AI-CYBSEC.AJ1
Artificial Intelligence for Cybersecurity
Learn how to utilize the power of AI for guardrailing your digital assets. Streamline, structure and automate complex tasks.
- Practice in 27 Hands-On Labs — nothing to install
- 11 Interactive Lessons and 59 topics mapped to the official exam objectives
- 237 Practice Test Questions
Intermediate Self-paced · 1 year access
27 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
11Interactive Lessons
59Topics
27LiveLab
237Practice Test Questions
63Flashcards
63Glossary of terms
01 / Skills you'll get
What you will be able to do
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No credit card required
Artificial Intelligence for Cybersecurity is a new-age course that focuses on the use of AI techniques to detect and prevent various cybersecurity threats. The course has been especially designed to fit the needs of both, AI experts wanting to expand their skills to cybersecurity, and other professionals wanting to learn how to use AI for protection against cyber threats. By the end of this hands-on Artificial Intelligence for Cybersecurity training, you’ll have the required skill and knowledge to develop and deploy AI-driven security solutions using popular tools and libraries.
- Strong foundation in AI concepts, including machine learning, deep learning, and neural networks
- Expertise in using Python programming language for AI and cybersecurity, to implement AI models and solutions
- Discover AI techniques to detect threats like spam, malware, and network deviations
- Identify unusual patterns and behaviors in network traffic that may indicate a security breach
- Explore AI-based methods for securing user authentication, including keystroke recognition and biometric authentication
- Using AI to prevent fraud, especially in areas like credit card fraud detection
- Automate spam email detection with machine learning algorithms using AI
- Explore methods for analyzing and classifying different types of malware using AI
- Implementation of generative adversarial networks (GANs) and their use for both offensive and defensive purposes
- Evaluating the performance of AI security models using ROC curves and cross-validation metrics
Course Highlights
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11 Structured Lessons Comprehensive coverage of core course objectives
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27 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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237 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / 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.
03 / FAQs
Questions before you start
What is the use of AI in Cybersecurity? +
Artificial Intelligence (AI) has literally revolutionized the Cybersecurity workflow with advanced threat detection and automated response. By analyzing vast amounts of data in real-time, AI algorithms can identify suspicious patterns and proactively address vulnerabilities. This enables faster automated responses to potential cyber threats, enhancing overall security.
Who should take this AI cybersecurity training? +
This is an excellent course for the following people:
- Cybersecurity professionals wanting to enhance their AI skills
- AI experts wanting to learn how to apply their knowledge to prevent cyber crime
- Students/enthusiasts wanting to upgrade their skill set and seeking a career in cybersecurity or AI
What are the prerequisites for this course? +
This is an intermediary-level course and requires basic basic understanding of cybersecurity concepts, programming skills in Python, familiarity with statistics and linear algebra for understanding ML algorithms, and an analytical aptitude with problem-solving skills.
How does AI detect cyber threats? +
With AI, you can create some powerful tools for detecting and preventing cyber threats in real-time, and the best part is that all of this can be automated. It uses Machine Learning (ML) to learn what normal behavior looks like and then flags suspicious behavior/activities. This is how AI detects unusual patterns or threats.
What job opportunities can be explored after this course? +
With the growing demand for cybersecurity professionals skilled in AI, completing this course will place you in a better position to explore many rewarding career opportunities like Cybersecurity Analyst, AI Engineer (Cybersecurity Focus), Threat Intelligence Analyst, Advanced Security Consultant, Research Scientist (Cybersecurity), and Ethical Hacker.
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Hands-On AI Training for Cybersecurity
Build a rewarding career in cybersecurity with the use of Artificial Intelligence.
- 1 year of full access
- 27 LiveLab included
- Certificate of completion
No credit card required