How to Prevent Cyber Attacks with Big Data Analytics ?


The prevalent success of cloud computing, smart devices, SaaS and plethora of customer & end-user data collected as well as warehoused in today’s corporate environment in public and private sectors are more vulnerable to security threats and cyber attacks.

The issue isn’t if your business is prone to threats or someday it may face a cyber attack, but the issue is when, & if you’re already prepared.

Your organization’s reputation, intellectual property, and financial security are at risk from those desiring to do harm for profit.

Cyber attacks are harsh yet serious apprehensions of all sizes, calling for complex solutions to address real-time risks. Utilizing big data analytics is the perfect solution to safeguard businesses and organizations from breaches, identifying threats & attacks before and as they happen.

How Big Data is turning the Table on Security Threats?

The more big, sensitive & greater volume of end-user data you store, the more prone it is to be attacked virtually. That said, the same big data inviting threat can be used to prevent an attack. Big data comprises of all events, actions, activities and occurrences connected to a threat or cyber attack. Types of data prone to threat:

  • User: authentication & access location, user profiles, access date & time, roles, privileges, travel & business itineraries, normal working hours, activity behaviors, application usage, and typical data accessed.
  • Device: software revision, type, protocols and security certificates.
  • Customer: credit/debit card numbers, customer database, authentication, purchase histories, addresses, and personal data.
  • Network: destinations, locations, new & non-standard ports, date & time, log data, code installation, activity and bandwidth.
  • Content: files, documents, intellectual property, email, and application availability.

The more log data a company amasses, the greater the chances to detect, diagnose as well as shield the organization from cyber attacks by identifying glitches within the data & correlating them to other events going outside of expected behaviors, signaling a potential security threat. The challenge lies in assessing large amounts of data to unfurl unexpected patterns in a timely manner. That is where the role of analytics proves to be helpful.

Garnering Big Data with Analytics to Grasp a Thief

Using analytics, corporate sectors or businesses can practice real-time monitoring of network & user behaviors, discovering suspicious activity as it takes place. Organizations can model different network, application, user as well as service profiles to create intelligence-driven security parameters capable of quickly discovering glitches and correlated events signaling a security breach:

  • Suspicious customer transactions
  • Traffic anomalies to, from or between data warehouses
  • Unauthorized or dated devices accessing a network
  • Suspicious activity in high value or sensitive resources of your data network
  • Identify ports used to aggregate traffic for external offload of data
  • Newly installed software or different protocols used to access sensitive data
  • Suspicious user behaviors, for example, location, varied access times, levels, information queries & destinations

Big data analytics can be utterly effective in sniffing out an attack not quite underway or suggesting an action to defy an attack, hence minimizing or reducing losses. Analytics exploits big data with timely analysis of distracted events to prevent both the smallest, as well as the largest scale threats.

Security Monitoring via Big Data Solution

If security monitoring happens to be a data storage issue, it is needed a big data analytics solution with the capability of analyzing large amounts of data in real-time. The best place to look for this solution is Apache Hadoop, & the ecosystem of open-source technologies. Although Hadoop performs a good job in terms of analyzing large amounts of data, it was developed to facilitate batch analysis, not real-time streaming analytics needed to find out any security threats.

Contrary, the perfect solution for real-time streaming analytics is Apache Storm. It is a free & open-source real-time computation solution. It is similar to Hadoop, however, it was introduced for real-time analytics. It is fast & scalable, supporting not only analytics in real-time but, machine learning too, required to cut back the number of false positives found during security monitoring. This solution is commonly available in cloud solutions supporting antivirus programs, in which big data is analyzed to find out threats.




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