Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection
Abstract Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84421-0 |
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| author | Pramodh Krishna D. E. Sandhya Khaja Shareef Sk Srihari Varma Mantena Venkata Subbaiah Desanamukula Ch Koteswararao Srinivasa Rao Vemula Maruthi Vemula |
| author_facet | Pramodh Krishna D. E. Sandhya Khaja Shareef Sk Srihari Varma Mantena Venkata Subbaiah Desanamukula Ch Koteswararao Srinivasa Rao Vemula Maruthi Vemula |
| author_sort | Pramodh Krishna D. |
| collection | DOAJ |
| description | Abstract Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs. To optimize the model’s efficiency, a unique DECEHGS algorithm combining Differential Evolution and Evolutionary Population Dynamics techniques is employed, enhancing both convergence and performance. The proposed model demonstrates significant improvements over existing methods, achieving an accuracy of 95%, a 12% increase in packet delivery ratio, and a 20% reduction in routing overhead compared to traditional techniques. These advancements underline the model’s superiority in detecting malicious nodes, conserving energy, and ensuring reliable network performance. The comprehensive evaluation using MATLAB R2023a validates the proposed approach as an effective and energy-efficient solution for enhancing MANET security. |
| format | Article |
| id | doaj-art-623292bf1e964a5c82e63cf37f503e24 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-623292bf1e964a5c82e63cf37f503e242025-01-05T12:14:23ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84421-0Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detectionPramodh Krishna D.0E. Sandhya1Khaja Shareef Sk2Srihari Varma Mantena3Venkata Subbaiah Desanamukula4Ch Koteswararao5Srinivasa Rao Vemula6Maruthi Vemula7Department of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Computer Science & Engineering-AI&ML, Madanapalle Institute of Technology & ScienceDepartment of Computer Science & Information Technology, Koneru Lakshmaiah Education FoundationDepartment of Computer Science and Engineering, SRKR Engineering CollegeDepartment of Computer Science and Engineering, Lakireddy Bali Reddy College of EngineeringSchool of Computer Science and Engineering, VIT-AP UniversityFIS Management ServicesNorth Carolina School of Science and MathematicsAbstract Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs. To optimize the model’s efficiency, a unique DECEHGS algorithm combining Differential Evolution and Evolutionary Population Dynamics techniques is employed, enhancing both convergence and performance. The proposed model demonstrates significant improvements over existing methods, achieving an accuracy of 95%, a 12% increase in packet delivery ratio, and a 20% reduction in routing overhead compared to traditional techniques. These advancements underline the model’s superiority in detecting malicious nodes, conserving energy, and ensuring reliable network performance. The comprehensive evaluation using MATLAB R2023a validates the proposed approach as an effective and energy-efficient solution for enhancing MANET security.https://doi.org/10.1038/s41598-024-84421-0Mobile Ad Hoc NetworksConvolutional neural networkHunger game searchLong short-term memoryEvolutionary population dynamics technique |
| spellingShingle | Pramodh Krishna D. E. Sandhya Khaja Shareef Sk Srihari Varma Mantena Venkata Subbaiah Desanamukula Ch Koteswararao Srinivasa Rao Vemula Maruthi Vemula Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection Scientific Reports Mobile Ad Hoc Networks Convolutional neural network Hunger game search Long short-term memory Evolutionary population dynamics technique |
| title | Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection |
| title_full | Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection |
| title_fullStr | Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection |
| title_full_unstemmed | Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection |
| title_short | Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection |
| title_sort | enhancing security and efficiency in mobile ad hoc networks using a hybrid deep learning model for flooding attack detection |
| topic | Mobile Ad Hoc Networks Convolutional neural network Hunger game search Long short-term memory Evolutionary population dynamics technique |
| url | https://doi.org/10.1038/s41598-024-84421-0 |
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