A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
Cybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availabi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8730 |
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| author | Nikolaos Peppes Emmanouil Daskalakis Theodoros Alexakis Evgenia Adamopoulou |
| author_facet | Nikolaos Peppes Emmanouil Daskalakis Theodoros Alexakis Evgenia Adamopoulou |
| author_sort | Nikolaos Peppes |
| collection | DOAJ |
| description | Cybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availability is limited or heterogeneous. The current study tries to address these challenges by proposing a unified, multimodal threat detection framework that leverages the combination of synthetic data generation through Generative Adversarial Networks (GANs), advanced ensemble learning, and transfer learning techniques. The research objective is to enhance detection accuracy and resilience against zero-day, botnet, and image-based malware attacks by integrating multiple data modalities, including structured network logs and malware binaries, within a scalable and flexible pipeline. The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. Experimental results demonstrate significant improvements in detection performance and false positive reduction, especially when multimodal outputs are fused using the proposed confidence-weighted strategy. The findings highlight the framework’s adaptability and practical applicability in real-world intrusion detection and response systems. |
| format | Article |
| id | doaj-art-2b10d9d35c3140b6962bf91864820deb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2b10d9d35c3140b6962bf91864820deb2025-08-20T03:36:02ZengMDPI AGApplied Sciences2076-34172025-08-011515873010.3390/app15158730A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data SynthesisNikolaos Peppes0Emmanouil Daskalakis1Theodoros Alexakis2Evgenia Adamopoulou3Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, GreeceInstitute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, GreeceInstitute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, GreeceInstitute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, GreeceCybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availability is limited or heterogeneous. The current study tries to address these challenges by proposing a unified, multimodal threat detection framework that leverages the combination of synthetic data generation through Generative Adversarial Networks (GANs), advanced ensemble learning, and transfer learning techniques. The research objective is to enhance detection accuracy and resilience against zero-day, botnet, and image-based malware attacks by integrating multiple data modalities, including structured network logs and malware binaries, within a scalable and flexible pipeline. The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. Experimental results demonstrate significant improvements in detection performance and false positive reduction, especially when multimodal outputs are fused using the proposed confidence-weighted strategy. The findings highlight the framework’s adaptability and practical applicability in real-world intrusion detection and response systems.https://www.mdpi.com/2076-3417/15/15/8730threat detectioncybersecuritysynthetic data generationgenerative adversarial networks (GANs)multimodal fusiondeep learning |
| spellingShingle | Nikolaos Peppes Emmanouil Daskalakis Theodoros Alexakis Evgenia Adamopoulou A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis Applied Sciences threat detection cybersecurity synthetic data generation generative adversarial networks (GANs) multimodal fusion deep learning |
| title | A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis |
| title_full | A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis |
| title_fullStr | A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis |
| title_full_unstemmed | A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis |
| title_short | A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis |
| title_sort | multimodal framework for advanced cybersecurity threat detection using gan driven data synthesis |
| topic | threat detection cybersecurity synthetic data generation generative adversarial networks (GANs) multimodal fusion deep learning |
| url | https://www.mdpi.com/2076-3417/15/15/8730 |
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