Intro
Preface
Security and Management
SAM 2020
Program Committee
Wireless Networks
ICWN 2020
Program Committee
Internet Computing & IoT
ICOMP 2020
Program Committee
Embedded Systems, Cyber-physical Systems, & Applications
ESCS 2020
Program Committee
Contents
Part I Authentication, Biometrics, and Cryptographic Technologies
Statistical Analysis of Prime Number Generators Putting Encryption at Risk
1 Introduction
2 Related Work and Basics
2.1 Deterministic RNG
2.2 Non-deterministic RNG
2.3 Prime Number Generator

2.4 Evaluation of PRNG
3 Statistical Analysis
3.1 Largest and Smallest Prime Numbers
3.2 Mean Value of Prime Numbers
3.3 Standard Deviation of Prime Numbers
3.4 Largest and Smallest Prime Distances
3.5 Maximum Distance
3.6 Mean Value of Prime Distances
3.7 Standard Deviation of Prime Distances
4 Occurrence of Primes Near the Threshold Values
5 Occurrence of Distances Near the Threshold Values
6 Patterns Within Primes
7 Searching for Last Digits
8 Benefits from the Statistics
9 Conclusions
References

Secure Authentication Protocol for Drones in LTE Networks
1 Introduction
2 Related Works
3 LTE Drone Control System
3.1 General System Architecture
3.2 LTE Authentication Protocol
3.3 Security Analysis
4 Proposed Protocol
4.1 Architecture of Proposed Protocol
4.2 Phase of Proposed Protocol
4.3 Security Analysis of Proposed Protocol
5 Formal Analysis
5.1 Protocol Verification Tool : Scyther
5.2 Specification
5.3 Analysis of the Verification Results
6 Conclusion
References
Memorable Password Generation with AES in ECB Mode
1 Introduction

2 Concerns
3 Methodology/Experimental Setup
4 Experiment Results
5 Conclusion
References
A Comprehensive Survey on Fingerprint Liveness Detection Algorithms by Database and Scanner Model
1 Introduction
2 A Brief Review of the LivDet Competition Series
3 The LivDet-2009 Competition Dataset
4 The LivDet-2011 Competition Dataset
5 The LivDet-2013 Competition Dataset
6 The LivDet-2015 Competition Dataset
7 The LivDet-2017 Competition Dataset
8 Traditional Machine Learning Algorithms
9 Performance on Other Datasets

9.1 Performance on ATVS Data by Scanner Type (Capacitive, Optical, and Thermal)
9.2 Performance on Miscellaneous Datasets Using ACE, FAR, and FRR
10 Conclusion
References
Suitability of Voice Recognition Within the IoT Environment
1 Introduction
2 Background
2.1 Filter Bank Analysis
2.2 Linear Predictive Coding [LPC]
3 Related Work
4 Motivation
5 Our Proposed Model
6 Implementation
6.1 Description of the Implementation
6.2 Open Source Software
6.3 Dataset
6.4 Preprocessing
6.5 Feature Extraction
6.6 Voice Model Training/Server Side