Predictive Analytics: Identifying Network Attacks

Machine Learning    |    Intermediate
  • 18 videos | 2h 3m 34s
  • Includes Assessment
  • Earns a Badge
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In cybersecurity, it's important to determine whether a user interaction or action represents an attack, followed by discerning the specific attack type and signature. Machine learning (ML) models and managed ML solutions like Microsoft Azure Machine Learning can help with this. In this course, learn how to create an Azure Machine Learning workspace, read in data, and categorize all of the different types of attacks. Next, discover how to train a random forest classification model using the scikit-learn library and test it on the in-sample validation data. Finally, practice performing multiclass classification to identify the specific type of attack. Upon completion, you'll be able to detect intrusions using data, train and evaluate classification models, and perform multiclass classification.


  • Discover the key concepts covered in this course
    Create an azure machine learning workspace
    Read in a network attack detection dataset to pandas
    Create a column for the cyberattack type
    View the training and testing datasets for identifying cyberattacks
    Visualize network cyberattack data and view the unique values
    Predict cyberattacks using heatmap and pie charts
    Set up cyberattack data for machine learning (ml)
    Perform one-hot encoding on cyberattack data and view the results
  • View the performance of an overfit cyberattack prediction model
    Predict cyberattacks by performing feature selection on a model
    Use a chi-square test to perform feature selection
    Set up data for cyberattack type classification
    Train the attack type classification model and view the results on the in-sample validation data
    Test the performance of an overfit attack classification model and select some features from the data
    Perform feature selection for the cyberattack classification model
    Select features for the model using the chi-square test
    Summarize the key concepts covered in this course


  • 2m 3s
  • 4m 30s
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    3.  Importing Data on Network Cyberattacks
    7m 25s
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    4.  Creating a Data Column for Cyberattack Type
    7m 4s
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    5.  Viewing and Testing Network Cyberattack Data
    6m 12s
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    6.  Visualizing Network Cyberattack Data
    7m 54s
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    7.  Predicting Cyberattacks
    9m 6s
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    8.  Setting Up Cyberattack Data for Machine Learning
    5m 18s
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    9.  Performing One-hot Encoding on Cyberattack Data
    7m 45s
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    10.  Viewing Cyberattack Prediction Model Performance
    7m 35s
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    11.  Predicting Cyberattacks with Feature Selection
    7m 34s
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    12.  Using a Chi-square Test for Feature Selection
    7m 41s
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    13.  Setting Up Data for Cyberattack Type Classification
    7m 40s
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    14.  Training the Cyberattack Type Classification Model
    6m 2s
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    15.  Testing an Overfit Cyberattack Classification Model
    7m 26s
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    16.  Performing Classification Model Feature Selection
    8m 36s
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    17.  Selecting Features Using the Chi-square Test
    9m 41s
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    18.  Course Summary
    4m 3s


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