Cyber Security

The use of data science for cyber-security applications is a relatively new paradigm. This includes deployment of statistical methodology, machine learning, and Big Data analytics for network modelling, anomaly detection, forensics, risk management, and more.
The Next Data Science for Cyber Security is dedicated in developing the capacity for keeping data secure and private throughout its lifetime.

Case Study 1:

Problem:
The leakage of the business data including the operation plans, sales and marketing, intellectual capitals, financial plans and projections can affect the business badly. How this can be prevented?
Solution:
Next Data Science provides the IT strategies and operations for your business data and also provides you the storage innovation for your business data storage.
Outcome:
It ensures help for better understand your Security Practice Development and add a security-focused practice to your business.

Case Study 2:

Problem:
Today Cyber Security needs increased visibility with context, not just silos of data from legacy solutions. The frustrating process of pivoting across products to gain more contexts is time-consuming and further restricts teams from keeping up with the modern pace of attacker evolution.
Solution:
Next Data Science improves contextual visibility, exposes blind spots and initiates faster response times and enhances current investments through advanced correlation and analysis across multiple customer sources.
Outcome:
With processes and people limited by this restrictions, organizations are investing heavily in solutions to reduce risk.
Case Study 3:
Problem:
How to detect DDoS attacks against data center?
Solution:
Next Data Science first analyze the correlation information of flows in data center. Second, we present an effective detection approach based on CKNN (K-nearest neighbours traffic classification with correlation analysis) to detect DDoS attacks.
Outcome:
The approach exploits correlation information of training data to improve the classification accuracy and reduce the overhead caused by the density of training data.