Description |
1 online resource (357 pages) |
Series |
Chapman and Hall/CRC Data Science Ser |
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Chapman and Hall/CRC Data Science Ser
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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 |
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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 |
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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 |
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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 |
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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.
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Computer Security
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Computer security
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Form |
Electronic book
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Author |
Marchette, David J
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ISBN |
9781000727654 |
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1000727653 |
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9781000727890 |
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1000727890 |
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9781000727777 |
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1000727777 |
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9780429326813 |
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0429326815 |
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