Welcome to Journal of Network Communications and Emerging Technologies (JNCET)

Volume 13, Issue 1, January (2025)                              

S.No	Title & Authors	Full Text
1	Ruzicka Indexed Schmidt-Samoa Certificateless Signcryptive Connectionist Artificial Deep Neural Learning for Secure Transmission Using Satellite Images S. Padmalal, I. Edwin Dayanand, F.R.Shiny Malar Abstract - Content protection for digital images has become a critical concern due to their widespread use on the Internet. As digital images become increasingly integral to daily communication, ensuring their confidentiality and security during transmission is paramount. To address these challenges, this paper introduces a novel technique called Ruzicka Indexed Schmidt-Samoa Certificateless Signcryptive Connectionist Artificial Deep Neural Learning (RISCSCADL) for enhanced secure image transmission. The RISCSCADL method employs a multi-layer architecture comprising input, hidden, and output layers for processing satellite images. The method integrates Schmidt-Samoa certificateless signcryption within the initial hidden layer to enhance security through three distinct processes: Ephemeral Agreement session key generation, Schmidt-Samoa certificateless signcryption, and Ruzicka indexive Schmidt-Samoa certificateless unsigncryption. The framework generates session-specific private and public keys in the first hidden layer, performs signature generation and encryption in the second layer, and conducts signature verification through Ruzicka Indexed Schmidt-Samoa certificateless unsigncryption in the third layer. Experimental results demonstrate that the RISCSCADL method achieves superior image transmission security with enhanced confidentiality, integrity, and reduced computational complexity compared to existing approaches.	Download