Leveraging Large Language Models for Phishing Email Detection

Основные понятия

The author introduces ChatSpamDetector, a system that utilizes large language models to accurately detect phishing emails and provide detailed explanations for its determinations.

Аннотация

ChatSpamDetector is a system that leverages large language models to detect phishing emails with high accuracy. It provides detailed reasoning for its classifications, aiding users in making informed decisions about suspicious emails. Evaluation experiments showed superior performance compared to baseline systems.

The proliferation of phishing sites and emails poses challenges despite existing cybersecurity efforts. Users often struggle with false positives in spam filters, risking missing important communications or falling for phishing attempts. ChatSpamDetector offers accurate detection and detailed explanations to combat email-based phishing threats effectively.

The system converts email data into prompts suitable for analysis by large language models, enabling advanced contextual interpretation to identify various phishing tactics and impersonations. Through comprehensive evaluations, ChatSpamDetector using GPT-4 achieved an impressive accuracy of 99.70%.

Key points include the importance of understanding why emails are flagged as spam, the need for accurate phishing detection methods, and the effectiveness of large language models in identifying deceptive strategies in emails.

Статистика

Our system using GPT-4 achieved an accuracy of 99.70%.
The dataset consisted of 1,010 phishing emails spanning 19 languages.
Baseline systems showed accuracies ranging from 54.53% to 86.22%.

Цитаты

"Users often struggle to understand why emails are flagged as spam."
"ChatSpamDetector provides detailed reasoning for its phishing determinations."
"Our system outperformed existing baseline systems with an accuracy of 99.70%."

Ключевые выводы из

ChatSpamDetector

by Takashi Koid... в arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18093.pdf

Дополнительные вопросы

How can ChatSpamDetector adapt to new types of phishing attacks?

ChatSpamDetector can adapt to new types of phishing attacks by continuously updating its dataset with the latest phishing emails. By regularly collecting and analyzing new examples of phishing attempts, the system can learn to recognize emerging patterns and tactics used by attackers. Additionally, incorporating feedback mechanisms that allow users to report suspicious emails as well as leveraging external threat intelligence sources can enhance the system's ability to detect novel phishing techniques. Moreover, implementing a robust training pipeline that includes retraining the model with updated data on a regular basis will ensure that ChatSpamDetector remains effective against evolving threats.

How can user education be improved to prevent falling victim to sophisticated phishing techniques?

User education plays a crucial role in preventing individuals from falling victim to sophisticated phishing techniques. To improve user awareness and resilience against such attacks, organizations should invest in comprehensive cybersecurity training programs that cover topics like recognizing common red flags in phishing emails, understanding social engineering tactics employed by cybercriminals, and practicing good email hygiene habits (e.g., not clicking on suspicious links or downloading attachments from unknown senders). Simulated phishing exercises can also be beneficial in providing hands-on experience for users to identify and respond appropriately to potential threats.

What are the ethical considerations when implementing large language models in cybersecurity?

When implementing large language models (LLMs) in cybersecurity, several ethical considerations must be taken into account. Firstly, there is a concern about bias within LLMs, which may inadvertently perpetuate discriminatory practices if not properly addressed during model development and training. Transparency regarding how LLMs make decisions is essential for ensuring accountability and trustworthiness in their use for cybersecurity purposes. Additionally, privacy issues arise when LLMs process sensitive information contained within emails or other communication channels. Safeguarding user data and ensuring compliance with data protection regulations are paramount when deploying LLMs for security applications.