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Explain the management technologies in cyber operational planning.
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Solution

  • Knowledge management is a structured and systematic process to extract learning from past activities to make better future decisions. Knowledge management processes deliver measurable benefits.

  • We will focus in this section on examples of using machine learning (ML) techniques in cyber operations especially for cyber analysts.

  • A ML approach usually consists of two phases: training and testing. Often, the following steps are performed (Buczak and Guven 2016):

    • Identify class attributes (features) and classes (class labels) from training data.

    • Identify a subset of the attributes necessary for classification (i.e., dimensionality reduction, feature selection, etc.).

    • Divide data into training and testing; learn the model using the training data.

    • Use the trained model to classify the unknown data.

  • Some of the popular algorithms: ANN, SVM, GA, KNN, Random forest, HMM, etc.

  • Readers are expected to learn some of the popular data mining tools such as

    • Python: One of the most popular programming/scripting languages for cyber security and data analytics. Several open source IDEs can be used to write and execute Python code such as PyCharm and Anaconda.

    • Some of the popular Python libraries to learn in this scope: Scikit learn and TensorFlow.

    • R: Of the popular GUI IDEs based on R is R-studio. Users can write scripts which utilize rich libraries built and available in R.

    • Weka: A simple but popular open source GUI-based data mining tool. Libraries also exist to export Weka to Java.

    • Knime , written in Java, Knime is a free and open source data analytics’ reporting and integration platform.

    • RapidMiner.

    • H2O.

    • MATLAB/Octave.

    • Julia.

    • Several tools and libraries in Java.

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