Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks
Abstract The growing number of connected devices in smart home environments has amplified security risks, particularly from Man-in-the-Middle (MitM) attacks. These attacks allow cybercriminals to intercept and manipulate communication streams between devices, often remaining undetected. Traditional...
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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/s41598-025-85547-5 |
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| author | V. Kandasamy A. Ameelia Roseline |
| author_facet | V. Kandasamy A. Ameelia Roseline |
| author_sort | V. Kandasamy |
| collection | DOAJ |
| description | Abstract The growing number of connected devices in smart home environments has amplified security risks, particularly from Man-in-the-Middle (MitM) attacks. These attacks allow cybercriminals to intercept and manipulate communication streams between devices, often remaining undetected. Traditional rule-based methods struggle to cope with the complexity of these attacks, creating a need for more advanced, adaptive intrusion detection systems. This research introduces the AEXB Model, a hybrid deep learning approach that combines the feature extraction capabilities of an AutoEncoder with the classification power of XGBoost. By combining these complementary methods, the model enhances detection accuracy and significantly reduces false positives. The AEXB Model’s methodology encompasses robust preprocessing steps, including data cleaning, scaling, and dimensionality reduction, followed by comprehensive feature engineering and selection techniques, such as Recursive Feature Elimination (RFE) and correlation analysis. By applying this approach to the Intrusion Detection in Smart Home (IDSH) dataset, the model achieves an impressive 97.24% accuracy, demonstrating its effectiveness in identifying anomalous network behavior indicative of MitM attacks. Additionally, the model’s real-time detection capabilities allow for rapid responses to threats, thus providing continuous protection in dynamic smart home environments. |
| format | Article |
| id | doaj-art-6f9885c9f816489a8e540bbbd55b74c1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6f9885c9f816489a8e540bbbd55b74c12025-01-12T12:17:31ZengNature PortfolioScientific Reports2045-23222025-01-0115112610.1038/s41598-025-85547-5Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacksV. Kandasamy0A. Ameelia Roseline1Department of Information and Communication Engineering, Anna UniversityDepartment of Electronics and Communication Engineering, Panimalar Engineering CollegeAbstract The growing number of connected devices in smart home environments has amplified security risks, particularly from Man-in-the-Middle (MitM) attacks. These attacks allow cybercriminals to intercept and manipulate communication streams between devices, often remaining undetected. Traditional rule-based methods struggle to cope with the complexity of these attacks, creating a need for more advanced, adaptive intrusion detection systems. This research introduces the AEXB Model, a hybrid deep learning approach that combines the feature extraction capabilities of an AutoEncoder with the classification power of XGBoost. By combining these complementary methods, the model enhances detection accuracy and significantly reduces false positives. The AEXB Model’s methodology encompasses robust preprocessing steps, including data cleaning, scaling, and dimensionality reduction, followed by comprehensive feature engineering and selection techniques, such as Recursive Feature Elimination (RFE) and correlation analysis. By applying this approach to the Intrusion Detection in Smart Home (IDSH) dataset, the model achieves an impressive 97.24% accuracy, demonstrating its effectiveness in identifying anomalous network behavior indicative of MitM attacks. Additionally, the model’s real-time detection capabilities allow for rapid responses to threats, thus providing continuous protection in dynamic smart home environments.https://doi.org/10.1038/s41598-025-85547-5Man-in-the-middle (MitM) attacksCybersecurityDeep learning (DL)AutoencoderXGBoostIntrusion detection (ID) |
| spellingShingle | V. Kandasamy A. Ameelia Roseline Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks Scientific Reports Man-in-the-middle (MitM) attacks Cybersecurity Deep learning (DL) Autoencoder XGBoost Intrusion detection (ID) |
| title | Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks |
| title_full | Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks |
| title_fullStr | Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks |
| title_full_unstemmed | Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks |
| title_short | Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks |
| title_sort | harnessing advanced hybrid deep learning model for real time detection and prevention of man in the middle cyber attacks |
| topic | Man-in-the-middle (MitM) attacks Cybersecurity Deep learning (DL) Autoencoder XGBoost Intrusion detection (ID) |
| url | https://doi.org/10.1038/s41598-025-85547-5 |
| work_keys_str_mv | AT vkandasamy harnessingadvancedhybriddeeplearningmodelforrealtimedetectionandpreventionofmaninthemiddlecyberattacks AT aameeliaroseline harnessingadvancedhybriddeeplearningmodelforrealtimedetectionandpreventionofmaninthemiddlecyberattacks |