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Machine Learning to Detect Phishing Websites

  • Course Code: Data Science - Machine Learning to Detect Phishing Websites
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 1 Days Audience: This course is geared for those who wants to develop a machine learning model for predicting whether or not an email that contains a link to a website is a phishing website or not.

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level Use Machine Learning to Detect Phishing Websites skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to develop a machine learning model for predicting whether or not an email that contains a link to a website is a phishing website or not. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In this course, you will be filling in the role of a data scientist employed by an organization’s cybersecurity manager. Lately, the employees of the organization are receiving a lot of emails containing links to phishing websites. Your task will be to develop a machine learning model for predicting whether or not an email that contains a link to a website is a phishing website or not. 

Phishing attacks are considered to be one of the most common types of online security threats, and are capable of breaking into an organization’s online security so as to extract confidential information like user passwords, financial information, and so on. The Internet Crime Report 2018 presents the effects of phishing websites. 

Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore: 

  • Load and understand a tabular dataset. As a data scientist, you should be comfortable working with tabular data. 
  • Query the dataset for deriving interesting reports. 
  • Clean the dataset accordingly so that it is well-suited for a machine learning model. 
  • Build and train machine learning models, like Logistic Regression and Neural Networks. 
  • Perform hyperparameter tuning techniques, like random search. 
  • Provide a summary of the performance of the machine learning models. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Loading and understanding the Phishing Websites dataset 
  • Asking the right questions of the data to understand it better and preparing reports 
  • Cleaning data 
  • Creating a Logistic Regression classifier as a baseline model 
  • Analyzing the results and using random searches to find the optimal hyperparameters of the baseline model 
  • Summarizing the results of your findings 

Audience & Pre-Requisites 

This course is designed for developers interested in data science and for beginner data scientists 

Pre-Requisites:  Students should have familiar with  

TOOLS 

  • Basics of Python and its utility functions 
  • Basics of pandas 
  • Basics of NumPy 
  • Basics of scikit-learn 

TECHNIQUES 

Basics of data science 

Course Agenda / Topics 

1. Loading and Understanding the Phishing Websites Dataset 

  • Knowing the Dataset 
  • A Quick Tour of Pandas 
  • Submit Your Work 

2. Further Data Investigation and Preparing Investigation Reports 

  • Getting Useful Information from the Dataset 
  • Submit Your Work 

3. Cleaning the Class Labels and Inspecting for Missing Values 

  • Cleaning the Class Labels and Inspecting for Missing Values 
  • Submit Your Work 

4. Training a Logistic Regression Model 

  • Training a Logistic Regression Model 
  • A Quick Primer on Logistic Regression 
  • A Brief Take on Scikit-Learn 
  • A Continuous Approach to Splitting Points: Logistic Regression 
  • Submit Your Work 
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