FPGA Realization of a Reversible Data Hiding Scheme for 5G MIMO-OFDM System by Chaotic Key Generation-Based Paillier Cryptography Along with LDPC and Its Side Channel Estimation Using Machine Learning Technique

Abstract

Multiple-Input and Multiple-Output (MIMO) technology is a significant and timely subject, which is highly motivated by the needs of 5G wireless communications. Data transmission performs MIMO, which is highly sensitive. There are several security issues while transmitting the data such as loss of data and code injection. Two efficient methods are Encryption and Data Hiding protection of data in wireless communication. This dissertation suggests FPGA Implementation of RDHS by Chaotic Key Generation-Based Paillier Cryptography with LDPC using machine learning technique. RDHS stands for Reversible Data Hiding Scheme. In a reversible method, the initial stage of preprocessing is to shrink the histogram of image before the process of encryption. Hence, the plaintext domain changing the encrypted images to data embedding cannot result from any pixel repletion. A little distortion data embedding may be taken as the original image may recover the directly decrypted image. Here, the performance metrics of throughput, area consumed, latency, delay, packet delivery, network life and overhead are calculated. The proposed Paillier homomorphic cryptosystem proposes higher network throughput as 99%, higher network life 98%, lower delay rate as 60%, packet delivery as 74%, overhead as 66%, latency as 55% and area consumed as 61% with the existing method such as McEliece, Elgamal and Elliptic curve cryptosystem in the security analysis of the proposed method providing decryption time 94% and encryption time 98% better than the existing method.

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

“…The irrelevant features have low correlation and the classes are filtered depending on univariate filter. The general equation of UEFS is expressed in Equation (9),…”

“…Consecutively, the cloud utility is debugged on the internet throughout the privacy is the important problem of cloud services 6,7 . Subsequently, the security of data, prevention, and intrusion detection is the main security to be impacted in the cloud infrastructure 8‐12 . Nevertheless, cloud network utilizing the incorporated technologies and virtualization models activate with the help of standard internet protocol 13 .…”

“…6,7 Subsequently, the security of data, prevention, and intrusion detection is the main security to be impacted in the cloud infrastructure. [8][9][10][11][12] Nevertheless, cloud network utilizing the incorporated technologies and virtualization models activate with the help of standard internet protocol. 13 The CC stimulates conventional attacks, such as denial of service (DoS), distributed denial of service (DDoS), and Internet protocol (IP) spoofing.…”

Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique

“…17 Various scholars have executed research in the capacity configuration of EVCSs. 18 Using existing research, key influences affect the optimization method in the load capacity configuration and optimizing objective system.. [19][20][21][22][23] Several works were assessed the public electric vehicle charging station function in the power system; these are divided as two states: (i) internal management of electric vehicle charging station and (ii) external function and contact with the power grid. Most of the work falls under the 1ststate.…”

Modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier using flamingo search optimization algorithm for disease diagnosis model

“…Protecting information from clinical image is the important factor to consider when analyzing the clinical imageries. Generic illustration detection tasks have strained the concentration of several researchers, in which the deep learning approach is producing an excellent concert 14–18 . But those methods did not provide the adequate accuracy and error rate is maximized, such drawbacks have motivated do this research work.…”