Udemy Building Machine Learning & NLP Models for Cyber Security (1 Viewer)

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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 suspicious authentication patterns. In the next section, we are going to conduct feature importance analysis, specifically, we will rank the features with the strongest correlation to the target variable, and create a heatmap to visualize their relationships. Then, in the next section, we will start building machine learning models. In the first project, we are going to build intrusion detection models using Random Forest Classifier and Logistic Regression, which will allow us to detect unauthorized access attempts and prevent potential breaches in the system. In the second project, we are going to build multiclass cyber threat classification models using K Nearest Neighbour and XGBoost, which will enable us to identify different types of cyber attacks and take the right preventive measures. Meanwhile, in the third project, we are going to predict vulnerability scores using Multi Layer Perceptron Regressor and Decision Tree Regressor, which will help us to assess the severity of system weaknesses. Then, in the fourth project, we are going to detect phishing emails using natural language processing, specifically we will use the Multinomial Naive Bayes model, which will allow us to recognize malicious email content. In the fifth project, we are going to analyze user behavior using unsupervised machine learning, specifically we will use K Means clustering, which will enable us to detect unusual activity patterns. Lastly, at the end of the course, we are going to test the machine learning model in a real time simulation where every five seconds new synthetic login data is generated, which will allow us to observe how the system responds by blocking access when intrusion is detected and approving access when the session is normal.

Before getting into the course, we need to ask this question to ourselves, why should we use machine learning to enhance cyber security? Well, here is my answer. From a technical perspective, machine learning can quickly analyze large volumes of security data, detect hidden patterns, and identify potential threats. From a business perspective, it helps businesses reduce risk, prevent costly security breaches, and make faster decisions to protect their digital assets.

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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 suspicious authentication patterns. In the next section, we are going to conduct feature importance analysis, specifically, we will rank the features with the strongest correlation to the target variable, and create a heatmap to visualize their relationships. Then, in the next section, we will start building machine learning models. In the first project, we are going to build intrusion detection models using Random Forest Classifier and Logistic Regression, which will allow us to detect unauthorized access attempts and prevent potential breaches in the system. In the second project, we are going to build multiclass cyber threat classification models using K Nearest Neighbour and XGBoost, which will enable us to identify different types of cyber attacks and take the right preventive measures. Meanwhile, in the third project, we are going to predict vulnerability scores using Multi Layer Perceptron Regressor and Decision Tree Regressor, which will help us to assess the severity of system weaknesses. Then, in the fourth project, we are going to detect phishing emails using natural language processing, specifically we will use the Multinomial Naive Bayes model, which will allow us to recognize malicious email content. In the fifth project, we are going to analyze user behavior using unsupervised machine learning, specifically we will use K Means clustering, which will enable us to detect unusual activity patterns. Lastly, at the end of the course, we are going to test the machine learning model in a real time simulation where every five seconds new synthetic login data is generated, which will allow us to observe how the system responds by blocking access when intrusion is detected and approving access when the session is normal.

Before getting into the course, we need to ask this question to ourselves, why should we use machine learning to enhance cyber security? Well, here is my answer. From a technical perspective, machine learning can quickly analyze large volumes of security data, detect hidden patterns, and identify potential threats. From a business perspective, it helps businesses reduce risk, prevent costly security breaches, and make faster decisions to protect their digital assets.

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