Neural Network-Based Encryption of Data Communication


Foo, Jia Lin and Ng, Kok Why and Naveen, Palanichamy (2022) Neural Network-Based Encryption of Data Communication. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Neural Network uses a technique that mimics human brain to try to find hidden connections in a collection of data. A set of neurons are created artificially or naturally, constitutes a neural network in this context. They are extensively used in a variety of financial technology industried, which include fraud evaluation and detection as well as forecasting and marketing research. Layers of connected nodes make up a neural network. Each node is a perceptron, which resembles multiple linear regression and is referred to as such. The multiple linear regression signal is fed into a potentially nonlinear activation function via the perceptron. The usage of neural networks is widespread, including applications in trading, business analytics, financial operations, corporate planning, and product maintenance. In corporate applications including forecasting and marketing research solutions, fraud detection, and risk assessment, neural networks have also become increasingly popular. • Neural networks and cryptography techniques have coexisted in peace and war, creating a duality that calls for a thorough review study. In cryptanalysis and assaults against encryption algorithms and encrypted data, neural networks can be used to undermine cryptosystems. However, there is a mutually beneficial relationship between cryptographic algorithms and neural networks. The performance and security of cryptosystems can be enhanced by neural networks, while the secrecy of neural networks can be supported by encryption methods. As of now, there are currently no surveys that examine all of the various ways that neural networks are engaging with cryptography. By giving an overview of the current status of the interactions between artificial neural networks and cryptography systems, this paper seeks to address that gap. In order accomplish this, this article will emphasise the topics where advancement is currently being made as well as the topics where more study must be carried out.

Item Type: Conference or Workshop Item (Poster)
Uncontrolled Keywords: Neural Network
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 19 Dec 2022 07:25
Last Modified: 19 Dec 2022 07:25


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