Data Science Cybersecuity Implementation

Description

Machine learning is disrupting cybersecurity to a greater extent than almost any other industry. Many problems in cyber security are well suited to the application of machine learning as they often involve some form of anomaly detection on very large volumes of data. This course deals the most found issues in cybersecurity such as malware, anomalies detection, SQL injection, credit card fraud, bots, spams and phishing. All these problems are covered in case studies.

• Section 1:Statistics - Machine Learning

  
  

• Lecture 1:Central Tendency (Preview)

• Lecture 2:Measures Dispersion (Preview)

• Lecture 3:Data Visualization (Preview)

• Lecture 4:Confusion Matrix, Accuracy and Kappa

  
  

• Section 2:Case Studies

  
  

• Lecture 5:Introduction to Payment Fraud (Preview)

• Lecture 6:Machine Learning in Payment Fraud

• Lecture 7:"NO CODING"_Machine Learning in Payment Fraud

• Lecture 8:Introduction to Malware

• Lecture 9:Machine Learning in Malware

• Lecture 10:Introduction to Phishing

• Lecture 11:Machine Learning in Phishing

• Lecture 12:Introduction to IDS

• Lecture 13:Machine Learning in IDS

• Lecture 14:Introduction to Spam

• Lecture 15:Machine Learning in Spam

• Lecture 16:Introduction to Twitter Bot Detector

• Lecture 17:Machine Learning in Twitter Bot Detector

• Lecture 18:Introduction to Malicious SQL Injection

• Lecture 19:Machine Learning in SQL Injection

• Lecture 20:"NO CODE"_Machine Learning in Medical Fraud Detection (Preview)

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