* update 2024-03-21 06:15:33

arXiv_db/Malware/2024.md

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 </details>
 
+<details>
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+<summary>2024-03-18 19:10:12 - Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models</summary>
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+- *Nusrat Zahan, Philipp Burckhardt, Mikola Lysenko, Feross Aboukhadijeh, Laurie Williams*
+
+- `2403.12196v1` - [abs](http://arxiv.org/abs/2403.12196v1) - [pdf](http://arxiv.org/pdf/2403.12196v1)
+
+> The Gartner 2022 report predicts that 45% of organizations worldwide will encounter software supply chain attacks by 2025, highlighting the urgency to improve software supply chain security for community and national interests. Current malware detection techniques aid in the manual review process by filtering benign and malware packages, yet such techniques have high false-positive rates and limited automation support. Therefore, malware detection techniques could benefit from advanced, more automated approaches for accurate and minimally false-positive results. The goal of this study is to assist security analysts in identifying malicious packages through the empirical study of large language models (LLMs) to detect potential malware in the npm ecosystem.   We present SocketAI Scanner, a multi-stage decision-maker malware detection workflow using iterative self-refinement and zero-shot-role-play-Chain of Thought (CoT) prompting techniques for ChatGPT. We studied 5,115 npm packages (of which 2,180 are malicious) and performed a baseline comparison of the GPT-3 and GPT-4 models with a static analysis tool. Our findings showed promising results for GPT models with low misclassification alert rates. Our baseline comparison demonstrates a notable improvement over static analysis in precision scores above 25% and F1 scores above 15%. We attained precision and F1 scores of 91% and 94%, respectively, for the GPT-3 model. Overall, GPT-4 demonstrates superior performance in precision (99%) and F1 (97%) scores, while GPT-3 presents a cost-effective balance between performance and expenditure.
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+</details>
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+<details>
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+<summary>2024-03-19 15:21:20 - Review of Generative AI Methods in Cybersecurity</summary>
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+- *Yagmur Yigit, William J Buchanan, Madjid G Tehrani, Leandros Maglaras*
+
+- `2403.08701v2` - [abs](http://arxiv.org/abs/2403.08701v2) - [pdf](http://arxiv.org/pdf/2403.08701v2)
+
+> Over the last decade, Artificial Intelligence (AI) has become increasingly popular, especially with the use of chatbots such as ChatGPT, Gemini, and DALL-E. With this rise, large language models (LLMs) and Generative AI (GenAI) have also become more prevalent in everyday use. These advancements strengthen cybersecurity's defensive posture and open up new attack avenues for adversaries as well. This paper provides a comprehensive overview of the current state-of-the-art deployments of GenAI, covering assaults, jailbreaking, and applications of prompt injection and reverse psychology. This paper also provides the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware. GenAI can significantly improve the automation of defensive cyber security processes through strategies such as dataset construction, safe code development, threat intelligence, defensive measures, reporting, and cyberattack detection. In this study, we suggest that future research should focus on developing robust ethical norms and innovative defense mechanisms to address the current issues that GenAI creates and to also further encourage an impartial approach to its future application in cybersecurity. Moreover, we underscore the importance of interdisciplinary approaches further to bridge the gap between scientific developments and ethical considerations.
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+</details>
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