Cybersecurity in intelligent networking systems / Shengjie Xu, Yi Qian, Rose Qingyang Hu.

Format:

Book

Author/Creator:

Xu, Shengjie (Professor), author.

Series:

IEEE Press

Language:

English

Subjects (All):

Computer networks--Security measures.

Computer networks.

Physical Description:

1 online resource (147 pages)

Place of Publication:

Chichester, England : John Wiley & Sons, [2023]

Summary:

"Data-driven network intelligence is an important revolution of the intelligent networking systems. Many well-established and cutting-edge edge network communications, artificial intelligence (AI), and cyber security technologies are applied into edge network to achieve a ?smart? and efficient data communication. In recent years, intelligent networking system has attracted more and more attention from industry, research, and academia. There is a need for a comprehensive book to investigate and summarize the recent advances in AI, cyber security, and edge network communications. This book will serve the purpose to investigate technologies, applications and issues in data-driven cyber infrastructure. Data-driven Network Intelligence for Cyber Security describes data-driven network intelligence for anomaly detection and information privacy. It covers a proposed novel data-driven network intelligence system, and further presents the edge computing empowered network intelligence."-- Provided by publisher.

Contents:

Cover

Title Page

Copyright

Contents

About the Authors

Preface

Acknowledgments

Acronyms

Chapter 1 Cybersecurity in the Era of Artificial Intelligence

1.1 Artificial Intelligence for Cybersecurity

1.1.1 Artificial Intelligence

1.1.2 Machine Learning

1.1.2.1 Supervised Learning

1.1.2.2 Unsupervised Learning

1.1.2.3 Semi‐supervised Learning

1.1.2.4 Reinforcement Learning

1.1.3 Data‐Driven Workflow for Cybersecurity

1.2 Key Areas and Challenges

1.2.1 Anomaly Detection

1.2.2 Trustworthy Artificial Intelligence

1.2.3 Privacy Preservation

1.3 Toolbox to Build Secure and Intelligent Systems

1.3.1 Machine Learning and Deep Learning

1.3.1.1 NumPy

1.3.1.2 SciPy

1.3.1.3 Scikit‐learn

1.3.1.4 PyTorch

1.3.1.5 TensorFlow

1.3.2 Privacy‐Preserving Machine Learning

1.3.2.1 Syft

1.3.2.2 TensorFlow Federated

1.3.2.3 TensorFlow Privacy

1.3.3 Adversarial Machine Learning

1.3.3.1 SecML and SecML Malware

1.3.3.2 Foolbox

1.3.3.3 CleverHans

1.3.3.4 Counterfit

1.3.3.5 MintNV

1.4 Data Repositories for Cybersecurity Research

1.4.1 NSL‐KDD

1.4.2 UNSW‐NB15

1.4.3 EMBER

1.5 Summary

Notes

References

Chapter 2 Cyber Threats and Gateway Defense

2.1 Cyber Threats

2.1.1 Cyber Intrusions

2.1.2 Distributed Denial of Services Attack

2.1.3 Malware and Shellcode

2.2 Gateway Defense Approaches

2.2.1 Network Access Control

2.2.2 Anomaly Isolation

2.2.3 Collaborative Learning

2.2.4 Secure Local Data Learning

2.3 Emerging Data‐driven Methods for Gateway Defense

2.3.1 Semi‐supervised Learning for Intrusion Detection

2.3.2 Transfer Learning for Intrusion Detection

2.3.3 Federated Learning for Privacy Preservation

2.3.4 Reinforcement Learning for Penetration Test.

2.4 Case Study: Reinforcement Learning for Automated Post‐breach Penetration Test

2.4.1 Literature Review

2.4.2 Research Idea

2.4.3 Training Agent Using Deep Q‐Learning

2.5 Summary

Chapter 3 Edge Computing and Secure Edge Intelligence

3.1 Edge Computing

3.2 Key Advances in Edge Computing

3.2.1 Security

3.2.2 Reliability

3.2.3 Survivability

3.3 Secure Edge Intelligence

3.3.1 Background and Motivation

3.3.2 Design of Detection Module

