Phishing, whether through email, SMS, or malicious websites, poses a major
threat to organizations by using social engineering to trick users into
revealing sensitive information. It not only compromises company's data
security but also incurs significant financial losses. In this paper, we
investigate whether the remarkable performance of Large Language Models (LLMs)
can be leveraged for particular task like text classification, particularly
detecting malicious content and compare its results with state-of-the-art
Deberta V3 (DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled
Embedding Sharing) model. We systematically assess the potential and
limitations of both approaches using comprehensive public datasets comprising
diverse data sources such as email, HTML, URL, SMS, and synthetic data
generation. Additionally, we demonstrate how LLMs can generate convincing
phishing emails, making it harder to spot scams and evaluate the performance of
both models in this context. Our study delves further into the challenges
encountered by DeBERTa V3 during its training phases, fine-tuning methodology
and transfer learning processes. Similarly, we examine the challenges
associated with LLMs and assess their respective performance. Among our
experimental approaches, the transformer-based DeBERTa method emerged as the
most effective, achieving a test dataset (HuggingFace phishing dataset) recall
(sensitivity) of 95.17% closely followed by GPT-4 providing a recall of 91.04%.
We performed additional experiments with other datasets on the trained DeBERTa
V3 model and LLMs like GPT 4 and Gemini 1.5. Based on our findings, we provide
valuable insights into the effectiveness and robustness of these advanced
language models, offering a detailed comparative analysis that can inform
future research efforts in strengthening cybersecurity measures for detecting
and mitigating phishing threats.

本研究探讨了大型语言模型（LLMs）在特定任务（如文本分类，特别是检测恶意内容）中的潜力和局限，并将其结果与最先进的 DeBERTa V3 模型进行对比。通过使用包括电子邮件、HTML、URL、短信和合成数据生成的综合公共数据集，我们系统地评估了这两种方法的潜力和局限，并展示了 LLMs 如何生成具有说服力的网络钓鱼邮件以及在此背景下两种模型的性能。我们的实验结果表明，基于变形金刚的 DeBERTa 方法是最有效的，其测试数据集（HuggingFace 网络钓鱼数据集）召回率（敏感性）达到 95.17%，紧随其后的是 GPT-4，召回率为 91.04%。通过我们的研究，我们提供了这些先进语言模型的有效性和稳定性的有价值的见解，并提供了详细的比较分析，以指导未来在加强检测和减轻网络钓鱼威胁方面的研究工作。

SecureNet: DeBERTa 与大型语言模型在钓鱼检测中的比较研究

SecureNet: A Comparative Study of DeBERTa and Large Language Models for  Phishing Detection

Our analysis of inaudible attacks on voice-activated devices confirms the
alarming risk factor of 7.6 out of 10, underlining significant security
vulnerabilities scored independently by NIST National Vulnerability Database
(NVD). Our baseline network model showcases a scenario in which an attacker
uses inaudible voice commands to gain unauthorized access to confidential
information on a secured laptop. We simulated many attack scenarios on this
baseline network model, revealing the potential for mass exploitation of
interconnected devices to discover and own privileged information through
physical access without adding new hardware or amplifying device skills. Using
Microsoft's CyberBattleSim framework, we evaluated six reinforcement learning
algorithms and found that Deep-Q learning with exploitation proved optimal,
leading to rapid ownership of all nodes in fewer steps. Our findings underscore
the critical need for understanding non-conventional networks and new
cybersecurity measures in an ever-expanding digital landscape, particularly
those characterized by mobile devices, voice activation, and non-linear
microphones susceptible to malicious actors operating stealth attacks in the
near-ultrasound or inaudible ranges. By 2024, this new attack surface might
encompass more digital voice assistants than people on the planet yet offer
fewer remedies than conventional patching or firmware fixes since the inaudible
attacks arise inherently from the microphone design and digital signal
processing.

通过对无声攻击进行的分析，我们确认安全风险因素为 7.6（满分为 10），突显了 NIST 国家漏洞数据库（NVD）独立评分的重大安全漏洞。我们的基线网络模型展示了一个攻击者使用无声语音命令来未经授权地访问安全笔记本中的机密信息的情景。我们在该基线网络模型上模拟了许多攻击场景，揭示了通过物理访问和不添加新硬件或放大设备技能即可发现和掌握特权信息的潜力。我们使用微软的 CyberBattleSim 框架评估了六种强化学习算法，并发现 Deep-Q 学习与利用相结合的方法最佳，可以在较少的步骤中迅速掌控所有节点。我们的发现强调了在不断扩大的数字化环境中，尤其是由移动设备、语音激活和容易受到恶意行为者进行隐蔽攻击的非常规网络和新的网络安全措施的紧迫性。到 2024 年，这个新的攻击面可能包括比地球上的人更多的数字助手，但是却比常规的修补程序或固件修复提供更少的解决方案，因为无声攻击源自于麦克风设计和数字信号处理。