6 min read
There's a downside to the widespread success of cloud computing, SaaS, smart devices and the plethora of customer and end-user data collected and warehoused in today's business environment: public and private sector organizations are more vulnerable than ever to attacks on their data systems.
The issue is no longer _if_ your business is vulnerable or may someday face a cyber-attack; the issue is when, and if you will be prepared.
Cyber attacks are serious concerns for organizations of all sizes, requiring complex solutions to address real-time threats. Utilizing the combination of big data + analytics is the ideal solution to protect businesses and organizations from such attacks, identifying threats and attacks before and as they occur.
Big Data is Turning the Table on Security Threats
The more comprehensive, sensitive and greater volume of end user and customer data you warehouse, the more tempting you are to someone wanting to do harm. That said, the same data attracting the threat can be used to thwart an attack. Big data includes all events, activities, actions, and occurrences associated with a threat or attack:
The more log data you amass, the greater the opportunity to detect, diagnose and protect an organization from cyber attacks by identifying anomalies within the data and correlating them to other events falling outside of expected behaviors, indicating a potential security breach. The challenge lies in analyzing large amounts of data to uncover unexpected patterns in a timely manner. That's where analytics comes into play.
Leveraging Big Data with Analytics to Catch a Thief
Using analytics, organizations can exercise real-time monitoring of network and user behaviors, identifying suspicious activity as it occurs. Organizations can model various network, user, application and service profiles to create intelligence-driven security measures capable of quickly identifying anomalies and correlating events indicating a threat or attack:
Analytics can be highly effective in identifying an attack not quite underway or recommending an action to counter an attack, thus minimizing or eliminating losses. Analytics makes use of big data with timely analysis of disparate events to thwart both the smallest and largest scale attacks.
The Big Data Solution to Security Monitoring
If security monitoring is a data storage problem, then it requires a big data analytics solution capable of analyzing large amounts of data in real time. The natural place to look for that solution is within Apache Hadoop, and the ecosystem of dependent technologies. But although Hadoop does a good job performing analytics on large amounts of data, it was developed to provide batch analysis, not real-time streaming analytics required to detect security threats.
In contrast, the solution for real-time streaming analytics is Apache Storm, a free and open source real-time computation system. Storm functions similar to Hadoop, but was developed for real-time analytics. Storm is fast and scalable, supporting not only real-time analytics but machine learning as well, necessary to reduce the number of false positives found in security monitoring. Storm is commonly found in cloud solutions supporting antivirus programs, where large amounts of data is analyzed to identify threats, supporting quick data processing and anomaly detection.
The key is real-time analysis. Big data contains the activities and events signaling a potential threat, but it takes real-time analytics to make it an effective security tool, and the statistical analysis of data science tools to prevent security breaches.
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