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Home » Technology » AI / Machine Learning » Machine Learning for Cybersecurity

Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    MLCYSEL21E09

Who should attend & recommended skills:

Those with basic to intermediate IT, MS Azure, ML, & Linux experience entering smart data security using ML algorithms & Python libraries

Who should attend & recommended skills

  • This course is geared for those who want to get into the world of smart data security using machine learning algorithms and Python libraries.
  • Skill-level: Foundation-level Machine Learning for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-2 years’ experience)
  • Microsoft Azure: Basic to Intermediate (1-2 years’ experience)
  • Machine Learning: Basic to Intermediate (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with Azure expert instructor, students will learn about and explore:
  • Machine learning algorithms and cybersecurity fundamentals
  • Automating your daily workflow by applying use cases to many facets of security
  • Implementing smart machine learning solutions to detect various cybersecurity problems
  • Using machine learning algorithms with complex datasets to implement cybersecurity concepts
  • Implementing machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems
  • Speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA

Course breakdown / modules

  • What is machine learning?

  • 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

  • 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

  • Characteristics of CAPTCHA
  • Using artificial intelligence to crack CAPTCHA

  • Email spoofing
  • Spam detection

  • 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

  • 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

  • Understanding impersonation
  • Different types of impersonation fraud
  • Levenshtein distance

  • 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

  • Financial Fraud and How Deep Learning Can Mitigate It
  • Machine learning to detect financial fraud
  • Logistic regression classifier under-sampled data
  • Deep learning time

  • Introduction to our password dataset

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