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The Implication of Deep Neural Networks in Solving Optimization Problems for Network Security

by Shabbir Hassan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 20
Year of Publication: 2020
Authors: Shabbir Hassan
10.5120/ijca2020920154

Shabbir Hassan . The Implication of Deep Neural Networks in Solving Optimization Problems for Network Security. International Journal of Computer Applications. 176, 20 ( May 2020), 6-13. DOI=10.5120/ijca2020920154

@article{ 10.5120/ijca2020920154,
author = { Shabbir Hassan },
title = { The Implication of Deep Neural Networks in Solving Optimization Problems for Network Security },
journal = { International Journal of Computer Applications },
issue_date = { May 2020 },
volume = { 176 },
number = { 20 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number20/31313-2020920154/ },
doi = { 10.5120/ijca2020920154 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:01.097313+05:30
%A Shabbir Hassan
%T The Implication of Deep Neural Networks in Solving Optimization Problems for Network Security
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 20
%P 6-13
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Optimization which implies minimization and maximization of some objective functions often becomes heuristics, as all the problems are not just in the form of linear or polynomial. To optimize problems we may apply heuristics method or any other type of approximation method that can be employed. On the application of derivatives and partial derivatives, these evolutionary algorithms liberalize the objective functions and their restrictions at a specific point. The objective function approximation method of (NLO) Non-linear optimization which used to resolve the optimization problems efficiently. This study paper proposes the critical use of artificial neural networks to strategically optimize these problems so that to apply other possible techniques or methods if it could not be optimized directly. We have enforced the conversion of problems into polynomials so that the solution of Optimization problems (OP) can be calculated accurately.

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  50. AUTHORS PROFILE
  51. Shabbir Hassan Sun Certified Java Programmer (SCJP) currently working as Assistant Professor at Centre for Distance Education, Aligarh Muslim University, Aligarh. He holds Master in Computer Science and Applications (MCA) and currently pursuing Ph.D. at Department of Computer Science, Aligarh Muslim University. His thrust area is “Analysis and Design of Lightweight Stream Cipher” and area of interest includes Applied Mathematics, Analysis and Design of Algorithms, Dynamic Programming, Network Se
Index Terms

Computer Science
Information Sciences

Keywords

Deep leering neural network (DNN) Neural network model Optimization problems (OP) Non-linear optimization (NLO) Particle Swarm Optimization (PSO) Method of Approximation (MAP) Unprotected AES implementation.

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