Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing

• URL: http://arxiv.org/abs/2510.11915v1
• Date: Mon, 13 Oct 2025 20:34:19 GMT
• Title: Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
• Authors: Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar, Nidhi Rastogi, Quamar Niyaz,
• Abstract summary: We propose a robust phishing email detection system featuring an enhanced text preprocessing pipeline.<n>Our approach integrates widely adopted natural language processing (NLP) feature extraction techniques and machine learning algorithms.<n>We evaluate our models on publicly available datasets comprising both phishing and legitimate emails, achieving a detection accuracy of 94.26% and F1-score of 84.39%.
• Score: 3.3166006294048427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing malicious content. Unlike earlier phishing attempts which are identifiable by grammatical errors, misspellings, incorrect phrasing, and inconsistent formatting, LLM generated emails are grammatically sound, contextually relevant, and linguistically natural. These advancements make phishing emails increasingly difficult to distinguish from legitimate ones, challenging traditional detection mechanisms. Conventional phishing detection systems often fail when faced with emails crafted by LLMs or manipulated using adversarial perturbation techniques. To address this challenge, we propose a robust phishing email detection system featuring an enhanced text preprocessing pipeline. This pipeline includes spelling correction and word splitting to counteract adversarial modifications and improve detection accuracy. Our approach integrates widely adopted natural language processing (NLP) feature extraction techniques and machine learning algorithms. We evaluate our models on publicly available datasets comprising both phishing and legitimate emails, achieving a detection accuracy of 94.26% and F1-score of 84.39% in model deployment setting. To assess robustness, we further evaluate our models using adversarial phishing samples generated by four attack methods in Python TextAttack framework. Additionally, we evaluate models' performance against phishing emails generated by LLMs including ChatGPT and Llama. Results highlight the resilience of models against evolving AI-powered phishing threats.

Related papers

• STEAD: Robust Provably Secure Linguistic Steganography with Diffusion Language Model [71.35577462669856]
  We propose a robust, provably secure linguistic steganography with diffusion language models (DLMs)<n>We introduce error correction strategies, including pseudo-random error correction and neighborhood search correction, during steganographic extraction.
  arXiv  Detail & Related papers  (2026-01-21T08:58:12Z)
• Phishing Email Detection Using Large Language Models [4.751410425426722]
  Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion.<n>Current Large Language Models (LLMs) require substantial hardening before deployment in email security systems.<n>This paper proposes LLMPEA, an LLM-based framework to detect phishing email attacks across multiple attack vectors.
  arXiv  Detail & Related papers  (2025-12-10T21:50:52Z)
• Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors [77.82885394684202]
  We propose textbfContrastive textbfParaphrase textbfAttack (CoPA), a training-free method that effectively deceives text detectors.<n>CoPA constructs an auxiliary machine-like word distribution as a contrast to the human-like distribution generated by large language models.<n>Our theoretical analysis suggests the superiority of the proposed attack.
  arXiv  Detail & Related papers  (2025-05-21T10:08:39Z)
• Debate-Driven Multi-Agent LLMs for Phishing Email Detection [0.0]
  We propose a multi-agent large language model (LLM) prompting technique that simulates deceptive debates among agents to detect phishing emails.<n>Our approach uses two LLM agents to present arguments for or against the classification task, with a judge agent adjudicating the final verdict.<n>Results show that the debate structure itself is sufficient to yield accurate decisions without extra prompting strategies.
  arXiv  Detail & Related papers  (2025-03-27T23:18:14Z)
• Next-Generation Phishing: How LLM Agents Empower Cyber Attackers [10.067883724547182]
  The escalating threat of phishing emails has become increasingly sophisticated with the rise of Large Language Models (LLMs) As attackers exploit LLMs to craft more convincing and evasive phishing emails, it is crucial to assess the resilience of current phishing defenses. We conduct a comprehensive evaluation of traditional phishing detectors, such as Gmail Spam Filter, Apache SpamAssassin, and Proofpoint, as well as machine learning models like SVM, Logistic Regression, and Naive Bayes. Our results reveal notable declines in detection accuracy for rephrased emails across all detectors, highlighting critical weaknesses in current phishing defenses.
  arXiv  Detail & Related papers  (2024-11-21T06:20:29Z)
• ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection [2.3999111269325266]
  This study introduces ChatSpamDetector, a system that uses large language models (LLMs) to detect phishing emails. By converting email data into a prompt suitable for LLM analysis, the system provides a highly accurate determination of whether an email is phishing or not. We conducted an evaluation using a comprehensive phishing email dataset and compared our system to several LLMs and baseline systems.
  arXiv  Detail & Related papers  (2024-02-28T06:28:15Z)
• An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach [2.1301560294088318]
  Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm.<n>Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape.<n>We present an optimized, fine-tuned transformer-based DistilBERT model designed for the detection of phishing emails.
  arXiv  Detail & Related papers  (2024-02-21T15:23:21Z)
• Prompted Contextual Vectors for Spear-Phishing Detection [41.26408609344205]
  Spear-phishing attacks present a significant security challenge.<n>We propose a detection approach based on a novel document vectorization method.<n>Our method achieves a 91% F1 score in identifying LLM-generated spear-phishing emails.
  arXiv  Detail & Related papers  (2024-02-13T09:12:55Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text [219.15486286590016]
  This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.
  arXiv  Detail & Related papers  (2021-10-10T17:19:37Z)
• Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes [81.85509264573948]
  In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier. In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool. In this work, we aim to design information embedding attacks that are verifiable and robust against popular post-processing methods.
  arXiv  Detail & Related papers  (2020-10-26T17:42:42Z)
• Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding [80.3811072650087]
  We study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training. AWT is the first end-to-end model to hide data in text by automatically learning -- without ground truth -- word substitutions along with their locations.
  arXiv  Detail & Related papers  (2020-09-07T11:01:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.