Phishing Detection Systems Evaluated Against LLM-Rephrased Emails
/ 4 min read
Quick take - The study evaluates the effectiveness of traditional phishing detection systems and machine learning models in identifying both original and Large Language Model (LLM)-rephrased phishing emails, revealing a significant decline in detection accuracy against the latter and highlighting the urgent need for improved security measures and regulatory oversight.
Fast Facts
- The rise of Large Language Models (LLMs) has led to more sophisticated phishing attacks, prompting a reevaluation of existing phishing defenses.
- A study assessed traditional phishing detection systems and machine learning models, revealing a significant decline in detection accuracy against LLM-rephrased emails.
- Companies like NTT Security Holdings and JPMorgan Chase are exploring LLMs for improving phishing detection capabilities.
- The research highlights vulnerabilities in current defenses and emphasizes the need for enhanced security measures and regulatory oversight on LLM-generated content.
- The study suggests that while traditional detectors perform well on original phishing emails, their effectiveness is compromised against those rephrased by LLMs, indicating a need for continuous advancements in detection systems.
The Rise of Sophisticated Phishing Attacks
The increasing sophistication of phishing attacks has become a pressing concern due to the rise of Large Language Models (LLMs). Attackers are employing these models to create more convincing phishing emails. This development necessitates a thorough evaluation of existing phishing defenses.
Evaluation of Phishing Detection Systems
A recent study has assessed various traditional phishing detection systems, including the Gmail Spam Filter, Apache SpamAssassin, and Proofpoint. Machine learning models like Support Vector Machines (SVM), Logistic Regression, and Naive Bayes were also evaluated. The research investigates the effectiveness of these detectors in identifying traditional phishing emails and examines their ability to detect emails rephrased using LLMs.
Companies such as NTT Security Holdings and JPMorgan Chase are exploring LLMs for phishing detection. The findings reveal a notable decline in detection accuracy across all evaluated systems, particularly when faced with LLM-rephrased emails. The study highlights significant vulnerabilities in current phishing defenses and underscores the urgent need for enhanced security measures. Regulatory oversight on LLM-generated content is also deemed necessary.
Methodology and Experimental Results
The study aims to improve Cyber Threat Intelligence (CTI) by utilizing LLMs to generate diverse phishing variants for data augmentation. LLMs are known for their ability to produce human-like text. The paper is structured into several sections, including related works, methodology, experimental results, discussions, limitations, and conclusions.
Phishing emails exploit human psychology to deceive recipients, resulting in substantial financial losses for organizations. Traditional phishing detection methods have been effective in the past; however, they face increasing challenges due to advancements in LLM technologies. LLMs have shown superior performance compared to domain experts in cybersecurity benchmarks, complicating the detection of phishing attempts. Attackers can leverage prompt engineering techniques, including zero-shot and few-shot prompting, allowing for the generation of targeted phishing emails with minimal effort.
For the experimental part of the study, two primary datasets were utilized. The Nazario dataset included 1,200 emails, evenly split between legitimate and phishing messages. The Nigerian Fraud dataset comprised 800 emails with a similar division, focusing on financial scams. Various phishing detection systems and machine learning models were tested using both original and rephrased phishing emails, applying different text encoding techniques. The TF-IDF encoding method demonstrated the highest accuracy in the experiments.
Findings and Future Directions
Evaluation metrics for the study included true positives, true negatives, false positives, and false negatives, with accuracy, precision, recall, and F1 score also considered. Results indicated a significant shift in detection effectiveness when comparing traditional phishing emails to those rephrased by LLMs. Traditional phishing messages were easier to detect due to specific keywords, while rephrased emails employed more legitimate-sounding language, complicating detection efforts.
The capability of LLMs to generate personalized phishing messages at scale poses an increasing challenge. The performance of phishing detectors saw a notable decline with rephrased emails, particularly evident in zero-shot and few-shot scenarios. However, the introduction of LLM-augmented datasets improved detection capabilities. The study stresses the need for continuous advancements in phishing detection systems to adapt to evolving threats.
Limitations of the research include its focus on English-language datasets and the lack of exploration into the fine-tuning of smaller language models. Future research should aim to incorporate diverse language datasets and investigate the effects of fine-tuning on model performance. The study concludes that traditional phishing detectors perform well on original emails, but their effectiveness is significantly compromised against LLM-rephrased emails. Leveraging LLMs for data augmentation presents a promising strategy to enhance the robustness of phishing detectors and help adapt to new phishing tactics.
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