# A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing

arXiv: https://arxiv.org/abs/1805.01554 · HTML: https://mlsanity.com/paper/1805.01554/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 4 of 4)

- [Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection](https://mlsanity.com/paper/2110.06025/) (2021)
- [SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection](https://mlsanity.com/paper/2406.06663/) (2024)
- [From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention](https://mlsanity.com/paper/2102.09136/) (2021)
- [AdaPhish: AI-Powered Adaptive Defense and Education Resource Against Deceptive Emails](https://mlsanity.com/paper/2502.03622/) (2025)

## Abstract (arXiv)

Anti-phishing aims to detect phishing content/documents in a pool of textual data. This is an important problem in cybersecurity that can help to guard users from fraudulent information. Natural language processing (NLP) offers a natural solution for this problem as it is capable of analyzing the textual content to perform intelligent recognition. In this work, we investigate state-of-the-art techniques for text categorization in NLP to address the problem of anti-phishing for emails (i.e, predicting if an email is phishing or not). These techniques are based on deep learning models that have attracted much attention from the community recently. In particular, we present a framework with hierarchical long short-term memory networks (H-LSTMs) and attention mechanisms to model the emails simultaneously at the word and the sentence level. Our expectation is to produce an effective model for anti-phishing and demonstrate the effectiveness of deep learning for problems in cybersecurity.