Biblio
Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.
Modern Internet TCP uses Secure Sockets Layers (SSL)/Transport Layer Security (TLS) for secure communication, which relies on Public Key Infrastructure (PKIs) to authenticate public keys. Conventional PKI is done by Certification Authorities (CAs), issuing and storing Digital Certificates, which are public keys of users with the users identity. This leads to centralization of authority with the CAs and the storage of CAs being vulnerable and imposes a security concern. There have been instances in the past where CAs have issued rogue certificates or the CAs have been hacked to issue malicious certificates. Motivated from these facts, in this paper, we propose a method (named as Trustful), which aims to build a decentralized PKI using blockchain. Blockchains provide immutable storage in a decentralized manner and allows us to write smart contracts. Ethereum blockchain can be used to build a web of trust model where users can publish attributes, validate attributes about other users by signing them and creating a trust store of users that they trust. Trustful works on the Web-of-Trust (WoT) model and allows for any entity on the network to verify attributes about any other entity through a trusted network. This provides an alternative to the conventional CA-based identity verification model. The proposed model has been implemented and tested for efficacy and known major security attacks.