Strengthening Cybersecurity Intelligence with Big Data Engineering and Analytics
Monitoring information systems and understanding security incidents and events play a critical role in undertaking cybersecurity operations. As the scale and complexity of information systems grow, the tasks of managing and analyzing massive heterogeneous, interdependent, security-relevant data sources have become a challenging feat to achieve. In addition, data sources are both structured and unstructured, including log files not only of networks and servers, but also of social media, blogs, forums, and surveillance cameras.
Some of the main data engineering and analytics technologies essential to cybersecurity intelligence must be able to answer the following concerns:
(i) store and manage massive time series data in cloud/data store to support efficient, distributed big data analytics
(ii) deliver Data as a Service (DaaS) efficiently in cloud store with proper APIs for sharing and collaboration
(iii) utilize DaaSs to analyze the causation of behavior footprints and experience of users
(iv) perform data ingestion and integration to enable effective and efficient data analytics
Applying big data analytics in cybersecurity is critical. Analysts can discover useful network information from data by exploiting data from various networks and computers. This can allow the analysts to uncover insights that are more informative, including the actions, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Leveraging the information collected from ever-growing data sources, big data analytics has already proven its value to the client organizations. In that sense, particularly when it comes to learning from past attacks, cybersecurity is next.
Virtually any industry can use big data for better cybersecurity. In fact, federal IT managers are now able to decrease instances of malware, insider threats, and social engineering by using big data. Companies are also investing in business intelligence and machine learning to analyze vast amounts of structured and unstructured information to deliver valuable insights.
In order to use big data more efficiently and improving cybersecurity beyond measure, some organizations are adding machine learning into the equation. The technology is built to scan data and generate historical patterns of positive and negative behaviors. These capabilities can be used by organizations to detect vulnerabilities, identify breaches and correlate information from multiple sources.
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