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Machine Learning for Cybersecurity

  • Course Code: CyberSecurity - Machine Learning for Cybersecurity
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to get into the world of smart data security using machine learning algorithms and Python libraries

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to get into the world of smart data security using machine learning algorithms and Python libraries 
  • 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. 

Cyber threats today are one of the costliest losses that an organization can face. In this course, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The course begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems 

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

  • Learn machine learning algorithms and cybersecurity fundamentals 
  • Automate your daily workflow by applying use cases to many facets of security 
  • Implement smart machine learning solutions to detect various cybersecurity problems 

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

  • Use machine learning algorithms with complex datasets to implement cybersecurity concepts 
  • Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems 
  • Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA 
  • Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes 
  • Use TensorFlow in the cybersecurity domain and implement real-world examples 
  • Learn how machine learning and Python can be used in complex cyber issues 

Audience & Pre-Requisites 

This course is geared for attendees wants to get into the world of smart data security using machine learning algorithms and Python libraries 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills, Microsoft azure and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Basics of Machine Learning in Cybersecurity 
  • What is machine learning? 
  1. Time Series Analysis and Ensemble Modeling 
  • What is a time series? 
  • Classes of time series models 
  • Time series decomposition 
  • Use cases for time series 
  • Time series analysis in cybersecurity 
  • Time series trends and seasonal spikes 
  • Predicting DDoS attacks 
  • Ensemble learning methods 
  • Voting ensemble method to detect cyber attacks 
  1. Segregating Legitimate and Lousy URLs 
  • Introduction to the types of abnormalities in URLs 
  • Using heuristics to detect malicious pages 
  • Using machine learning to detect malicious URLs  
  • Logistic regression to detect malicious URLs 
  • SVM to detect malicious URLs 
  • Multiclass classification for URL classification 
  1. Knocking Down CAPTCHAs 
  • Characteristics of CAPTCHA 
  • Using artificial intelligence to crack CAPTCHA 
  1. Using Data Science to Catch Email Fraud and Spam 
  • Email spoofing  
  • Spam detection 
  1. Efficient Network Anomaly Detection Using k-means 
  • Stages of a network attack 
  • Dealing with lateral movement in networks 
  • Using Windows event logs to detect network anomalies 
  • Ingesting active directory data 
  • Data parsing 
  • Modeling 
  • Detecting anomalies in a network with k-means 
  1. Decision Tree and Context-Based Malicious Event Detection 
  • Adware 
  • Bots 
  • Bugs 
  • Ransomware 
  • Rootkit 
  • Spyware 
  • Trojan horses 
  • Viruses 
  • Worms 
  • Malicious data injection within databases 
  • Malicious injections in wireless sensors 
  • Use case 
  • Revisiting malicious URL detection with decision trees 
  1. Catching Impersonators and Hackers Red Handed 
  • Understanding impersonation 
  • Different types of impersonation fraud  
  • Levenshtein distance 
  1. Changing the Game with TensorFlow 
  • Introduction to TensorFlow 
  • Installation of TensorFlow 
  • TensorFlow for Windows users 
  • Hello world in TensorFlow 
  • Importing the MNIST dataset 
  • Computation graphs 
  • Tensor processing unit 
  • Using TensorFlow for intrusion detection 
  1. Financial Fraud and How Deep Learning Can Mitigate It 
  • Financial Fraud and How Deep Learning Can Mitigate It 
  • Machine learning to detect financial fraud 
  • Logistic regression classifier – under-sampled data 
  • Deep learning time 
  1. Case Studies 
  • Introduction to our password dataset 
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