Mastering machine learning for penetration testing develop an extensive skill set to break self-learning systems using Python
Become a master at penetration testing using machine learning with Python About This Book Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Who This Book Is For This book is for pen testers...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt
2018.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630649106719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Contributors
- Table of Contents
- Preface
- Chapter 1: 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
- Chapter 2: 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
- Chapter 3: 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
- Chapter 4: 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
- Chapter 5: 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
- Chapter 6: 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
- Chapter 7: 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
- Chapter 8: 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
- Chapter 9: 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
- Chapter 10: 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
- Other Books You May Enjoy
- Index.
