Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

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 cope with the complexity these attacks, creating a need for more advanced, adaptive intrusion detection systems. This research introduces AEXB Model, hybrid deep learning approach that combines feature extraction capabilities an AutoEncoder classification power XGBoost. By combining complementary methods, model enhances accuracy significantly reduces false positives. Model's methodology encompasses robust preprocessing steps, including data cleaning, scaling, dimensionality reduction, followed by comprehensive engineering selection techniques, such as Recursive Feature Elimination (RFE) correlation analysis. applying this Intrusion Detection Smart Home (IDSH) dataset, achieves impressive 97.24% accuracy, demonstrating its effectiveness identifying anomalous network behavior indicative MitM Additionally, model's real-time rapid responses threats, thus providing continuous protection dynamic environments.

Language: Английский

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A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing

Computer Science Review, Journal Year: 2025, Volume and Issue: 57, P. 100740 - 100740

Published: March 3, 2025

Language: Английский

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Securing SDON with hybrid evolutionary intrusion detection system: An ensemble algorithm for feature selection and classification

Optical Fiber Technology, Journal Year: 2025, Volume and Issue: 93, P. 104206 - 104206

Published: March 20, 2025

Language: Английский

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