A Closer Look at Cybersecurity and Cryptanalysis

CONTENTS
Preface
Acknowledgments
Chapter 1
Cryptanalysis from  an Adversary View: A Detailed Analogy of Cryptanalysis
	Abstract
	Introduction
	Methods
		What Parameters Do We Need to Consider for Cryptanalysis?
		Where the Cryptanalysis Takes Place?
		How Affine Cipher Works?
		The Weakness of Affine Cipher
		Cryptanalysis of Affine Cipher
	Conclusion
Chapter 2
Cryptanalysis and Various Attacks
	Abstract
	Introduction
	Methods
		Ciphertext Only Attack
		Known Plaintext Attack
		Chosen Plaintext Attack
		Chosen Ciphertext Attack
		Man-in-the-Middle Attack
		Brute Force Attack
		Side-Channel Attack
		Malleability Attack
		Birthday Attack
		Divide and Conquer Attack
		Correlation Attack
		Guess and Determine Attack
		Power Analysis Attack
		Some General Cryptanalysis Attacks
	Recommended Strategy
	Conclusion
	References
Chapter 3
Evolution of Cryptanalysis Techniques: A Review
	Abstract
	1. Introduction
	2. Methods
		2.1. Cryptanalysis of Block Ciphers
			Integral Cryptanalysis
		2.2. Cryptanalysis of RSA
		2.3. Neural Cryptanalysis
		2.4. AI Based Cryptanalysis
		2.5. DNA Based Cryptanalysis
	3. Future Challenges
	Conclusion
	References
Chapter 4
Role of Penetration Testing  in Security Analysis
	Abstract
	Introduction
	Pen Test Phases
	Methods
		Reconnaissance
		Scanning
		Gaining Access
		Maintaining Access
		Reporting and Analysis
		Results and Vulnerability Analysis
	Conclusion
Chapter 5
Vulnerability Assessment Using Nessus Tool
	Abstract
	1. Introduction
	2. Installation of NESSUS Tool
	3. Functioning of NESSUS Tool
	Conclusion
Chapter 6
Digitization with Cyber Security  and AI: A Review
	Abstract
	1. Introduction
		1.1. Types of Threats
		1.2. Major Domains
		1.3. Requirements of Cyber Security
		1.4. Benefits
		1.5. Complications
	2. Applying AI for Cyber Security
		2.1. AI Based Authentication
		2.2. Phishing Detection Using AI
		2.3. AI Based Behavioral Analysis
		2.4. AI Based Malware Detection
	Conclusion
	References
Chapter 7
An Analytical Approach for Checking the Performance of Different Classifiers  in Network of IDS
	Abstract
	1. Intrusion Detection Architecture
		1.1. Data Mining Used in IDS
			1.1.1. Data Mining Techniques for IDS
		1.2. Data Mining Algorithms in IDS
			1.2.1. Decision Trees
			1.2.2. Bayesian System Classifier
			1.2.3. K-Nearest Neighbor
			1.2.4. Artificial Neural Network
			1.2.5. Support Vector Machine
			1.2.6. K-Means Clustering
			1.2.7. Genetic Algorithms
		1.3. Models
			1.3.1. Artificial Neural Network (ANN)
			1.3.2. Support Vector Machines (SVM)
			1.3.3. Multivariate Adaptive Regression Splines (MARS)
			1.3.4. Bayesian Thinking
				1.3.4.1. Bayesian Systems
			1.3.5. Naïve Bayes
	Conclusion
	References
Chapter 8
Smart IoT Device for Women Safety
	Abstract
	1. Introduction
	2. Methods
		2.1. Related Works
		2.2. Proposed Methodology
			2.2.1. Hardware Components
				2.2.1.1. Raspberry Pi
				2.2.1.2. GPS
				2.2.1.3. Force Sensor
				2.2.1.4. AD Convertor
			2.2.2. Software Components
				2.2.2.1. Web Server
				2.2.2.2. SMS Gateway
				2.2.2.3. Python
		2.3. Procedure
		2.4. Explanation
	3. Results
		3.1. SMS Output
	Conclusion
	References
About the Editors
Index
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