Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework
The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10414101/ |
| _version_ | 1827340043838029824 |
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| author | Faiza Babar Khan Muhammad Hanif Durad Asifullah Khan Farrukh Aslam Khan Muhammad Rizwan Aftab Ali |
| author_facet | Faiza Babar Khan Muhammad Hanif Durad Asifullah Khan Farrukh Aslam Khan Muhammad Rizwan Aftab Ali |
| author_sort | Faiza Babar Khan |
| collection | DOAJ |
| description | The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators. Our results showcase enhanced malware detection performance, yielding a 10% performance boost, thus affirming the efficacy of our approach compared to existing models. |
| first_indexed | 2024-03-07T20:11:19Z |
| format | Article |
| id | doaj.art-e167ba0691db425d9d7d75cfc42b3021 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T20:11:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e167ba0691db425d9d7d75cfc42b30212024-02-28T00:01:12ZengIEEEIEEE Access2169-35362024-01-0112276832770810.1109/ACCESS.2024.335845410414101Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network FrameworkFaiza Babar Khan0https://orcid.org/0000-0002-6751-8360Muhammad Hanif Durad1https://orcid.org/0000-0002-8026-1045Asifullah Khan2https://orcid.org/0000-0003-2039-5305Farrukh Aslam Khan3https://orcid.org/0000-0002-7023-7172Muhammad Rizwan4https://orcid.org/0000-0002-0855-3465Aftab Ali5https://orcid.org/0000-0002-4578-7631Department of Computer and Information Sciences (DCIS), CIPMA Laboratory, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanDepartment of Computer and Information Sciences (DCIS), CIPMA Laboratory, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanDepartment of Computer and Information Sciences (DCIS), Pattern Recognition Laboratory, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, PakistanPIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, PakistanDepartment of Computer and Information Sciences (DCIS), CIPMA Laboratory, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, PakistanSchool of Computing, Ulster University, Belfast, U.KThe cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators. Our results showcase enhanced malware detection performance, yielding a 10% performance boost, thus affirming the efficacy of our approach compared to existing models.https://ieeexplore.ieee.org/document/10414101/Anti-malware systemgenerative adversarial networksmalware sandboxesmalwareunpackerperformance |
| spellingShingle | Faiza Babar Khan Muhammad Hanif Durad Asifullah Khan Farrukh Aslam Khan Muhammad Rizwan Aftab Ali Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework IEEE Access Anti-malware system generative adversarial networks malware sandboxes malware unpacker performance |
| title | Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework |
| title_full | Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework |
| title_fullStr | Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework |
| title_full_unstemmed | Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework |
| title_short | Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework |
| title_sort | design and performance analysis of an anti malware system based on generative adversarial network framework |
| topic | Anti-malware system generative adversarial networks malware sandboxes malware unpacker performance |
| url | https://ieeexplore.ieee.org/document/10414101/ |
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