3.3.2.1 Data Pre‐processing

3.3.2.2 Model Learning

3.3.2.3 Model Updating

3.3.3 Challenges Against Poisoning Attacks

3.4 Summary

Chapter 4 Edge Intelligence for Intrusion Detection

4.1 Edge Cyberinfrastructure

4.2 Edge AI Engine

4.2.1 Feature Engineering

4.2.2 Model Learning

4.2.3 Model Update

4.2.4 Predictive Analytics

4.3 Threat Intelligence

4.4 Preliminary Study

4.4.1 Dataset

4.4.2 Environmental Setup

4.4.3 Performance Evaluation

4.4.3.1 Computational Efficiency

4.4.3.2 Prediction Accuracy

4.5 Summary

Chapter 5 Robust Intrusion Detection

5.1 Preliminaries

5.1.1 Median Absolute Deviation

5.1.2 Mahalanobis Distance

5.2 Robust Intrusion Detection

5.2.1 Problem Formulation

5.2.2 Step 1: Robust Data Pre‐processing

5.2.3 Step 2: Bagging for Labeled Anomalies

5.2.4 Step 3: One‐class SVM for Unlabeled Samples

5.2.4.1 One‐class Classification

5.2.4.2 Algorithm of Optimal Sampling Ratio Section

5.2.5 Step 4: The Final Classifier

5.3 Experimental and Evaluation

5.3.1 Experiment Setup

5.3.1.1 Datasets

5.3.1.2 Environmental Setup

5.3.1.3 Evaluation Metrics

5.3.2 Performance Evaluation

5.3.2.1 Step 1

5.3.2.2 Step 2

5.3.2.3 Step 3

5.3.2.4 Step 4

5.4 Summary

References.

Chapter 6 Efficient Pre‐processing Scheme for Anomaly Detection

6.1 Efficient Anomaly Detection

6.1.1 Related Work

6.1.2 Principal Component Analysis

6.2 Proposed Pre‐processing Scheme for Anomaly Detection

6.2.1 Robust Pre‐processing Scheme

6.2.2 Real‐Time Processing

6.2.3 Discussion

6.3 Case Study

6.3.1 Description of the Raw Data

6.3.1.1 Dimension

6.3.1.2 Predictors

6.3.1.3 Response Variables

6.3.2 Experiment

6.3.3 Results

6.4 Summary

Chapter 7 Privacy Preservation in the Era of Big Data

7.1 Privacy Preservation Approaches

7.1.1 Anonymization

7.1.2 Differential Privacy

7.1.3 Federated Learning

7.1.4 Homomorphic Encryption

7.1.5 Secure Multi‐party Computation

7.1.6 Discussion

7.2 Privacy‐Preserving Anomaly Detection

7.2.1 Literature Review

7.2.2 Preliminaries

7.2.2.1 Bilinear Groups

7.2.2.2 Asymmetric Predicate Encryption

7.2.3 System Model and Security Model

7.2.3.1 System Model

7.2.3.2 Security Model

7.3 Objectives and Workflow

7.3.1 Objectives

7.3.2 Workflow

7.4 Predicate Encryption‐Based Anomaly Detection

7.4.1 Procedures

7.4.2 Development of Predicate

7.4.3 Deployment of Anomaly Detection

7.5 Case Study and Evaluation

7.5.1 Overhead

7.5.2 Detection

7.6 Summary

Chapter 8 Adversarial Examples: Challenges and Solutions

8.1 Adversarial Examples

8.1.1 Problem Formulation in Machine Learning

8.1.2 Creation of Adversarial Examples

8.1.3 Targeted and Non‐targeted Attacks

8.1.4 Black‐box and White‐box Attacks

8.1.5 Defenses Against Adversarial Examples

8.2 Adversarial Attacks in Security Applications

8.2.1 Malware

8.2.2 Cyber Intrusions

8.3 Case Study: Improving Adversarial Attacks Against Malware Detectors

8.3.1 Background.

8.3.2 Adversarial Attacks on Malware Detectors

8.3.3 MalConv Architecture

8.3.4 Research Idea

8.4 Case Study: A Metric for Machine Learning Vulnerability to Adversarial Examples

8.4.1 Background

8.4.2 Research Idea

8.5 Case Study: Protecting Smart Speakers from Adversarial Voice Commands

8.5.1 Background

8.5.2 Challenges

8.5.3 Directions and Tasks

8.6 Summary

Index

EULA.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Other Format:

Print version: Xu, Shengjie Cybersecurity in Intelligent Networking Systems

ISBN:

9781119784135

1119784131

9781119784104

1119784107

9781119784128

1119784123

OCLC:

1343160840