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Halder, Soma. Hands-on machine learning for cybersecurity: safeguard your system by making your machines intelligent using the Python ecosystem / Soma Halder, Sinan Ozdemir. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/1993339.pdf>.

Дата создания записи: 31.01.2019

Тематика: Computer security.; Machine learning.; COMPUTERS / Security / General; Computer security.; Machine learning.

Коллекции: EBSCO

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Оглавление

  • Cover
  • Title Page
  • Copyright and Credits
  • About Packt
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Basics of Machine Learning in Cybersecurity
    • What is machine learning?
      • Problems that machine learning solves
      • Why use machine learning in cybersecurity?
      • Current cybersecurity solutions
      • Data in machine learning
        • Structured versus unstructured data
        • Labelled versus unlabelled data
        • Machine learning phases
        • Inconsistencies in data
          • Overfitting
          • Underfitting
      • Different types of machine learning algorithm
        • Supervised learning algorithms
        • Unsupervised learning algorithms 
        • Reinforcement learning
        • Another categorization of machine learning
        • Classification problems
        • Clustering problems
        • Regression problems
        • Dimensionality reduction problems
        • Density estimation problems
        • Deep learning
      • Algorithms in machine learning
        • Support vector machines
        • Bayesian networks
        • Decision trees
        • Random forests
        • Hierarchical algorithms
        • Genetic algorithms
        • Similarity algorithms
        • ANNs
      • The machine learning architecture
        • Data ingestion
        • Data store
        • The model engine
          • Data preparation 
          • Feature generation
          • Training
          • Testing
        • Performance tuning
          • Mean squared error
          • Mean absolute error
          • Precision, recall, and accuracy
        • How can model performance be improved?
          • Fetching the data to improve performance
          • Switching machine learning algorithms
          • Ensemble learning to improve performance
      • Hands-on machine learning
        • Python for machine learning
        • Comparing Python 2.x with 3.x 
        • Python installation 
        • Python interactive development environment
          • Jupyter Notebook installation
        • Python packages
          • NumPy
          • SciPy
          • Scikit-learn 
          • pandas
          • Matplotlib
        • Mongodb with Python
          • Installing MongoDB
          • PyMongo
        • Setting up the development and testing environment
          • Use case
          • Data
          • Code
    • Summary
  • Chapter 2: Time Series Analysis and Ensemble Modeling
    • What is a time series?
      • Time series analysis
        • Stationarity of a time series models
        • Strictly stationary process
        • Correlation in time series
          • Autocorrelation
          • Partial autocorrelation function
    • Classes of time series models
      • Stochastic time series model
      • Artificial neural network time series model
      •  Support vector time series models
      • Time series components
        • Systematic models
        • Non-systematic models
    • Time series decomposition
      • Level 
      • Trend 
      • Seasonality 
      • Noise 
    • Use cases for time series
      • Signal processing
      • Stock market predictions
      • Weather forecasting
      • Reconnaissance detection
    • Time series analysis in cybersecurity
    • Time series trends and seasonal spikes
      • Detecting distributed denial of series with time series
      • Dealing with the time element in time series
      • Tackling the use case
      • Importing packages
        • Importing data in pandas
        • Data cleansing and transformation
      • Feature computation
    • Predicting DDoS attacks
      • ARMA
      • ARIMA
      • ARFIMA
    • Ensemble learning methods
      • Types of ensembling
        • Averaging
        • Majority vote
        • Weighted average
      • Types of ensemble algorithm
        • Bagging
        • Boosting
        • Stacking
        • Bayesian parameter averaging
        • Bayesian model combination
        • Bucket of models
      • Cybersecurity with ensemble techniques
    • Voting ensemble method to detect cyber attacks
    • Summary
  • Chapter 3: Segregating Legitimate and Lousy URLs
    • Introduction to the types of abnormalities in URLs
      • URL blacklisting
        • Drive-by download URLs
        • Command and control URLs
        • Phishing URLs
    • Using heuristics to detect malicious pages
      • Data for the analysis
      • Feature extraction
        • Lexical features
      • Web-content-based features
      • Host-based features
      • Site-popularity features
    • Using machine learning to detect malicious URLs 
    • Logistic regression to detect malicious URLs
      • Dataset
      • Model
        • TF-IDF
    • SVM to detect malicious URLs
    • Multiclass classification for URL classification
