How Big Data Analytic can Help Prevent Cyber Attacks?

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More than three lakh computers in over 150 countries, including India were trapped by WannaCry a couple of days back. Have you wondered that what disaster this attack could have caused or may be something could have been done previously to prevent the hazardous cyber-attacks like WannaCry.

 

There are different ways to prevent cyber-attacks. Big Data is one of the solutions that can now be used as a preventive measure for severe cyber-attacks. The insistent growth of Big Data can be a double-edged sword for financial institutions; while it presents valuable opportunities to streamline operations and improve service delivery, it also increases openness from a cyber-security standpoint.

For several financial services organizations, a key challenge is scrutinizing these enormous amounts of data in a timely manner in order to identify when and where an attack is expected to occur. Real-time analysis is vital to being able to take action immediately on any Big Data insight, especially when it comes to turning that intelligence into a data security device.

The value in Big Data analytics is the capability to rapidly aggregate and analyze large datasets from many incongruent sources to identify patterns and expose glitches that could indicate a cyber-attack is forthcoming or may be in progress. Big Data analytics is already in use across the financial services industry to help predict and prevent cyber-attacks and fraud activities in a number of ways as follows:

  • Data analytics can help monitor user behavior and network activity in real-time, helping to identify abnormal occurrences and suspicious activity.
  • Algorithmic rules can be developed to generate symptoms or may be alarms when analytics picks up on irregular activity, such as recurrent visits from suspicious IP addresses or domains.
  • Machine learning techniquesproficient of learning typical user behavioral patterns can pinpoint incongruities and warning signs of fraud.
  • Transactional data from online banking channels and geo-location data from mobile applications can be analyzed along with historical data sets to identify abnormal activities.

Investing in a Big Data platform will help financial organizations quickly put their data to work to help them more intelligently protect themselves against cybercrime. As financial Big Data continues to increase in volume, velocity, and variety, reacting to security incidents is no longer an adequate way to address fraudulent activity. An end-to-end Big Data solution built on analytics and insight can work as a key to help financial organizations proactively predict, identify, and take action against malicious events before they occur, rather than simply responding to them.

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