Applied Data Science and AI/Machine Learning for Cybersecurity Professionals

This course provides students with a crash-course introduction to practical data science, statistics, probability, and machine learning. The course is structured as a series of short discussions with extensive hands-on labs that help students to develop useful intuitive understandings of how these concepts relate and can be used to solve real-world problems. If you’ve never done anything with data science or machine learning but want to use these techniques, this is definitely the course for you!

Course Key  Learnings: 

• 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

Business Takeaways

This course will help your organization:

• Generate useful visualization dashboards
• Solve problems with Neural networks
• Improve the effectiveness, efficiency, and success of cybersecurity initiatives
• Build custom machine learning solutions for your organization’s specific needs

Course Content:

Module1:  Data Acquisition, Cleaning, and Manipulation

Overview

This section introduces some of the terminology in the data science and machine learning fields, in addition to introducing a number of the technologies that are used as data sources. Since the first step in any data science or machine learning project is to acquire data, the balance of the day is focused on hands-on exercises to prepare the student for these tasks.

Exercise

• Python Refresher
• Accessing, Manipulating, and Retrieving SQL data
• Accessing, Manipulating, and Retrieving NoSQL data: MongoDB
• Webscraping for data acquisition

Topics

• Data Science
• Python
• SQL
• NoSQL
• Webscraping

Module2: Data Exploration and Statistics

Overview

This section begins with the fundamentals of statistics that matter for data science and machine learning. Following this introduction and hands-on exercises that provide practical uses for these techniques against real-world data, the course transitions to probability theory.

Exercises

• Statistics Fundamentals: Medians and Means
• Statistics Fundamentals: Variance, Deviations, and Robust Measures
• Applications of Statistics to Data Identification
• Probability, Beyes, and Phishing
• Threat Hunting through Signals Analysis

Topics

• Statistics
• Robust Measures
• Probability
• Bayes Theorem and Inference
• Fourier Series and Related Derivations

Module3:  Essentials of Machine Learning

Overview

The section begins with various clustering approaches and unsupervised machine learning. The exploration begins with Support Vector Classifiers, kernel functions, and Support Vector Machines. Following this discussion and exercises, we continue the clustering theme by considering the K-Means and KNN approaches. After working through examples in just two or three dimensions, we turn our attention to methods for determining the ideal number of clusters. With this done, we finally explore high-dimensional applications and dimensionality reduction through Primary Component Analysis. The DBSCAN algorithm is covered in some depth, with application made to threat hunting and efficient SOC analysis of large scale data.

Exercises

• K-Means / KNN
• Elbow Functions and PCA
• DNSCAN for Clustering
• Support Vector Classifiers
• Support Vector Machines
• Decision Trees
• Random Forests

Topics

• Support Vector Classifiers
• Support Vector Machines
• Kernel Functions
• Primary Component Analysis
• DBSCAN
• K-Means
• KNN
• Elbow Functions
• Decision Trees
• Random Forests
• Anomaly Detection

Module4: Essentials of Machine Learning

Overview

The entire focus of this section is on the theory, development, and use of supervised learning approaches in the field of information security. Building on the mathematics and statistics covered in section 2, this section begins with linear regressions and ends with the application of deep learning neural networks to multi-class classification problems involving real-time network data.

Exercises

• Polyfit Regressions
• Hello, World! Sentiment Analysis
• Ham vs. Spam via Deep Learning
• Identifying Protocols
• Protocol Anomaly Detection

Topics

• Regression and fitting
• Loss and Error functions
• Vectors, Matrices, and Tensors
• Fundamentals of the Perceptron
• Dense Networks

Module5:  Essentials of Machine Learning

Overview

This section of the course is dedicated to expanding students knowledge of deep learning solutions. The first half of the section is focused entirely on convolutional networks (CNNs). The class explores the application of CNNs to text classification problems, but also to predictive identification of zero-day malware.

Exercises

• 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

Topics

• Convolutional Neural Networks
• Embedding Layers
• Applying CNNs to text problems
• Autoencoders
• Reconstruction loss measurements
• Creating ensemble autoencoders

Module6: Essentials of Machine Learning

Overview

The final section of this course continues discussing Convolutional Neural Networks and the application of CNNs and fully connected networks for solving regression problems. The major focus of this section is on the creation of a deep neural network using TensorFlows functional pattern for both testing the quality of and solving CAPTCHAs. Whether you are on a red, blue, or purple team, you will learn how to think through and use machine learning to solve what amounts to a computer vision problem and to solve it at greater than 95% accuracy! After this, we explore a different way to think about the problem that results in even greater accuracy with far less training time.

The final portion of the section investigates Genetic Algorithms as they can be applied to machine learning problems.

Exercises

• Solving CAPTCHAs: POC
• Solving CAPTCHAs: Functional API
• Solving CAPTCHAs: Split model
• Genetic Algorithms

Topics

• Convolutional Neural Networks
• Functional definition of Neural Networks
• Deep Learning Networks with Multiple Outputs
• Thinking about Machine Learning Problems
• Genetic Algorithms

International Student Fee: 950 US$

Job Interview Preparation  (Soft Skills Questions & Answers)

• Tough Open-Ended Job Interview Questions
• What to Wear for Best Job Interview Attire
• Job Interview Question- What are You Passionate About?
• How to Prepare for a Job Promotion Interview

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Internships, Freelance and Full-Time Work opportunities

• Join Internships and Referral Program (click for details)
• Work as Freelancer or Full-Time Employee (click for details)

Flexible Class Options

• Week End Classes For Professionals  SAT | SUN
• Corporate Group Trainings Available
• Online Classes – Live Virtual Class (L.V.C), Online Training

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