      • One-versus-rest
    • Summary
  • Chapter 4: Knocking Down CAPTCHAs
    • Characteristics of CAPTCHA
    • Using artificial intelligence to crack CAPTCHA
      • Types of CAPTCHA
      • reCAPTCHA
        • No CAPTCHA reCAPTCHA
      • Breaking a CAPTCHA
      • Solving CAPTCHAs with a neural network
        • Dataset 
        • Packages
        • Theory of CNN
        • Model
      • Code
        • Training the model
        • Testing the model 
    • Summary
  • Chapter 5: Using Data Science to Catch Email Fraud and Spam
    • Email spoofing 
      • Bogus offers
      • Requests for help
      • Types of spam emails
        • Deceptive emails
        • CEO fraud
        • Pharming 
        • Dropbox phishing
        • Google Docs phishing
    • Spam detection
      • Types of mail servers 
      • Data collection from mail servers
      • Using the Naive Bayes theorem to detect spam
      • Laplace smoothing
      • Featurization techniques that convert text-based emails into numeric values
        • Log-space
        • TF-IDF
        • N-grams
        • Tokenization
      • Logistic regression spam filters
        • Logistic regression
        • Dataset
        • Python
        • Results
    • Summary
  • Chapter 6: Efficient Network Anomaly Detection Using k-means
    • Stages of a network attack
      • Phase 1 – Reconnaissance 
      • Phase 2 – Initial compromise 
      • Phase 3 – Command and control 
      • Phase 4 – Lateral movement
      • Phase 5 – Target attainment 
      • Phase 6 – Ex-filtration, corruption, and disruption 
    • Dealing with lateral movement in networks
    • Using Windows event logs to detect network anomalies
      • Logon/Logoff events 
      • Account logon events
      • Object access events
      • Account management events
        • Active directory events
    • Ingesting active directory data
    • Data parsing
    • Modeling
    • Detecting anomalies in a network with k-means
      • Network intrusion data
        • Coding the network intrusion attack
        • Model evaluation 
          • Sum of squared errors
        • Choosing k for k-means
        • Normalizing features
        • Manual verification
    • Summary
  • Chapter 7: Decision Tree and Context-Based Malicious Event Detection
    • Adware
    • Bots
    • Bugs
    • Ransomware
    • Rootkit
    • Spyware
    • Trojan horses
    • Viruses
    • Worms
    • Malicious data injection within databases
    • Malicious injections in wireless sensors
    • Use case
      • The dataset
      • Importing packages 
      • Features of the data
      • Model
        • Decision tree 
        • Types of decision trees
          • Categorical variable decision tree
          • Continuous variable decision tree
        • Gini coeffiecient
        • Random forest
        • Anomaly detection
          • Isolation forest
          • Supervised and outlier detection with Knowledge Discovery Databases (KDD)
    • Revisiting malicious URL detection with decision trees
    • Summary
  • Chapter 8: Catching Impersonators and Hackers Red Handed
    • Understanding impersonation
    • Different types of impersonation fraud 
      • Impersonators gathering information
      • How an impersonation attack is constructed
        • Using data science to detect domains that are impersonations
    • Levenshtein distance
      • Finding domain similarity between malicious URLs
      • Authorship attribution
        • AA detection for tweets
      • Difference between test and validation datasets
        • Sklearn pipeline
      • Naive Bayes classifier for multinomial models
      • Identifying impersonation as a means of intrusion detection 
    • Summary
  • Chapter 9: Changing the Game with TensorFlow
    • Introduction to TensorFlow
    • Installation of TensorFlow
    • TensorFlow for Windows users
    • Hello world in TensorFlow
    • Importing the MNIST dataset
    • Computation graphs
      • What is a computation graph?
    • Tensor processing unit
    • Using TensorFlow for intrusion detection
    • Summary
  • Chapter 10: Financial Fraud and How Deep Learning Can Mitigate It
    • Machine learning to detect financial fraud
      • Imbalanced data
      • Handling imbalanced datasets
        • Random under-sampling
        • Random oversampling
        • Cluster-based oversampling
        • Synthetic minority oversampling technique
        • Modified synthetic minority oversampling technique
      • Detecting credit card fraud
        • Logistic regression
        • Loading the dataset
        • Approach
    • Logistic regression classifier – under-sampled data
      • Tuning hyperparameters 
        • Detailed classification reports
        • Predictions on test sets and plotting a confusion matrix
      • Logistic regression classifier – skewed data
      • Investigating precision-recall curve and area
    • Deep learning time
      • Adam gradient optimizer
    • Summary
  • Chapter 11: Case Studies
    • Introduction to our password dataset
      • Text feature extraction
      • Feature extraction with scikit-learn
      • Using the cosine similarity to quantify bad passwords
      • Putting it all together
    • Summary
  • Other Books You May Enjoy
  • Index

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