MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks
With the continuous development of network technology and the increasing complexity of application scenarios, network attacks have become more diverse and covert, posing significant challenges to system security. Traditional network security measures often struggle to detect and respond to rapidly e...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825005885 |
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| _version_ | 1849318281125560320 |
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| author | Fucai Luo Tingfa Xu Jianan Li Fengxiang Xu |
| author_facet | Fucai Luo Tingfa Xu Jianan Li Fengxiang Xu |
| author_sort | Fucai Luo |
| collection | DOAJ |
| description | With the continuous development of network technology and the increasing complexity of application scenarios, network attacks have become more diverse and covert, posing significant challenges to system security. Traditional network security measures often struggle to detect and respond to rapidly evolving attack patterns in real time. Therefore, there is an urgent need for a new detection technology that can dynamically assess risks and adapt to changing environments. The Markov Decision Process (MDP) offers an effective and interpretable approach to sequential decision-making, providing a novel method for automatic network attack detection. This study proposes an automatic detection model based on MDP, which dynamically analyzes network traffic and system behavior while continuously improving detection accuracy through adaptive learning strategies. To evaluate the model's effectiveness, multiple experiments were conducted in various scenarios, achieving a maximum detection accuracy of 94.3 %. The results demonstrate that the proposed MDP-based detection model offers significant advantages in detection accuracy, response speed, and adaptability to unknown attacks. |
| format | Article |
| id | doaj-art-a2ecd6338f434cfb9b6cff67c925d42c |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-a2ecd6338f434cfb9b6cff67c925d42c2025-08-20T03:50:53ZengElsevierAlexandria Engineering Journal1110-01682025-07-0112648049010.1016/j.aej.2025.04.091MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacksFucai Luo0Tingfa Xu1Jianan Li2Fengxiang Xu3Corresponding author.; School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optoelectronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optoelectronics, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Optoelectronics, Beijing Institute of Technology, Beijing 100081, ChinaWith the continuous development of network technology and the increasing complexity of application scenarios, network attacks have become more diverse and covert, posing significant challenges to system security. Traditional network security measures often struggle to detect and respond to rapidly evolving attack patterns in real time. Therefore, there is an urgent need for a new detection technology that can dynamically assess risks and adapt to changing environments. The Markov Decision Process (MDP) offers an effective and interpretable approach to sequential decision-making, providing a novel method for automatic network attack detection. This study proposes an automatic detection model based on MDP, which dynamically analyzes network traffic and system behavior while continuously improving detection accuracy through adaptive learning strategies. To evaluate the model's effectiveness, multiple experiments were conducted in various scenarios, achieving a maximum detection accuracy of 94.3 %. The results demonstrate that the proposed MDP-based detection model offers significant advantages in detection accuracy, response speed, and adaptability to unknown attacks.http://www.sciencedirect.com/science/article/pii/S1110016825005885Automatic DetectionMarkov Decision ProcessNetwork AttacksReinforcement LearningResource Utilization |
| spellingShingle | Fucai Luo Tingfa Xu Jianan Li Fengxiang Xu MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks Alexandria Engineering Journal Automatic Detection Markov Decision Process Network Attacks Reinforcement Learning Resource Utilization |
| title | MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks |
| title_full | MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks |
| title_fullStr | MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks |
| title_full_unstemmed | MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks |
| title_short | MDP-AD: A Markov decision process-based adaptive framework for real-time detection of evolving and unknown network attacks |
| title_sort | mdp ad a markov decision process based adaptive framework for real time detection of evolving and unknown network attacks |
| topic | Automatic Detection Markov Decision Process Network Attacks Reinforcement Learning Resource Utilization |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825005885 |
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