A Closer Look at Cybersecurity and Cryptanalysis

Description

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 Blank Page