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Mastering Machine Learning for Penetration Testing
Cybersecurityiscrucialforbothbusinessesandindividuals.Assystemsaregettingsmarter,wenowseemachinelearninginterruptingcomputersecurity.Withtheadoptionofmachinelearninginupcomingsecurityproducts,it’simportantforpentestersandsecurityresearcherstounderstandhowthesesystemswork,andtobreachthemfortestingpurposes.Thisbookbeginswiththebasicsofmachinelearningandthealgorithmsusedtobuildrobustsystems.Onceyou’vegainedafairunderstandingofhowsecurityproductsleveragemachinelearning,you'lldiveintothecoreconceptsofbreachingsuchsystems.Throughpracticalusecases,you’llseehowtofindloopholesandsurpassaself-learningsecuritysystem.Asyoumakeyourwaythroughthechapters,you’llfocusontopicssuchasnetworkintrusiondetectionandAVandIDSevasion.We’llalsocoverthebestpracticeswhenidentifyingambiguities,andextensivetechniquestobreachanintelligentsystem.Bytheendofthisbook,youwillbewell-versedwithidentifyingloopholesinaself-learningsecuritysystemandwillbeabletoefficientlybreachamachinelearningsystem.
目錄(206章)
倒序
- 封面
- 版權(quán)信息
- Dedication
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Introduction to Machine Learning in Pentesting
- Technical requirements
- Artificial intelligence and machine learning
- Machine learning models and algorithms
- Supervised
- Bayesian classifiers
- Support vector machines
- Decision trees
- Semi-supervised
- Unsupervised
- Artificial neural networks
- Linear regression
- Logistic regression
- Clustering with k-means
- Reinforcement
- Performance evaluation
- Dimensionality reduction
- Improving classification with ensemble learning
- Machine learning development environments and Python libraries
- NumPy
- SciPy
- TensorFlow
- Keras
- pandas
- Matplotlib
- scikit-learn
- NLTK
- Theano
- Machine learning in penetration testing - promises and challenges
- Deep Exploit
- Summary
- Questions
- Further reading
- Phishing Domain Detection
- Technical requirements
- Social engineering overview
- Social Engineering Engagement Framework
- Steps of social engineering penetration testing
- Building real-time phishing attack detectors using different machine learning models
- Phishing detection with logistic regression
- Phishing detection with decision trees
- NLP in-depth overview
- Open source NLP libraries
- Spam detection with NLTK
- Summary
- Questions
- Malware Detection with API Calls and PE Headers
- Technical requirements
- Malware overview
- Malware analysis
- Static malware analysis
- Dynamic malware analysis
- Memory malware analysis
- Evasion techniques
- Portable Executable format files
- Machine learning malware detection using PE headers
- Machine learning malware detection using API calls
- Summary
- Questions
- Further reading
- Malware Detection with Deep Learning
- Technical requirements
- Artificial neural network overview
- Implementing neural networks in Python
- Deep learning model using PE headers
- Deep learning model with convolutional neural networks and malware visualization
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short Term Memory networks
- Hopfield networks
- Boltzmann machine networks
- Malware detection with CNNs
- Promises and challenges in applying deep learning to malware detection
- Summary
- Questions
- Further reading
- Botnet Detection with Machine Learning
- Technical requirements
- Botnet overview
- Building a botnet detector model with multiple machine learning techniques
- How to build a Twitter bot detector
- Visualization with seaborn
- Summary
- Questions
- Further reading
- Machine Learning in Anomaly Detection Systems
- Technical requirements
- An overview of anomaly detection techniques
- Static rules technique
- Network attacks taxonomy
- The detection of network anomalies
- HIDS
- NIDS
- Anomaly-based IDS
- Building your own IDS
- The Kale stack
- Summary
- Questions
- Further reading
- Detecting Advanced Persistent Threats
- Technical requirements
- Threats and risk analysis
- Threat-hunting methodology
- The cyber kill chain
- The diamond model of intrusion analysis
- Threat hunting with the ELK Stack
- Elasticsearch
- Kibana
- Logstash
- Machine learning with the ELK Stack using the X-Pack plugin
- Summary
- Questions
- Evading Intrusion Detection Systems
- Technical requirements
- Adversarial machine learning algorithms
- Overfitting and underfitting
- Overfitting and underfitting with Python
- Detecting overfitting
- Adversarial machine learning
- Evasion attacks
- Poisoning attacks
- Adversarial clustering
- Adversarial features
- CleverHans
- The AML library
- EvadeML-Zoo
- Evading intrusion detection systems with adversarial network systems
- Summary
- Questions
- Further reading
- Bypassing Machine Learning Malware Detectors
- Technical requirements
- Adversarial deep learning
- Foolbox
- Deep-pwning
- EvadeML
- Bypassing next generation malware detectors with generative adversarial networks
- The generator
- The discriminator
- MalGAN
- Bypassing machine learning with reinforcement learning
- Reinforcement learning
- Summary
- Questions
- Further reading
- Best Practices for Machine Learning and Feature Engineering
- Technical requirements
- Feature engineering in machine learning
- Feature selection algorithms
- Filter methods
- Pearson's correlation
- Linear discriminant analysis
- Analysis of variance
- Chi-square
- Wrapper methods
- Forward selection
- Backward elimination
- Recursive feature elimination
- Embedded methods
- Lasso linear regression L1
- Ridge regression L2
- Tree-based feature selection
- Best practices for machine learning
- Information security datasets
- Project Jupyter
- Speed up training with GPUs
- Selecting models and learning curves
- Machine learning architecture
- Coding
- Data handling
- Business contexts
- Summary
- Questions
- Further reading
- Assessments
- Chapter 1 – Introduction to Machine Learning in Pentesting
- Chapter 2 – Phishing Domain Detection
- Chapter 3 – Malware Detection with API Calls and PE Headers
- Chapter 4 – Malware Detection with Deep Learning
- Chapter 5 – Botnet Detection with Machine Learning
- Chapter 6 – Machine Learning in Anomaly Detection Systems
- Chapter 7 – Detecting Advanced Persistent Threats
- Chapter 8 – Evading Intrusion Detection Systems with Adversarial Machine Learning
- Chapter 9 – Bypass Machine Learning Malware Detectors
- Chapter 10 – Best Practices for Machine Learning and Feature Engineering
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-25 21:03:42
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