| Description |
1 online resource (433 p.) |
| Series |
Lecture Notes in Networks and Systems ; v. 697 |
|
Lecture notes in networks and systems ; v. 697.
|
| Contents |
Intro -- Committee -- Message from General Chairs -- Message from Program Chairs -- Preface -- Contents -- About the Editors -- Verifiable Delay Function Based on Non-linear Hybrid Cellular Automata -- 1 Introduction -- 1.1 Our Contributions -- 1.2 Organization of the Paper -- 2 Related Work -- 2.1 Review of Verifiable Delay Functions -- 2.2 Cryptography Based on Non-linear CA -- 3 Preliminaries -- 3.1 Notation -- 3.2 Verifiable Delay Function -- 3.3 Cellular Automata -- 4 VDF Based on Cellular Automata -- 4.1 The Setup(1,T) Algorithm -- 4.2 The Eval(pp,x) Algorithm |
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5 Conclusion -- References -- MILP Modeling of S-box: Divide and Merge Approach -- 1 Introduction -- 2 MILP Modeling of S-boxes -- 2.1 Generating Linear Inequalities of DDT -- 2.2 Inequality Minimization Using Impossible Transitions -- 2.3 Existing Improvements in Inequality Minimization -- 2.4 Inequality Minimization: Divide and Merge Approach -- 3 Experimental Results: 4-Bit and 5-Bit S-boxes -- 3.1 Divide and Merge Approach: k=2 -- 3.2 Divide and Merge Approach: k=3 -- 4 Conclusion -- References -- A Relation Between Properties of S-box and Linear Inequalities of DDT -- 1 Introduction |
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2 Non-linear S-boxes -- 2.1 Properties of S-box -- 3 Methods for Construction and Minimization of Linear Inequalities of DDT -- 3.1 H-representation of Convex Hull -- 3.2 Boolean Logic Minimization Tools-Logic Friday, MILES -- 3.3 Limitation of Convex Hull Approach and Logic Friday Tool -- 4 Results -- 4.1 Experiments -- 4.2 Relation Between Boomerang Uniformity and Number of Linear Inequalities -- 5 Conclusion -- References -- Damage Level Estimation of Rubble-Mound Breakwaters Using Deep Artificial Neural Network -- 1 Introduction -- 2 Deep Artificial Neural Network -- 3 Dataset |
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4 Development of the Deep ANN-Based Damage Level Estimation Model -- 5 Results and Discussion -- 5.1 Performance Analysis and Model Accuracy -- 5.2 Comparison of the Proposed Deep ANN Model with the Existing ANN-Based Damage Level Estimation Model -- 6 Conclusion -- References -- Facial Image Manipulation Detection Using Cellular Automata and Transfer Learning -- 1 Introduction -- 2 Digital Image Manipulation Detection -- 3 Results -- 3.1 Experimental Data -- 3.2 Application Previews -- 3.3 Performance Metrics of the Proposed Solution -- 3.4 Comparison with Other Works -- 4 Conclusion |
| Summary |
This book features selected papers from the 9th International Conference on Mathematics and Computing (ICMC 2023), organized at BITS Pilani K. K. Birla Goa Campus, India, during 68 January 2023. It covers recent advances in the field of mathematics, statistics, and scientific computing. The book presents innovative work by leading academics, researchers, and experts from industry in mathematics, statistics, cryptography, network security, cybersecurity, machine learning, data analytics, and blockchain technology in computer science and information technology |
| Bibliography |
Includes bibliographical references |
| Notes |
Includes author index |
|
Online resource; title from PDF title page (SpringerLink, viewed August 14, 2023) |
| Subject |
Computer science -- Mathematics -- Congresses
|
| Form |
Electronic book
|
| Author |
Giri, Debasis.
|
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Gollmann, Dieter.
|
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Ponnusamy, S
|
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Kouichi, Sakurai
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Stanimirović, Predrag.
|
|
Sahoo, J. K
|
| ISBN |
9789819930807 |
|
9819930804 |
|
