Cybersecurity Analytics / Rakesh M Verma

Preface IntroductionWhat is Data Analytics?Data Ingestion Data Processing and CleaningVisualization and Exploratory Analysis Scatterplots Pattern Recognition Classification Clustering Feature extractionFeature Selection Random Projections ModelingModel Specification Model Selection and FittingEvaluationStrengths and Limitations The Curse of DimensionalitySecurity: Basics and Security AnalyticsBasics of Security Know Thy Enemy - Attackers and Their Motivations Security Goals Mechanisms for Ensuring Security Goals Confidentiality IntegrityAvailability AuthenticationAccess Control Accountability Non-repudiation Threats, Attacks and Impacts Passwords Malware Spam, Phishing and its Variants Intrusions Internet SurfingSystem Maintenance and Firewalls Other Vulnerabilities Protecting Against Attacks Applications of Data Science to Security Challenges Cybersecurity Datasets Data Science Applications Passwords MalwareIntrusionsSpam/Phishing Credit Card Fraud/Financial FraudOpinion SpamDenial of Service Security Analytics and Why Do We Need It StatisticsProbability Density EstimationModelsPoisson UniformNormal Parameter EstimationThe Bias-Variance Trade-Off The Law of Large Numbers and the Central Limit Theorem Confidence IntervalsHypothesis Testing Bayesian Statistics Regression Logistic Regression Regularization Principal Components Multidimensional Scaling ProcrustesNonparametric Statistics Time Series Data Mining - Unsupervised Learning Data Collection Types of Data and Operations Properties of Datasets Data Exploration and Preprocessing Data Exploration Data Preprocessing/Wrangling Data Representation Association Rule Mining Variations on the Apriori Algorithm Clustering Partitional Clustering Choosing K Variations on K-means Algorithm Hierarchical Clustering Other Clustering Algorithms Measuring the Clustering Quality Clustering Miscellany: Clusterability, Robustness, Incremental,Manifold DiscoverySpectral Embedding Anomaly DetectionStatistical MethodsDistance-based Outlier DetectionkNN based approach Density-based Outlier Detection Clustering-based Outlier Detection One-class learning based Outliers Security Applications and Adaptations Data Mining for Intrusion DetectionMalware Detection Stepping-stone Detection Malware Clustering Directed Anomaly Scoring for Spear Phishing DetectionConcluding Remarks and Further Reading Machine Learning - Supervised LearningFundamentals of Supervised Learning The Bayes ClassifierNaive Bayes Nearest Neighbors Classifiers Linear Classifiers Decision Trees and Random ForestsRandom ForestSupport Vector Machines Semi-Supervised Classification Neural Networks and Deep Learning PerceptronNeural Networks Deep Networks Topological Data AnalysisEnsemble Learning Majority Adaboost One-class LearningOnline LearningAdversarial Machine LearningAdversarial Examples Adversarial Training Adversarial Generation Beyond Continuous Data Evaluation of Machine Learning Cost-sensitive Evaluation New Metrics for Unbalanced Datasets Security Applications and Adaptations Intrusion Detection Malware DetectionSpam and Phishing DetectionFor Further ReadingText Mining Tokenization PreprocessingBag-Of-WordsVector space modelWeightingLatent Semantic Indexing Embedding Topic Models: Latent Dirichlet Allocation Sentiment AnalysisNatural Language ProcessingChallenges of NLP Basics of Language Study and NLP Techniques Text Preprocessing Feature Engineering on Text Data Morphological, Word and Phrasal Features Clausal and Sentence Level Features Statistical Features Corpus-based Analysis Advanced NLP Tasks Part of Speech Tagging Word sense Disambiguation Language Modeling Topic Modeling Sequence to Sequence TasksKnowledge Bases and FrameworksNatural Language GenerationIssues with PipeliningSecurity Applications of NLP Password Checking Email Spam Detection Phishing Email Detection Malware DetectionAttack Generation Big Data Techniques and Security Key terms Ingesting the Data Persistent Storage Computing and Analyzing Techniques for Handling Big Data VisualizingStreaming Data Big Data Security Implications of Big Data Characteristics on Security and Privacy Mechanisms for Big Data Security GoalsLinear Algebra Basics Vectors MatricesEigenvectors and EigenvaluesThe Singular Value DecompositionGraphs Graph Invariants The Laplacian Probability ProbabilityConditional Probability and Bayes' Rule Base Rate Fallacy Expected Values and Moments Distribution Functions and Densities ModelsBernoulli and BinomialMultinomial UniformBibliography Author Index Index

This book organizes in one place the mathematics, probability, statistics and machine learning information that is required for a practitioner of cybersecurity analytics, as well as the basics of cybersecurity needed for a practitioner"--