Human User in Detecting and Responding to Phishing Attacks](https://mlsanity.com/paper/2502.05951/) (2025)
- [Every Character Counts: From Vulnerability to Defense in Phishing Detection](https://mlsanity.com/paper/2509.20589/) (2025)

## Abstract (arXiv)

Phishing attacks are one of the most common social engineering attacks targeting users emails to fraudulently steal confidential and sensitive information. They can be used as a part of more massive attacks launched to gain a foothold in corporate or government networks. Over the last decade, a number of anti-phishing techniques have been proposed to detect and mitigate these attacks. However, they are still inefficient and inaccurate. Thus, there is a great need for efficient and accurate detection techniques to cope with these attacks. In this paper, we proposed a phishing attack detection technique based on machine learning. We collected and analyzed more than 4000 phishing emails targeting the email service of the University of North Dakota. We modeled these attacks by selecting 10 relevant features and building a large dataset. This dataset was used to train, validate, and test the machine learning algorithms. For performance evaluation, four metrics have been used, namely probability of detection, probability of miss-detection, probability of false alarm, and accuracy. The experimental results show that better detection can be achieved using an artificial neural network.