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Book Cover
E-book
Author Verma, Rakesh M

Title Cybersecurity Analytics
Published Milton : CRC Press LLC, 2019

Copies

Description 1 online resource (357 pages)
Series Chapman and Hall/CRC Data Science Ser
Chapman and Hall/CRC Data Science Ser
Contents Cover; Half Title; Series Page; Title Page; Copyright Page; Dedication; Contents; Preface; Acknowledgments; 1. Introduction; 2. What Is Data Analytics?; 2.1 Data Ingestion; 2.2 Data Processing and Cleaning; 2.3 Visualization and Exploratory Analysis; 2.3.1 Scatterplots; 2.4 Pattern Recognition; 2.4.1 Classification; 2.4.2 Clustering; 2.5 Feature Extraction; 2.5.1 Feature Selection; 2.5.2 Random Projections; 2.6 Modeling; 2.6.1 Model Specification; 2.6.2 Model Selection and Fitting; 2.7 Evaluation; 2.8 Strengths and Limitations; 2.8.1 The Curse of Dimensionality
3. Security: Basics and Security Analytics3.1 Basics of Security; 3.1.1 Know Thy Enemy -- Attackers and Their Motivations; 3.1.2 Security Goals; 3.2 Mechanisms for Ensuring Security Goals; 3.2.1 Confidentiality; 3.2.2 Integrity; 3.2.3 Availability; 3.2.4 Authentication; 3.2.5 Access Control; 3.2.6 Accountability; 3.2.7 Nonrepudiation; 3.3 Threats, Attacks and Impacts; 3.3.1 Passwords; 3.3.2 Malware; 3.3.3 Spam, Phishing and its Variants; 3.3.4 Intrusions; 3.3.5 Internet Surfing; 3.3.6 System Maintenance and Firewalls; 3.3.7 Other Vulnerabilities; 3.3.8 Protecting Against Attacks
3.4 Applications of Data Science to Security Challenges3.4.1 Cybersecurity Data Sets; 3.4.2 Data Science Applications; 3.4.3 Passwords; 3.4.4 Malware; 3.4.5 Intrusions; 3.4.6 Spam/Phishing; 3.4.7 Credit Card Fraud/Financial Fraud; 3.4.8 Opinion Spam; 3.4.9 Denial-of-Service; 3.5 Security Analytics and Why We Need It; 4. Statistics; 4.1 Probability Density Estimation; 4.2 Models; 4.2.1 Poisson; 4.2.2 Uniform; 4.2.3 Normal; 4.3 Parameter Estimation; 4.3.1 The Bias-Variance Trade-Off; 4.4 The Law of Large Numbers and the Central Limit Theorem; 4.5 Confidence Intervals; 4.6 Hypothesis Testing
4.7 Bayesian Statistics4.8 Regression; 4.8.1 Logistic Regression; 4.9 Regularization; 4.10 Principal Components; 4.11 Multidimensional Scaling; 4.12 Procrustes; 4.13 Nonparametric Statistics; 4.14 Time Series; 5. Data Mining -- Unsupervised Learning; 5.1 Data Collection; 5.2 Types of Data and Operations; 5.2.1 Properties of Data Sets; 5.3 Data Exploration and Preprocessing; 5.3.1 Data Exploration; 5.3.2 Data Preprocessing/Wrangling; 5.4 Data Representation; 5.5 Association Rule Mining; 5.5.1 Variations on the Apriori Algorithm; 5.6 Clustering; 5.6.1 Partitional Clustering; 5.6.2 Choosing K
5.6.3 Variations on K-means Algorithm5.6.4 Hierarchical Clustering; 5.6.5 Other Clustering Algorithms; 5.6.6 Measuring the Clustering Quality; 5.6.7 Clustering Miscellany: Clusterability, Robustness, Incremen-tal ... ; 5.7 Manifold Discovery; 5.7.1 Spectral Embedding; 5.8 Anomaly Detection; 5.8.1 Statistical Methods; 5.8.2 Distance-based Outlier Detection; 5.8.3 kNN Based Approach; 5.8.4 Density-based Outlier Detection; 5.8.5 Clustering-based Outlier Detection; 5.8.6 One-class Learning Based Outliers; 5.9 Security Applications and Adaptations; 5.9.1 Data Mining for Intrusion Detection
Notes 5.9.2 Malware Detection
Bibliography Includes bibliographical references and indexes
Notes Print version record
Subject Computer security.
Computer Security
Computer security
Form Electronic book
Author Marchette, David J
ISBN 9781000727654
1000727653
9781000727890
1000727890
9781000727777
1000727777
9780429326813
0429326815