Building Machine Learning & NLP Models for Cyber Security

About this course

Welcome to Building Machine Learning & NLP Models for Cyber Security course. This is a comprehensive project based course where you will learn how to build intrusion detection system, predict vulnerability score, and classify cyber threat using machine learning models like Random Forest Classifier, Logistic Regression, MLP Regressor, Decision Tree Regressor, KNN, XGBoost, Naive Bayes, and K Means Clustering. This course is a perfect combination between machine learning and cyber security, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in system security. In the introduction session, you will learn about machine learning and natural language processing applications in cyber security, specifically how it can help to enhance risk management and strengthen overall security. Then, in the next section, we will learn how intrusion detection models work. This section will cover data collections, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, detecting intrusion, model evaluation, deployment, and monitoring. Afterward, we will download cyber security datasets from Kaggle, it is a platform that offers many high quality datasets from various sectors. Once everything is all set, then, we will start the project, firstly, we will clean the dataset by removing all missing values and duplicates, after we make sure the data is clean and ready to use, we will start exploratory data analysis, firstly we are going to analyze the relationship between protocol type and intrusion, which will enable us to understand how different communication protocols contribute to intrusion risk, following that, we are also going to analyze intrusion rate for each browser type, which will allow us to uncover potential vulnerabilities associated with specific browsers, then, we are going to calculate the average login attempts and failed logins for both normal and intrusion cases, which will help us to identify su

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