The Complete Artificial Intelligence for Cyber Security

Description

In this course, you will learn:

• Isolation Forest
• Markov Chains
• Statsmodels
• NLP (Natural Language Processing)
• Linear Regression
• Logistic Regression
• Naïve Bayes
• ANN (Artificial Intelligence)
• Random Forest
• K-means
• HMM
• Eigenfaces and Eigenvalues
• SVM (Support Vector Machine)
• XGBOOST
• Pandas
• Numpy
• matplotlib
• IF-IDF
• Tensorflow
• Scikit-Learn
• Cyber security
• Google Colab
• Data Pre-processing.
• Analysing Data.
• Data standardization.
• Splitting Data into Training Set and Test Set.
• One-hot Encoding.
• Understanding Machine Learning Algorithm.
• Training Neural Network.
• Model building.
• Analysing Results.
• Model compilation.
• A Comparison Of Categorical And Binary Problem.
• Make a Prediction.
• Testing Accuracy.
• Confusion Matrix.
• Keras.

Syllabus:

1. Basic machine learning for cyber security (NEW CONTENT)

• Train test splitting the data Introduction
• Train test splitting the data Implemetation
• Standardizing your data
• Summarizing large data using principal component analysis
• Generating text using Markov chains
• Performing clustering using scikit-learn
• Training an XGBoost classifier
• Analyzing time series using statsmodels
• Analyzing time series using statsmodels Explanation
• Anomaly detection with Isolation Forest introduction
• Anomaly detection with Isolation Forest Implementation
• Anomaly detection with Isolation Forest Explanation
• Natural language processing using a hashing vectorizer and tf-idf Introduction
• Natural language processing using a hashing vectorizer and tf-idf Implementation

2. (New Content) Detecting Email Cybersecurity Threats with AI

• 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

3. (NEW COTENT) 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

4. (New Content) Advanced malware threat detection

• Detecting obfuscated JavaScript Implementation
• Detecting obfuscated JavaScript Explaination
• Tracking malware drift Implementation
• Tracking malware drift Explaination

5. (New Content) Network Anomaly Detection with AI

• 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

5. (NEW COTENT) 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

6. (New Content) 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

7. (New Content) 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

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