Big Data Analytics for Improved Cyber Security

Big Data Analytics for Improved Cyber Security Big data is in danger with malware and cyber attacks becoming more aggressive and voracious. Securing the large volumes of valuable data they have has become one of the most important priorities for governments and businesses. According to a recent report, federal experts believe big data analytics is a new, powerful tool to combat cyber security threats. Organizations are analyzing huge volumes of data to gain new insights that help them run their businesses better as well as understand the multiple scenarios that threaten the security of their data so that they can come up with the best cyber security solution.

The Power of Big Data Sorting through the masses of data that they collect helps organizations detect hidden patterns, and unexpected correlations and connections. Analyzing vast and complex unstructured data sets at quickly and accurately helps find solutions to the innumerable problems that businesses and governmental entities face.

According to one report, a study showed that companies that successfully incorporate big data and advanced analytics into their operations have profitability and productivity rates that are 5­6% higher than their peers.

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However, big data is exposed to potential internal and external security threats due to the following factors: •

Organizations open and extend their data networks to others, allowing suppliers, customers, outsourcing companies, and partners to access data in new ways to enhance collaboration

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Cyber attacks have become more complex to evade traditional defenses static threat detection measures and signature­based tools.

The need of the day is intelligence­driven security models to protect big data.

Features of an Intelligence Driven Security Model for Big Data

Experts point out that an intelligence­driven security model for big data should have the following features

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A comprehensive enterprise information architecture strategy that includes both big data as well as cyber security

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Classification of risks levels and mitigation measures

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D ata capture from

multiple sources , not just data inside your physical walls or inside your network or infrastructure. There are several open

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source data platforms (such as Hadoop) designed to do this and help you predict security threats.

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Develop analytics to continually monitor data, regardless of how vast or fast changing.

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Have facilities for user authentication, censoring data transmissions, and password protection

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Have a centralized system from where all security related data can be accessed by security analysts to query

Intelligence­driven security programs for big data automate the processes of risk assessment and threat detection, enhance situational awareness, and shorten reaction times to potential risks and problems.

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