Cybersecurity Data Science Training Course
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Introduction to Data Science

Introduction to Cyber Security

Python Basics

• Statements and Loops
• Creating Functions
• Data Wrangling using Pandas and NumPy

Exploratory Data Analysis

• Summary Statistics
• Data Visualization
• Missing Value detection
• Outlier detection
• Data Transformation
• Splitting the data Into Training and Test Data

Machine Learning for Cyber Security

• Data Dimensionality Reduction PCA Statistical Intuition and Implementation
• Segregating data using clustering Statistical Intuition and Implementation
• Training an XGBoost classifier
• Analyzing time series using statsmodels Explanation and Implementation using statsmodels
• Anomaly detection with Isolation Forest Explanation and Implementation
• Natural language processing using a hashing vectorizer and Tf-Idf Explanation and Implementation
• Hyperparameter tuning with scikit-optimize Implementation
• Generating text using Markov chains

Email Cybersecurity Threats Detection

• Introduction to detect spam with Perceptrons
• Introduction to Perceptrons
• Introduction to spam filters
• Spam filter in action
• Detecting spam with linear classifiers
• How the Perceptron learns
• A simple Perceptron-based spam filter
• Pros and cons of Perceptrons
• Introduction to Spam detection with SVMs
• SVM spam filter example
• Introduction to Phishing detection with logistic regression and decision trees
• Linear regression for spam detection
• introduction to Logistic regression
• Logistic Regression Implementation
• Introduction to making decisions with trees
• Phishing detection with decision trees
• Spam detection with Naive Bayes
• NLP with Naive Bayes Implementation

Malware Threat Detection

• Introduction to Malware detection
• Malware goes by many names
• Malware analysis tools of the trade
• Static malware analysis
• Dynamic malware analysis
• Hacking the PE file format
• Introduction of Decision tree malware detectors
• Malware detection with decision trees
• Random Forest Malware classifier
• Clustering malware with K-Means
• K-Means steps and its advantages and disadvantages
• Detecting metamorphic malware with HMMs Introductions
• Polymorphic malware detection strategies
• HMM Implementation

Advanced malware threat detection

• Obfuscation Detection Python Implementation
• Obfuscation Detection Python Explanation
• Tracking malware drift Implementation
• Tracking malware drift Explanation

Network Anomaly Detection

• Turning service logs into datasets
• Introduction to classification of network attacks
• Detecting botnet topology
• Introduction to different ML algorithms for botnet detection
• Introduction to Gaussian anomaly detection
• Gaussian anomaly detection Implementation

Securing Users Authentication

• Introduction to Authentication abuse prevention
• Fake login management- reactive versus predictive
• Account reputation scoring
• User authentication with keystroke recognition Introduction
• User authentication with keystroke recognition Implementation
• Biometric authentication with facial recognition Introduction
• Dimensionality reduction with principal component analysis (PCA) Introduction
• Eigenfaces Implementation

Automatic intrusion detection

• Detecting DDos Attack
• Credit Card fraud detection Introduction
• Credit Card fraud detection Implementation
• Counterfeit bank note detection Implementation
• Ad blocking using machine learning Implementation
• Wireless indoor localization Implementation
• IoT device type identification using machine learning
• Deepfake recognition

Securing and Attacking Data with Machine Learning

• Assessing password security using ML
• ML-based steganalysis Introduction
• ML-based steganalysis Implementation
• ML attacks on PUFs Introduction
• ML attacks on PUFs Implementation
• ML attacks on PUFs Explanation
• HIPAA data breaches – data exploration and visualization

Projects

• Online Transaction Fraud Detection
• Fake News Detection
• Fake Product Review Detection
• Spam Email Detection

Credit Card Fraud Detection