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Chebbi, Chiheb. Mastering machine learning for penetration testing: develop an extensive skill set to break self-learning systems using Python / Chiheb Chebbi. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/1840534.pdf>.

Record create date: 7/25/2018

Subject: Python (Computer program language); Machine learning.; Penetration testing (Computer security); Computer networks — Security measures.; COMPUTERS / Programming Languages / Python.; Computer networks — Security measures.; Machine learning.; Penetration testing (Computer security); Python (Computer program language)

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Table of Contents

  • 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

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