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DV-SEC313.AJ1
Artificial Intelligence for Cybersecurity
Intermediate Self-paced · 1 year access
27 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
11 Interactive Lessons · 59 topics01 Preface 2 topics +
- Who this course is for
- What this course covers
02 Introduction to AI for Cybersecurity Professionals 7 topics · 3 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
03 Setting Up Your AI for Cybersecurity Arsenal 6 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
04 Ham or Spam? Detecting Email Cybersecurity Threats with AI 6 topics · 5 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
05 Malware Threat Detection 6 topics · 3 LiveLab +
- 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
3 LiveLab in this lesson — see the labs panel →
06 Network Anomaly Detection with AI 5 topics · 2 LiveLab +
- Network anomaly detection techniques
- How to classify network attacks
- Detecting botnet topology
- Different ML algorithms for botnet detection
- Summary
2 LiveLab in this lesson — see the labs panel →
07 Securing User Authentication 5 topics · 3 LiveLab +
- Authentication abuse prevention
- Account reputation scoring
- User authentication with keystroke recognition
- Biometric authentication with facial recognition
- Summary
3 LiveLab in this lesson — see the labs panel →
08 Fraud Prevention with Cloud AI Solutions 6 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
09 GANs - Attacks and Defenses 6 topics +
- 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 5 topics · 5 LiveLab +
- 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
5 LiveLab in this lesson — see the labs panel →
11 Assessing your AI Arsenal 5 topics · 2 LiveLab +
- Evading ML detectors
- Challenging ML anomaly detection
- Testing for data and model quality
- Ensuring security and reliability
- Summary
2 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
27 LiveLabs- Creating a Linear Regression Model
- Creating a Clustering Model
- Using Neural Networks for Spam Filtering
- Performing Matrix Operations
- Using a Linear Regression Model for Prediction
- 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
- 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
- Detecting Botnet
- Performing Gaussian Anomaly Detection
- Detecting Anomaly Using Keystrokes
- Creating an Image Classification Model
- Understanding Covariance Matrix
- Performing Oversampling and Undersampling
- Comparing Different Models for Detecting Credit Card Frauds
- 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
- Handling Missing Values in a Dataset
- Performing Hyperparameter Optimization
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
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Prepare for Artificial Intelligence for Cybersecurity
One-time payment. Full access for 1 year. Start with a free trial if you want to look around first.
- 1 year of full access
- 27 LiveLab included
- Certificate of completion
No credit card required