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AI Risk and Threats

(WU-CSC415.AJ1)
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1

Preface

  • Who this course is for
  • What this course covers
2

Introduction to AI for Cybersecurity Professionals

  • Applying AI in cybersecurity
  • Evolution in AI: from expert systems to data mining
  • Types of machine learning
  • Algorithm training and optimization
  • Getting to know Python's libraries
  • AI in the context of cybersecurity
  • Summary
3

Setting Up Your AI for Cybersecurity Arsenal

  • Getting to know Python for AI and cybersecurity
  • Python libraries for cybersecurity
  • Enter Anaconda – the data scientist's environment of choice
  • Playing with Jupyter Notebooks
  • Installing DL libraries
  • Summary
4

Ham or Spam? Detecting Email Cybersecurity Threats with AI

  • Detecting spam with Perceptrons
  • Spam detection with SVMs
  • Phishing detection with logistic regression and decision trees
  • Spam detection with Naive Bayes
  • NLP to the rescue
  • Summary
5

Malware Threat Detection

  • Malware analysis at a glance
  • Telling different malware families apart
  • Decision tree malware detectors
  • Detecting metamorphic malware with HMMs
  • Advanced malware detection with deep learning
  • Summary
6

Network Anomaly Detection with AI

  • Network anomaly detection techniques
  • How to classify network attacks
  • Detecting botnet topology
  • Different ML algorithms for botnet detection
  • Summary
7

Securing User Authentication

  • Authentication abuse prevention
  • Account reputation scoring
  • User authentication with keystroke recognition
  • Biometric authentication with facial recognition
  • Summary
8

Fraud Prevention with Cloud AI Solutions

  • Introducing fraud detection algorithms
  • Predictive analytics for credit card fraud detection
  • Getting to know IBM Watson Cloud solutions
  • Importing sample data and running Jupyter Notebook in the cloud
  • Evaluating the quality of our predictions
  • Summary
9

GANs - Attacks and Defenses

  • GANs in a nutshell
  • GAN Python tools and libraries
  • Network attack via model substitution
  • IDS evasion via GAN
  • Facial recognition attacks with GAN
  • Summary
10

Evaluating Algorithms

  • Best practices of feature engineering
  • Evaluating a detector's performance with ROC
  • How to split data into training and test sets
  • Using cross validation for algorithms
  • Summary
11

Assessing your AI Arsenal

  • Evading ML detectors
  • Challenging ML anomaly detection
  • Testing for data and model quality
  • Ensuring security and reliability
  • Summary

1

Introduction to AI for Cybersecurity Professionals

  • Creating a Linear Regression Model
  • Creating a Clustering Model
  • Using Neural Networks for Spam Filtering
2

Setting Up Your AI for Cybersecurity Arsenal

  • Performing Matrix Operations
  • Using a Linear Regression Model for Prediction
3

Ham or Spam? Detecting Email Cybersecurity Threats with AI

  • Creating a Perceptron-based Spam Filter
  • Creating an SVM Spam Filter
  • Creating a Phishing Detector with Logistic Regression
  • Creating a Phishing Detector with Decision Trees
  • Creating a Spam Detector with NLTK
4

Malware Threat Detection

  • Using the k-Means Clustering Algorithm for Malware Detection
  • Creating a Decision Tree and a Random Forest Malware Classifier
  • Detecting Malware using an HMM Model
5

Network Anomaly Detection with AI

  • Detecting Botnet
  • Performing Gaussian Anomaly Detection
6

Securing User Authentication

  • Detecting Anomaly Using Keystrokes
  • Creating an Image Classification Model
  • Understanding Covariance Matrix
7

Fraud Prevention with Cloud AI Solutions

  • Performing Oversampling and Undersampling
  • Comparing Different Models for Detecting Credit Card Frauds
8

Evaluating Algorithms

  • Performing Feature Normalization
  • Dealing with Categorical Data
  • Using Different Measures to Evaluate Algorithms
  • Creating a Learning Curve to Measure Performance of an Algorithm
  • Performing K-Folds Cross Validation
9

Assessing your AI Arsenal

  • Handling Missing Values in a Dataset
  • Performing Hyperparameter Optimization

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