An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

• URL: http://arxiv.org/abs/2311.04913v2
• Date: Sun, 12 Nov 2023 16:32:16 GMT
• Title: An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach
• Authors: Suhaima Jamal and Hayden Wimmer
• Abstract summary: We present IPSDM, our model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phishing and spam detection is long standing challenge that has been the subject of much academic research. Large Language Models (LLM) have vast potential to transform society and provide new and innovative approaches to solve well-established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic-based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential for the potential of society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, our model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. This work serves as an important first step towards employing LLMs to improve the security of our information systems.

Related papers

• A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
  arXiv  Detail & Related papers  (2024-07-10T06:57:58Z)
• Benchmarking Trustworthiness of Multimodal Large Language Models: A Comprehensive Study [51.19622266249408]
  MultiTrust is the first comprehensive and unified benchmark on the trustworthiness of MLLMs. Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts. Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks.
  arXiv  Detail & Related papers  (2024-06-11T08:38:13Z)
• Large Language Models for Cyber Security: A Systematic Literature Review [14.924782327303765]
  We conduct a comprehensive review of the literature on the application of Large Language Models in cybersecurity (LLM4Security) We observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training.
  arXiv  Detail & Related papers  (2024-05-08T02:09:17Z)
• 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)
• An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach [2.8282906214258805]
  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. Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape. 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)
• Detecting Scams Using Large Language Models [19.7220607313348]
  Large Language Models (LLMs) have gained prominence in various applications, including security. This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity. We propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams.
  arXiv  Detail & Related papers  (2024-02-05T16:13:54Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• Factuality Challenges in the Era of Large Language Models [113.3282633305118]
  Large Language Models (LLMs) generate false, erroneous, or misleading content. LLMs can be exploited for malicious applications. This poses a significant challenge to society in terms of the potential deception of users.
  arXiv  Detail & Related papers  (2023-10-08T14:55:02Z)
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies [104.32199881187607]
  Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. This paper presents a comprehensive review of this emerging class of techniques.
  arXiv  Detail & Related papers  (2023-08-06T18:38:52Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)
• Spear Phishing With Large Language Models [3.2634122554914002]
  This study explores how large language models (LLMs) can be used for spear phishing. I create unique spear phishing messages for over 600 British Members of Parliament using OpenAI's GPT-3.5 and GPT-4 models. My findings provide some evidence that these messages are not only realistic but also cost-effective, with each email costing only a fraction of a cent to generate.
  arXiv  Detail & Related papers  (2023-05-11T16:55:19Z)

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.