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Customized Machine Learning to Combat Cyber Attacks

White Paper

 

Customized Machine Learning to Combat Cyber Attacks

Combine machine learning solutions with mature commercial cyber security products to win against cyber criminals 

Adopt a three-pronged approach to make the best use of machine learning (ML) and artificial intelligence to detect and counter cyber-attacks:

  • Develop robust machine learning (ML) based algorithms against potential subversion and degradation by attackers: Proactively identify potential vulnerabilities of the ML algorithms by simulating different types of attacks.
  • Employ active learning with limited training date for ML-based algorithms: Obtain an ongoing feedback from the user on the accuracy of its prediction. Security experts will be engaged to identity false positives, which will be used to improve the existing algorithms using limited training datasets. 
  • Develop customized ML-based algorithms using client-specific data in the client-specific environment: Provide customers with a unique cyber-security protection solution, tailored exclusively to their needs.
Satish Thiagarajan

Global Head TCS Cyber Security

David Makovoz

Lead Data Scientist TCS Cyber Security