Cybersecurity analytics (Record no. 217176)

000 -LEADER
fixed length control field 06584aam a2200217 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210401b2020 ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780367346010
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.8
Item number V3C9
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Verma, Rakesh M.
9 (RLIN) 406332
245 ## - TITLE STATEMENT
Title Cybersecurity analytics
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2020
Place of publication, distribution, etc Boca Raton
300 ## - PHYSICAL DESCRIPTION
Extent xv, 339 p.: ill.
Other physical details Includes bibliographical references and index
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman and Hall/CRC: data science
9 (RLIN) 406333
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Table of contents<br/><br/>1. Introduction<br/>2. What is Data Analytics?<br/>Data Ingestion<br/>Data Processing and Cleaning<br/>Visualization and Exploratory Analysis<br/>Scatterplots<br/>Pattern Recognition<br/>Classification<br/>Clustering<br/>Feature extraction<br/>Feature Selection<br/>Random Projections<br/>Modeling<br/>Model Specification<br/>Model Selection and Fitting<br/>Evaluation<br/>Strengths and Limitations<br/>The Curse of Dimensionality<br/>3. Security: Basics and Security Analytics<br/>Basics of Security<br/>Know Thy Enemy – Attackers and Their Motivations<br/>Security Goals<br/>Mechanisms for Ensuring Security Goals<br/>Confidentiality<br/>Integrity<br/>Availability<br/>Authentication<br/>Access Control<br/>Accountability<br/>Non-repudiation<br/>Threats, Attacks and Impacts<br/>Passwords<br/>Malware<br/>Spam, Phishing and its Variants<br/>Intrusions<br/>Internet Surfing<br/>System Maintenance and Firewalls<br/>Other Vulnerabilities<br/>Protecting Against Attacks<br/>Applications of Data Science to Security Challenges<br/>Cybersecurity Datasets<br/>Data Science Applications<br/>Passwords<br/>Malware<br/>Intrusions<br/>Spam/Phishing<br/>Credit Card Fraud/Financial Fraud<br/>Opinion Spam<br/>Denial of Service<br/>Security Analytics and Why Do We Need It<br/>4. Statistics<br/>Probability Density Estimation<br/>Models<br/>Poisson<br/>Uniform<br/>Normal<br/>Parameter Estimation<br/>The Bias-Variance Trade-Off<br/>The Law of Large Numbers and the Central Limit Theorem<br/>Confidence Intervals<br/>Hypothesis Testing<br/>Bayesian Statistics<br/>Regression<br/>Logistic Regression<br/>Regularization<br/>Principal Components<br/>Multidimensional Scaling<br/>Procrustes<br/>Nonparametric Statistics<br/>Time Series<br/>5. Data Mining – Unsupervised Learning<br/>Data Collection<br/>Types of Data and Operations<br/>Properties of Datasets<br/>Data Exploration and Preprocessing<br/>Data Exploration<br/>Data Preprocessing/Wrangling<br/>Data Representation<br/>Association Rule Mining<br/>Variations on the Apriori Algorithm<br/>Clustering<br/>Partitional Clustering<br/>Choosing K<br/>Variations on K-means Algorithm<br/>Hierarchical Clustering<br/>Other Clustering Algorithms<br/>Measuring the Clustering Quality<br/>Clustering Miscellany: Clusterability, Robustness, Incremental,<br/>Manifold Discovery<br/>Spectral Embedding<br/>Anomaly Detection<br/>Statistical Methods<br/>Distance-based Outlier Detection<br/>kNN based approach<br/>Density-based Outlier Detection<br/>Clustering-based Outlier Detection<br/>One-class learning based Outliers<br/>Security Applications and Adaptations<br/>Data Mining for Intrusion Detection<br/>Malware Detection<br/>Stepping-stone Detection<br/>Malware Clustering<br/>Directed Anomaly Scoring for Spear Phishing Detection<br/>Concluding Remarks and Further Reading<br/>6. Machine Learning – Supervised Learning<br/>Fundamentals of Supervised Learning<br/>The Bayes Classifier<br/>Naïve Bayes<br/>Nearest Neighbors Classifiers<br/>Linear Classifiers<br/>Decision Trees and Random Forests<br/>Random Forest<br/>Support Vector Machines<br/>Semi-Supervised Classification<br/>Neural Networks and Deep Learning<br/>Perceptron<br/>Neural Networks<br/>Deep Networks<br/>Topological Data Analysis<br/>Ensemble Learning<br/>Majority<br/>Adaboost<br/>One-class Learning<br/>Online Learning<br/>Adversarial Machine Learning<br/>Adversarial Examples<br/>Adversarial Training<br/>Adversarial Generation<br/>Beyond Continuous Data<br/>Evaluation of Machine Learning<br/>Cost-sensitive Evaluation<br/>New Metrics for Unbalanced Datasets<br/>Security Applications and Adaptations<br/>Intrusion Detection<br/>Malware Detection<br/>Spam and Phishing Detection<br/>For Further Reading<br/>7. Text Mining<br/>Tokenization<br/>Preprocessing<br/>Bag-Of-Words<br/>Vector space model<br/>Weighting<br/>Latent Semantic Indexing<br/>Embedding<br/>Topic Models: Latent Dirichlet Allocation<br/>Sentiment Analysis<br/>8. Natural Language Processing<br/>Challenges of NLP<br/>Basics of Language Study and NLP Techniques<br/>Text Preprocessing<br/>Feature Engineering on Text Data<br/>Morphological, Word and Phrasal Features<br/>Clausal and Sentence Level Features<br/>Statistical Features<br/>Corpus-based Analysis<br/>Advanced NLP Tasks<br/>Part of Speech Tagging<br/>Word sense Disambiguation<br/>Language Modeling<br/>Topic Modeling<br/>Sequence to Sequence Tasks<br/>Knowledge Bases and Frameworks<br/>Natural Language Generation<br/>Issues with Pipelining<br/>Security Applications of NLP<br/>Password Checking<br/>Email Spam Detection<br/>Phishing Email Detection<br/>Malware Detection<br/>Attack Generation<br/>9. Big Data Techniques and Security<br/>Key terms<br/>Ingesting the Data<br/>Persistent Storage<br/>Computing and Analyzing<br/>Techniques for Handling Big Data<br/>Visualizing<br/>Streaming Data<br/>Big Data Security<br/>Implications of Big Data Characteristics on Security and Privacy<br/>Mechanisms for Big Data Security Goals<br/>A. Linear Algebra Basics<br/>Vectors<br/>Matrices<br/>Eigenvectors and Eigenvalues<br/>The Singular Value Decomposition<br/>B. Graphs<br/>Graph Invariants<br/>The Laplacian<br/>7. Probability<br/>Probability<br/>Conditional Probability and Bayes’ Rule<br/>Base Rate Fallacy<br/>Expected Values and Moments<br/>Distribution Functions and Densities<br/>Models<br/>Bernoulli and Binomial<br/>Multinomial<br/>Uniform<br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material.<br/>The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.<br/><br/>https://www.routledge.com/Cybersecurity-Analytics/Verma-Marchette/p/book/9780367346010
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer security
9 (RLIN) 56817
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Security measures - Computer networks
9 (RLIN) 406754
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computers
9 (RLIN) 62923
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Marchette, David J.
9 (RLIN) 406755
Relator term Co-author
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Total Checkouts Full call number Barcode Checked out Date last seen Date last borrowed Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library General Stacks 29/03/2021 5 7480.98 Slot 98 (0 Floor, West Wing) 1 005.8 V3C9 203564 18/08/2021 20/04/2021 20/04/2021 9351.23 Books

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