Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917886043357184 |
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| author | Basnet, Animesh Singh Ghanem, Mohamed Chahine Dunsin, Dipo Sowinski-Mydlarz, Wiktor |
| author_facet | Basnet, Animesh Singh Ghanem, Mohamed Chahine Dunsin, Dipo Sowinski-Mydlarz, Wiktor |
| contents | The development of the DRL model for malware attribution involved extensive research, iterative coding, and numerous adjustments based on the insights gathered from predecessor models and contemporary research papers. This preparatory work was essential to establish a robust foundation for the model, ensuring it could adapt and respond effectively to the dynamic nature of malware threats. Initially, the model struggled with low accuracy levels, but through persistent adjustments to its architecture and learning algorithms, accuracy improved dramatically from about 7 percent to over 73 percent in early iterations. By the end of the training, the model consistently reached accuracy levels near 98 percent, demonstrating its strong capability to accurately recognise and attribute malware activities. This upward trajectory in training accuracy is graphically represented in the Figure, which vividly illustrates the model maturation and increasing proficiency over time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning Basnet, Animesh Singh Ghanem, Mohamed Chahine Dunsin, Dipo Sowinski-Mydlarz, Wiktor Cryptography and Security Artificial Intelligence Machine Learning The development of the DRL model for malware attribution involved extensive research, iterative coding, and numerous adjustments based on the insights gathered from predecessor models and contemporary research papers. This preparatory work was essential to establish a robust foundation for the model, ensuring it could adapt and respond effectively to the dynamic nature of malware threats. Initially, the model struggled with low accuracy levels, but through persistent adjustments to its architecture and learning algorithms, accuracy improved dramatically from about 7 percent to over 73 percent in early iterations. By the end of the training, the model consistently reached accuracy levels near 98 percent, demonstrating its strong capability to accurately recognise and attribute malware activities. This upward trajectory in training accuracy is graphically represented in the Figure, which vividly illustrates the model maturation and increasing proficiency over time. |
| title | Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.11463 |