Security Analytics

• • practical data science, statistics, probability, and machine learning

  • Data Science, Artificial Intelligence, and Machine Learning

  • Data acquisition from SQL, NoSQL document stores, web scraping, and other common sources

  • Data exploration and visualization

  • Descriptive statistics

  • Inferential statistics and probability

  • Bayesian inference

  • Unsupervised learning and clustering

  • Deep learning neural networks

  • Autoencoders

  • Loss functions

  • Convolutional networks

  • Embedding layers

  • Apply statistical models to real world problems in meaningful ways

  • Generate visualizations of your data

  • Perform mathematics-based threat hunting on your network

  • Understand and apply unsupervised learning/clustering methods

  • Build Deep Learning Neural Networks

  • Build and understand Convolutional Neural Networks

  • Understand and build Genetic Search Algorithms

• • Fundamentals of python

  • • Lists

    • Arrays

    • Tuples

    • Dictionaries

    • NumPy variants

    • Connecting sql

    • Connecting mongo dB

    • Reading and writing csv

    • Web scraping

  • Data Acquisition

  • Data Cleaning

  • Data Manipulation

  • Statistics

  • • Statistics Fundamentals: Medians and Means

    • Statistics Fundamentals: Variance, Deviations, and Robust Measures

    • Applications of Statistics to Data Identification

    • Probability, Bayes, and Phishing

    • Threat Hunting through Signals Analysis

    • Bayesian theorem and inference

    • Linear algebra

    • Signal analysis

    • • Fourier series

      • Fast Fourier Transformation

      • Discreate Fourier Transformation

  • Fundamentals of Machine Learning

  • • Clustering

    • Unsupervised learning

    • Support vector machines

    • Kernel functions

    • Support vector classifiers

    • K-means

    • KNN

    • Elbow Functions and PCA

    • High dimensions

    • Dimensionality reduction

    • Primary component analysis

    • DCBSCAN

    • Decision Trees

    • Random Forests

    • Anomaly detection

    • Supervised learning

    • • Linear regression

      • Deep learning neural networks

      • Multi class classification

      • Predictive models

      • Forecasting and trend analysis for anomaly detection

      • NN and dense networks for phishing detection

      • Network protocol classification

      • Polyfit Regressions

      • Hello, World! Sentiment Analysis

      • Ham vs. Spam via Deep Learning

      • Identifying Protocols

      • Protocol Anomaly Detection

      • Regression and fitting

      • Loss and Error functions

      • Vectors, Matrices, and Tensors

      • Fundamentals of the Perceptron

      • Dense Networks

    • CNN

    • • Predictive identification zero day malware

      • Auto encoders

      • Latent representation

      • Reconstruction loss function work

      • Predictive Malware Identification - Finding Zero Days

      • Ham vs. Spam, CNN Style

      • Multi-class text classification via CNNs

      • Log Anomaly Detection using Autoencoders

      • Real-time Network Anomalies

      • Convolutional Neural Networks

      • Embedding Layers

      • Applying CNNs to text problems

      • Autoencoders

      • Reconstruction loss measurements

      • Creating ensemble autoencoders

      • CNNs and fully connected networks for solving regression problems

      • deep neural network using TensorFlow

      • Solving CAPTCHAs: POC

      • Solving CAPTCHAs: Functional API

      • Solving CAPTCHAs: Split model

      • Genetic Algorithms

      • Convolutional Neural Networks

      • Functional definition of Neural Networks

      • Deep Learning Networks with Multiple Outputs

      • Thinking about Machine Learning Problems

      • Genetic Algorithms