AI-AIDED CYBERSECURITY COURSE

Dive into what this course is all about. Meet your instructor, get a sneak peek at what’s ahead, and ask any burning questions.

• Instructor introduction
• Course structure & assignments
• Q&A

What is AI? Dive into its key types, machine learning and deep learning, and core cybersecurity concepts such as threats, vulnerabilities, attacks, and defenses. You will also explore the importance of AI in cybersecurity with real-world examples.

• What is AI? 
• Core cybersecurity concepts
• Convergence of AI and cybersecurity
• AI in cybersecurity: Real-world examples

Dive into the basics of machine learning. Explore supervised and unsupervised learning, ML algorithms, such as linear regression, logistical regression, and decision trees, and the training, validation, and testing process.

• Supervised vs. unsupervised learning
• Key ML algorithms
• Training, validation, and testing ML models
• Python libraries

Assignment #1: Set up a notebook on the Jupyter Notebook server. 

Discover how to implement the Sci-kit-learn workflow, apply data preprocessing techniques, and utilize these techniques to train and evaluate a simple classification model.

• Demo: Introduction to processing data
• Basic Sci-kit-learn workflow
• Common data preprocessing techniques
• Lab 1: Apply these techniques to a simple classification problem using the Iris dataset
• Capstone project rundown

Assignment #2: In your capstone project notebook, retrieve the data file and follow the steps to preprocess and clean it.

Learn how AI enhances threat detection, explore the differences between anomaly detection, IDS, and malware detection, and dive into a real-world AI-driven threat detection system. 

• Anomaly detection
• Intrusion detection systems (IDS)
• Malware detection
• Case Study: Examining a real-world AI-driven threat detection system

Class 5 will teach you to use Sci-kit and a malware data set to identify outliers and understand how outliers can relate to anomalies and can identify attacks. You will also learn to apply feature engineering techniques to malware data sets and prepare to train and evaluate supervised learning models for malware detection.

• Outliers and anomaly detection
• Common anomaly detection strategies
• Feature engineering for malware data.
• Lab 2: Prepare to train and evaluate classification models for malware detection using Sci-kit-learn

Assignment #3: Set up your Jupyter Notebook and import the provided malware data into a data frame. 

Explore the role of AI in vulnerability assessment, differentiate between vulnerability scanning and penetration testing, and analyze how AI prioritizes and predicts vulnerabilities.

• Vulnerability scanning & penetration testing
• Prioritizing and predicting vulnerabilities
• Automated vulnerability discovery
• Case Study: AI-powered penetration testing tools

Delve into static and dynamic malware analysis and learn to apply feature extraction techniques to malware samples. You will also get hands-on experience training and evaluating classification models for malware identification in a practical lab.

• Static and dynamic malware analysis
• Feature extraction from malware samples
• Lab 3: Build and evaluate classification models using Sci-kit-learn to identify malware families

Assignment #4: Visualize features using seaborn in Jupyter, identify important features, and relate labeled data to outliers.

Learn how AI automates incident response workflows, AI-driven threat intelligence and attack containment, and the role of AI in a Security Operations Center (SOC).

• Automating incident response workflows
• AI-driven threat intelligence and correlation
• Using AI to contain and remediate attacks
• Case Study: AI in a Security Operations Center (SOC)

In this class, you’ll learn types of user behavior and AI techniques to detect insider threats and account takeovers. You will also deep-dive into a real-world case study on the implementation of User Behavior Analytics (UBA) in an organization .

• Normal vs. anomalous user behavior
• Insider threat detection 
• Account takeover detection
• Case Study: Implementing UBA in Financial Services organizations

Dive into anomaly detection in cybersecurity, unsupervised anomaly detection algorithms such as K-Means, Isolation Forest, and On-Class SVM, and use Sci-kit-learn to implement these algorithms for anomaly detection tasks in a hands-on lab.

• Anomaly detection and its applications 
• Unsupervised anomaly detection algorithms
• Lab 4: Apply these algorithms to a cybersecurity-related anomaly detection task using Sci-kit-learn

Assignment #5: Train and evaluate your model using Sci-Kit.

Class 11 will teach you network traffic for anomaly detection, AI’s role in DDoS attack mitigation and firewall optimization, and AI-driven network segmentation in coin-mining.

• Network traffic analysis & anomaly detection
• DDoS attack mitigation 
• Firewall optimization
• Case Study: AI-driven network segmentation

Explore the basics of Natural Language Processing (NLP) for security purposes, text preprocessing techniques, and sentiment analysis and topic modeling for security-related text analysis.

• NLP for security basics
• Text preprocessing techniques
• Lab 5: Apply sentiment analysis to security-related text
• Topic modeling for security data

Assignment #6: Validate your model and import test data into a data frame.

In this class, you will cover attacks against AI systems, methods for defending against adversarial attacks, and the ongoing arms race between AI attackers and defenders in cybersecurity.

• Adversarial attacks against AI systems 
• Defending against adversarial attacks
• Arms race between AI attackers and defenders

Explore the nature and impact of adversarial attacks, learn to generate basic adversarial examples, and practice implementing defenses to protect machine learning models from such attacks in a practical lab.

• Adversarial attacks and their impact
• Techniques for generating adversarial examples
• Lab 6: Explore defenses against adversarial attacks

Class 15 will teach you to identify biases in AI algorithms, analyze privacy concerns and data usage in cybersecurity, discuss the potential for AI misuse, and explain the principles for responsible AI development and deployment.

• Bias in AI algorithms and its impact
• Privacy concerns & data usage
• Potential for AI misuse
• Responsible AI development and deployment

Delve into emerging trends and research areas in AI and cybersecurity, the impact of quantum computing on both fields, AI’s role in shaping cybersecurity’s future, and career paths in AI and cybersecurity.

• Emerging trends & research areas
• Quantum computing
• AI in shaping cybersecurity’s future
• Career paths

Capstone project: Malware Detection with Machine Learning
Apply the knowledge from the course to a Jupyter Notebook project that allows you to detect malware. Train your detector with samples of real malware.