* update 2024-05-07 06:17:57

Author: actions-user

arXiv_db/Malware/2024.md

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 </details>
 
+<details>
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+<summary>2024-05-02 19:03:11 - Generative AI in Cybersecurity</summary>
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+- *Shivani Metta, Isaac Chang, Jack Parker, Michael P. Roman, Arturo F. Ehuan*
+
+- `2405.01674v1` - [abs](http://arxiv.org/abs/2405.01674v1) - [pdf](http://arxiv.org/pdf/2405.01674v1)
+
+> The dawn of Generative Artificial Intelligence (GAI), characterized by advanced models such as Generative Pre-trained Transformers (GPT) and other Large Language Models (LLMs), has been pivotal in reshaping the field of data analysis, pattern recognition, and decision-making processes. This surge in GAI technology has ushered in not only innovative opportunities for data processing and automation but has also introduced significant cybersecurity challenges.   As GAI rapidly progresses, it outstrips the current pace of cybersecurity protocols and regulatory frameworks, leading to a paradox wherein the same innovations meant to safeguard digital infrastructures also enhance the arsenal available to cyber criminals. These adversaries, adept at swiftly integrating and exploiting emerging technologies, may utilize GAI to develop malware that is both more covert and adaptable, thus complicating traditional cybersecurity efforts.   The acceleration of GAI presents an ambiguous frontier for cybersecurity experts, offering potent tools for threat detection and response, while concurrently providing cyber attackers with the means to engineer more intricate and potent malware. Through the joint efforts of Duke Pratt School of Engineering, Coalfire, and Safebreach, this research undertakes a meticulous analysis of how malicious agents are exploiting GAI to augment their attack strategies, emphasizing a critical issue for the integrity of future cybersecurity initiatives. The study highlights the critical need for organizations to proactively identify and develop more complex defensive strategies to counter the sophisticated employment of GAI in malware creation.
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+</details>
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+<details>
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+<summary>2024-05-02 20:48:42 - Explainability Guided Adversarial Evasion Attacks on Malware Detectors</summary>
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+- *Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Moustafa Saleh*
+
+- `2405.01728v1` - [abs](http://arxiv.org/abs/2405.01728v1) - [pdf](http://arxiv.org/pdf/2405.01728v1)
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+> As the focus on security of Artificial Intelligence (AI) is becoming paramount, research on crafting and inserting optimal adversarial perturbations has become increasingly critical. In the malware domain, this adversarial sample generation relies heavily on the accuracy and placement of crafted perturbation with the goal of evading a trained classifier. This work focuses on applying explainability techniques to enhance the adversarial evasion attack on a machine-learning-based Windows PE malware detector. The explainable tool identifies the regions of PE malware files that have the most significant impact on the decision-making process of a given malware detector, and therefore, the same regions can be leveraged to inject the adversarial perturbation for maximum efficiency. Profiling all the PE malware file regions based on their impact on the malware detector's decision enables the derivation of an efficient strategy for identifying the optimal location for perturbation injection. The strategy should incorporate the region's significance in influencing the malware detector's decision and the sensitivity of the PE malware file's integrity towards modifying that region. To assess the utility of explainable AI in crafting an adversarial sample of Windows PE malware, we utilize the DeepExplainer module of SHAP for determining the contribution of each region of PE malware to its detection by a CNN-based malware detector, MalConv. Furthermore, we analyzed the significance of SHAP values at a more granular level by subdividing each section of Windows PE into small subsections. We then performed an adversarial evasion attack on the subsections based on the corresponding SHAP values of the byte sequences.
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+</details>
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