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Table of Contents
Intro
Preface
Contents
Introduction
1 Context and Definition
2 Characteristics and Design Goals
3 Security and Hardening
4 Intelligence
5 Summary
References
Machine Learning Construction: Implications to Cybersecurity
1 Introduction
1.1 Motivation
1.2 Notation
1.3 Roadmap
2 Statistical Decision Theory
2.1 Regression
2.2 Classification
2.3 Where Is Learning?
3 Parametric Regression and Classification
3.1 Linear Models (LM)
3.2 Generalized Linear Models (GLM)
3.3 Nonlinear Models
4 Nonparametric Regression and Classification
4.1 Smoothing Techniques
4.2 Additive Models (AM)
4.3 Generalized Additive Models (GAM)
4.4 Projection Pursuit Regression (PPR)
4.5 Neural Networks (NN)
5 Optimization
5.1 Introduction
5.2 Connection to Machine Learning
5.3 Types of MOP
6 Performance
6.1 Error Components
6.2 Receiver Operating Characteristic (ROC) Curve
6.3 The True Performance Is A Random Variable!
6.4 Bias-Variance Decomposition
6.5 Curse of Dimensionality
6.6 Performance of Unsupervised Learning
6.7 Classifier Calibration
7 Discussion and Conclusion
References
Machine Learning Assessment: Implications to Cybersecurity
1 Introduction
1.1 Motivation
1.2 Notation
1.3 Roadmap
2 Nonparametric Methods for Estimating the Bias and the Variance of a Statistic
2.1 Bootstrap Estimate
2.2 Jackknife Estimate
2.3 Bootstrap Versus Jackknife
2.4 Influence Function, Infinitesimal Jackknife, and Estimate of Variance
3 Nonparametric Methods for Estimating the Error Rate of a Classification Rule
3.1 Apparent Error
3.2 Cross Validation (CV)
3.3 Bootstrap Methods for Error Rate Estimation
3.4 Estimating the Standard Error of Error Rate Estimators
4 Nonparametric Methods for Estimating the AUC of a Classification Rule
4.1 Construction of Nonparametric Estimators for AUC
4.2 The Leave-Pair-Out Boostrap (LPOB) ModifyingAbove upper A upper U upper C With caret Super Subscript left parenthesis 1 comma 1 right parenthesisAUC""0362AUC( 1,1) , Its Smoothness and Variance Estimation
4.3 Estimating the Standard Error of AUC Estimators
5 Illustrative Numerical Examples
5.1 Error Rate Estimation
5.2 AUC Estimation
5.3 Components of Variance and Weak Correlation
5.4 Two Competing Classifiers
6 Discussion and Conclusion
7 Appendix
7.1 Proofs
7.2 More on Influence Function (IF)
7.3 ML in Other Fields
References
A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms
1 Introduction
2 Mil-STD-1553 Baseline
2.1 Major Components
2.2 Bus Communication
3 Mil-Std-1553 Attack Vectors
3.1 Assumptions and Attacker Position/foothold on 1553 Platform
3.2 Attack Vectors and Types
4 Simulation and IDS Dataset Generation
4.1 Simulation Setup
4.2 Baseline Scenarios and Datasets
Preface
Contents
Introduction
1 Context and Definition
2 Characteristics and Design Goals
3 Security and Hardening
4 Intelligence
5 Summary
References
Machine Learning Construction: Implications to Cybersecurity
1 Introduction
1.1 Motivation
1.2 Notation
1.3 Roadmap
2 Statistical Decision Theory
2.1 Regression
2.2 Classification
2.3 Where Is Learning?
3 Parametric Regression and Classification
3.1 Linear Models (LM)
3.2 Generalized Linear Models (GLM)
3.3 Nonlinear Models
4 Nonparametric Regression and Classification
4.1 Smoothing Techniques
4.2 Additive Models (AM)
4.3 Generalized Additive Models (GAM)
4.4 Projection Pursuit Regression (PPR)
4.5 Neural Networks (NN)
5 Optimization
5.1 Introduction
5.2 Connection to Machine Learning
5.3 Types of MOP
6 Performance
6.1 Error Components
6.2 Receiver Operating Characteristic (ROC) Curve
6.3 The True Performance Is A Random Variable!
6.4 Bias-Variance Decomposition
6.5 Curse of Dimensionality
6.6 Performance of Unsupervised Learning
6.7 Classifier Calibration
7 Discussion and Conclusion
References
Machine Learning Assessment: Implications to Cybersecurity
1 Introduction
1.1 Motivation
1.2 Notation
1.3 Roadmap
2 Nonparametric Methods for Estimating the Bias and the Variance of a Statistic
2.1 Bootstrap Estimate
2.2 Jackknife Estimate
2.3 Bootstrap Versus Jackknife
2.4 Influence Function, Infinitesimal Jackknife, and Estimate of Variance
3 Nonparametric Methods for Estimating the Error Rate of a Classification Rule
3.1 Apparent Error
3.2 Cross Validation (CV)
3.3 Bootstrap Methods for Error Rate Estimation
3.4 Estimating the Standard Error of Error Rate Estimators
4 Nonparametric Methods for Estimating the AUC of a Classification Rule
4.1 Construction of Nonparametric Estimators for AUC
4.2 The Leave-Pair-Out Boostrap (LPOB) ModifyingAbove upper A upper U upper C With caret Super Subscript left parenthesis 1 comma 1 right parenthesisAUC""0362AUC( 1,1) , Its Smoothness and Variance Estimation
4.3 Estimating the Standard Error of AUC Estimators
5 Illustrative Numerical Examples
5.1 Error Rate Estimation
5.2 AUC Estimation
5.3 Components of Variance and Weak Correlation
5.4 Two Competing Classifiers
6 Discussion and Conclusion
7 Appendix
7.1 Proofs
7.2 More on Influence Function (IF)
7.3 ML in Other Fields
References
A Collection of Datasets for Intrusion Detection in MIL-STD-1553 Platforms
1 Introduction
2 Mil-STD-1553 Baseline
2.1 Major Components
2.2 Bus Communication
3 Mil-Std-1553 Attack Vectors
3.1 Assumptions and Attacker Position/foothold on 1553 Platform
3.2 Attack Vectors and Types
4 Simulation and IDS Dataset Generation
4.1 Simulation Setup
4.2 Baseline Scenarios and Datasets