Abstract

Wireless communication is vulnerable to various security threats, including eavesdropping and data interception. To bolster the security of wireless networks, this research explores the application of novel transfer learning techniques for encryption. Transfer Attention learning leverages knowledge from related domains to enhance the performance of encryption methods in wireless communication. This research investigates the application of Transfer Attention Learning, a cutting-edge machine learning technique, to bolster the security of wireless networks. Transfer Attention Learning harnesses knowledge from related domains to enhance the performance of encryption methods in wireless communication. This comprehensive framework encompasses data collection, feature extraction, model selection, training, and deployment. A substantial dataset of wireless network traffic, encompassing benign and malicious packets, is collected. Feature extraction techniques are employed to discern pertinent patterns in the data. Transfer Attention Learning models are meticulously chosen and fine-tuned to align them with the specific requirements of wireless network encryption. The model efficacy is rigorously evaluated using diverse metrics to ensure optimal security enhancement. Subsequently, the model is seamlessly integrated into the wireless network infrastructure, thereby fortifying protection against potential security threats. Ongoing monitoring, updates, regulatory compliance, and user education constitute essential facets of this security framework. This research signifies a significant advancement in wireless network security by harnessing the potential of Transfer Attention Learning to fortify encryption measures, ensuring the safeguarding of sensitive data within wireless communication channels.