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

TL;DR

This paper introduces a deep learning framework using hierarchical LSTMs and attention mechanisms to improve the detection of phishing emails by analyzing textual content at multiple levels.

Contribution

It proposes a novel hierarchical LSTM model with supervised attention for anti-phishing, demonstrating its effectiveness in cybersecurity text classification tasks.

Findings

Effective detection of phishing emails demonstrated

Hierarchical LSTMs improve text analysis accuracy

Attention mechanisms enhance model interpretability

Abstract

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.