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Big Data in Security – Part III: Graph Analytics

TRACFollowing part two of our Big Data in Security series on University of California, Berkeley’s AMPLab stack, I caught up with talented data scientists Michael Howe and Preetham Raghunanda to discuss their exciting graph analytics work.

Where did graph databases originate and what problems are they trying to solve?

Michael: Disparate data types have a lot of connections between them and not just the types of connections that have been well represented in relational databases. The actual graph database technology is fairly nascent, really becoming prominent in the last decade. It’s been driven by the cheaper costs of storage and computational capacity and especially the rise of Big Data.

There have been a number of players driving development in this market, specifically research communities and businesses like Google, Facebook, and Twitter. These organizations are looking at large volumes of data with lots of inter-related attributes from multiple sources. They need to be able to view their data in a much cleaner fashion so that the people analyzing it don’t need to have in-depth knowledge of the storage technology or every particular aspect of the data. There are a number of open source and proprietary graph database solutions to address these growing needs and the field continues to grow.

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Big Data in Security – Part II: The AMPLab Stack

TRAC

Following part one of our Big Data in Security series on TRAC tools, I caught up with talented data scientist Mahdi Namazifar to discuss TRAC’s work with the Berkeley AMPLab Big Data stack.

Researchers at University of California, Berkeley AMPLab built this open source Berkeley Data Analytics Stack (BDAS), starting at the bottom what is Mesos?

AMPLab is looking at the big data problem from a slightly different perspective, a novel perspective that includes a number of different components. When you look at the stack at the lowest level, you see Mesos, which is a resource management tool for cluster computing. Suppose you have a cluster that you are using for running Hadoop Map Reduce jobs, MPI jobs, and multi-threaded jobs. Mesos manages the available computing resources and assigns them to different kinds of jobs running on the cluster in an efficient way. In a traditional Hadoop cluster, only one Map-Reduce job is running at any given time and that job blocks all the cluster resources.  Mesos on the other hand, sits on top of a cluster and manages the resources for all the different types of computation that might be running on the cluster. Mesos is similar to Apache YARN, which is another cluster resource management tool. TRAC doesn’t currently use Mesos.

 

AMPLab Stack

The AMPLab Statck
Source: https://amplab.cs.berkeley.edu/software/

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Big Data in Security – Part I: TRAC Tools

TRACRecently I had an opportunity to sit down with the talented data scientists from Cisco’s Threat Research, Analysis, and Communications (TRAC) team to discuss Big Data security challenges, tools and methodologies. The following is part one of five in this series where Jisheng Wang, John Conley, and Preetham Raghunanda share how TRAC is tackling Big Data.

Given the hype surrounding “Big Data,” what does that term actually mean?

John:  First of all, because of overuse, the “Big Data” term has become almost meaningless. For us and for SIO (Security Intelligence and Operations) it means a combination of infrastructure, tools, and data sources all coming together to make it possible to have unified repositories of data that can address problems that we never thought we could solve before. It really means taking advantage of new technologies, tools, and new ways of thinking about problems.

Big Data

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Summary : Sourcefire in our Data Center by Cisco Chief Security Officer

 

Sourcefire

 

 

 

 

 

Last October , Cisco confirmed that Sourcefire was now part of our family of security products and solutions .

“With this acquisition, we take a significant and exciting step in our journey to define the future of security. As one company, we offer an unbeatable combination that will greatly accelerate our mission of delivering a new, threat-centric security model. Through the addition of Sourcefire’s competitive talent and technologies, I see vast opportunities to expand Cisco’s global security footprint in both new and emerging markets, broaden our solution sets and deepen our customer relationships “

Chris Young, Cisco Senior Vice President Security Group
in his blog “Delivers Threat-Centric Security Model “

“Beyond the technology, one of the things that is important to me is that Cisco and Sourcefire both share key values that transcend our company names, HQ locations and number of employees. Much like Sourcefire’s Firemen Principles, you can be confident that these values will continue as one team at Cisco.”

Martin Roesch, Sourcefire founder and CTO and now VP and Chief Architect of Cisco Security Group
in his blog ONE Team 

These days , John Stewart , Senior Vice President, Cisco Chief Security Officer , announced that we completed the  deployment of Sourcefire at Cisco . John Stewart oversees at Cisco the Threat Response, Intelligence and Development ( TRIAD ) organization .

The implementation is already giving us insights into our data center that we never had before

To know  more about this deployment and John’s first impressions check his blog
 The First Inline Production Deployment at Cisco 

 

The Cisco security architecture helps data center networking teams take advantage of security capabilities built into the underlying data center fabric, to accelerate safe data center innovation. There are three important security measures that every IT organization should follow to securely support data center innovation. 
To learn more, download the Cisco white paper “Three Must-Have Security Measures that Accelerate Data Center Innovation.”

Tell us what do you think of the acquisition of Sourcefire by Cisco .

 

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Your Data, Yourself — in the Marketplace of Me

On a typical day, we leave a vast trail of data in our wake. Our browsing histories, online preferences, shopping habits, work decisions, social interactions—all are rendered in binary code, prompting a complex interaction of requests, responses, affirmations, and denials.

And that’s just from our laptops and smartphones.

What about when the Internet of Everything — with its explosion in connectivity from 10 billion “things” today to 50 billion in 2020 — truly shifts into overdrive? At that point, our clothing, our houses, our cars, our lawns, and our refrigerators may be generating ever-larger torrents of data — all about us.

This upsurge in personal Big Data has big implications. Indeed, each person’s emerging digital persona will go a long way toward defining their place in the world.  Furthermore, all of that data already has great intrinsic value to Internet giants, retailers, financial services companies, and many others. If we manage it right — in what I see as a burgeoning Marketplace of Me — some of that value may come right back to us.

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