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Securing Wireless Communication Using Novel Transfer Learning for Encryption in Wireless Networks


Affiliations
1 Department of Computer Science, Government Arts and Science College, India
2 Department of Physics, Government Science College, Hassan, Karnataka, India
3 Department of Electronics and Communication Engineering, KLM College of Engineering for Women, India
4 Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Saudi Arabia
     

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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.

Keywords

Transfer Learning, Wireless Networks, Encryption, Security, Data Protection.
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Abstract Views: 124

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  • Securing Wireless Communication Using Novel Transfer Learning for Encryption in Wireless Networks

Abstract Views: 124  |  PDF Views: 1

Authors

Jayaganesh Jagannathan
Department of Computer Science, Government Arts and Science College, India
S. G. Prasanna Kumara
Department of Physics, Government Science College, Hassan, Karnataka, India
T. Lakshmi Narayana
Department of Electronics and Communication Engineering, KLM College of Engineering for Women, India
Mohammad Shabbir Alam
Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Saudi Arabia

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.

Keywords


Transfer Learning, Wireless Networks, Encryption, Security, Data Protection.

References