APOLLO: A GPT-based tool to detect phishing emails and generate explanations that warn users

• URL: http://arxiv.org/abs/2410.07997v1
• Date: Thu, 10 Oct 2024 14:53:39 GMT
• Title: APOLLO: A GPT-based tool to detect phishing emails and generate explanations that warn users
• Authors: Giuseppe Desolda, Francesco Greco, Luca Viganò,
• Abstract summary: Large Language Models (LLMs) offer significant promise for text processing in various domains. We present APOLLO, a tool based on OpenAI's GPT-4o to detect phishing emails and generate explanation messages. We also conducted a study with 20 participants, comparing four different explanations presented as phishing warnings.
• Score: 2.3618982787621
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phishing is one of the most prolific cybercriminal activities, with attacks becoming increasingly sophisticated. It is, therefore, imperative to explore novel technologies to improve user protection across both technical and human dimensions. Large Language Models (LLMs) offer significant promise for text processing in various domains, but their use for defense against phishing attacks still remains scarcely explored. In this paper, we present APOLLO, a tool based on OpenAI's GPT-4o to detect phishing emails and generate explanation messages to users about why a specific email is dangerous, thus improving their decision-making capabilities. We have evaluated the performance of APOLLO in classifying phishing emails; the results show that the LLM models have exemplary capabilities in classifying phishing emails (97 percent accuracy in the case of GPT-4o) and that this performance can be further improved by integrating data from third-party services, resulting in a near-perfect classification rate (99 percent accuracy). To assess the perception of the explanations generated by this tool, we also conducted a study with 20 participants, comparing four different explanations presented as phishing warnings. We compared the LLM-generated explanations to four baselines: a manually crafted warning, and warnings from Chrome, Firefox, and Edge browsers. The results show that not only the LLM-generated explanations were perceived as high quality, but also that they can be more understandable, interesting, and trustworthy than the baselines. These findings suggest that using LLMs as a defense against phishing is a very promising approach, with APOLLO representing a proof of concept in this research direction.

Related papers

• Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts [2.6012482282204004]
  Phishing, a prevalent cybercrime tactic for decades, remains a significant threat in today's digital world. This paper aims to analyze the effectiveness of 15 Large Language Models (LLMs) in detecting phishing attempts.
  arXiv  Detail & Related papers  (2024-04-23T19:55:18Z)
• 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 Innovative Information Theory-based Approach to Tackle and Enhance The Transparency in Phishing Detection [23.962076093344166]
  We propose an innovative deep learning-based approach for phishing attack localization. Our method can not only predict the vulnerability of the email data but also automatically learn and figure out the most important and phishing-relevant information.
  arXiv  Detail & Related papers  (2024-02-27T00:03:07Z)
• ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings [58.82536530615557]
  We propose an Adversarial Suffix Embedding Translation Framework (ASETF) to transform continuous adversarial suffix embeddings into coherent and understandable text. Our method significantly reduces the computation time of adversarial suffixes and achieves a much better attack success rate to existing techniques.
  arXiv  Detail & Related papers  (2024-02-25T06:46:27Z)
• Prompted Contextual Vectors for Spear-Phishing Detection [45.07804966535239]
  Spear-phishing attacks present a significant security challenge. We propose a detection approach based on a novel document vectorization method. Our method achieves a 91% F1 score in identifying LLM-generated spear-phishing emails.
  arXiv  Detail & Related papers  (2024-02-13T09:12:55Z)
• From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude [3.7741995290294943]
  This study explores the potential of using four popular commercially available Large Language Models to generate phishing attacks. We build a BERT-based automated detection tool that can be used for the early detection of malicious prompts. Our model is transferable across all four commercial LLMs, attaining an average accuracy of 96% for phishing website prompts and 94% for phishing email prompts.
  arXiv  Detail & Related papers  (2023-10-29T22:52:40Z)
• Baseline Defenses for Adversarial Attacks Against Aligned Language Models [109.75753454188705]
  Recent work shows that text moderations can produce jailbreaking prompts that bypass defenses. We look at three types of defenses: detection (perplexity based), input preprocessing (paraphrase and retokenization), and adversarial training. We find that the weakness of existing discretes for text, combined with the relatively high costs of optimization, makes standard adaptive attacks more challenging for LLMs.
  arXiv  Detail & Related papers  (2023-09-01T17:59:44Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)
• Targeted Phishing Campaigns using Large Scale Language Models [0.0]
  Phishing emails are fraudulent messages that aim to trick individuals into revealing sensitive information or taking actions that benefit the attackers. We propose a framework for evaluating the performance of NLMs in generating these types of emails based on various criteria, including the quality of the generated text. Our evaluations show that NLMs are capable of generating phishing emails that are difficult to detect and that have a high success rate in tricking individuals, but their effectiveness varies based on the specific NLM and training data used.
  arXiv  Detail & Related papers  (2022-12-30T03:18:05Z)
• 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)
• Phishing and Spear Phishing: examples in Cyber Espionage and techniques to protect against them [91.3755431537592]
  Phishing attacks have become the most used technique in the online scams, initiating more than 91% of cyberattacks, from 2012 onwards. This study reviews how Phishing and Spear Phishing attacks are carried out by the phishers, through 5 steps which magnify the outcome.
  arXiv  Detail & Related papers  (2020-05-31T18:10:09Z)

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.