AI-Powered Cybersecurity: Advanced Training for Modern Threats

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Course Outline

Module 1: Foundations of AI and Cybersecurity

1. Introduction to AI in Cybersecurity:

• Key concepts: AI, ML, and DL in cybersecurity.
• Types of AI systems (rule-based, supervised, unsupervised, and reinforcement learning).
• Differences between traditional cybersecurity and AI-driven approaches.

2. Role of AI in Cybersecurity Domains:

• Threat detection and response.
• Predictive analytics for attack prevention.
• Behavioural analysis and anomaly detection.

3. AI in Network Traffic Analysis:

• Common attacks detectable via AI (DDoS, spoofing, port scans).
• Features extraction for network traffic using packet analysers (e.g., Wireshark).

4. Hands-On Lab:

• Setting up a network traffic dataset.
• Preprocessing the data for training AI models using Python and pandas.

Module 2: Machine Learning for Threat Detection

1. Supervised Learning in Cybersecurity:

• Training classifiers to detect malware, phishing, and fraud.
• Using decision trees, random forests, and support vector machines (SVM).

2. Unsupervised Learning:

• Clustering techniques for anomaly detection.
• Applications in insider threat detection and zero-day attack identification.

3. Data Preparation for ML Models:

• Handling imbalanced datasets (e.g., oversampling with SMOTE).
• Feature selection using mutual information or principal component analysis (PCA).

4. Hands-On Lab:

• Build a supervised ML model for intrusion detection using a public dataset (e.g., KDDCup99 or UNSW-NB15).
• Implement a clustering algorithm (e.g., K-means) for anomaly detection.

Module 3: Advanced Deep Learning Applications in Cybersecurity

1. Deep Learning Fundamentals for Cybersecurity:

• Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.
• How deep learning enhances malware detection and email classification.

2. AI for Endpoint Security and Fraud Detection:

• Endpoint vulnerability detection using DL models.
• Fraud detection techniques with recurrent models.

3. Phishing Email Detection with Natural Language Processing (NLP):

• Using pretrained NLP models (e.g., BERT, GPT) for phishing classification.
• Tokenization and text vectorization for cybersecurity datasets.

4. Hands-On Lab:

• Train and fine-tune a neural network for phishing detection.
• Use a malware dataset to train a CNN for malware classification.

Module 4: Securing AI Systems and Ethical Hacking with AI

1. AI Vulnerabilities and Adversarial Attacks:

• Types of attacks on AI models (e.g., poisoning, evasion).
• Adversarial examples and how attackers exploit AI systems.

2. Techniques to Secure AI Models:

• Defensive distillation and adversarial training.
• Role of explainable AI (XAI) in improving model robustness.

3. Using AI for Penetration Testing:

• AI-driven tools for vulnerability scanning and exploitation (e.g., Metasploit with ML extensions).
• Predicting attack vectors using AI models.

4. Hands-On Lab:

• Simulate adversarial attacks on a pre-trained AI model and implement mitigation strategies.
• Conduct AI-powered penetration testing on a virtual environment.

Module 5: Real-World AI Cybersecurity Solutions and Capstone Project

1. AI Cybersecurity Tools and Frameworks:

• Tools like IBM Watson for Cybersecurity, Darktrace, and Splunk with AI modules.
• Frameworks for building AI models (TensorFlow, PyTorch, Scikit-learn).

2. Emerging Trends in AI Cybersecurity:

• AI in quantum cryptography.
• The rise of generative AI in crafting and detecting cyber threats.

3. Capstone Project:

• Build an end-to-end AI-based cybersecurity solution:
• Dataset collection and preprocessing.
• Model training for anomaly detection.
• Deploy a proof-of-concept (PoC) in a simulated environment.