Following 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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Tags: analytics, Big Data, Cisco, database, Gremlin, InfiniteGraph, innovation, Intelligence, NoSQL, operations, security, Titan, TRAC, TRAC Big Data Analysis
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.
The AMPLab Statck
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Tags: AMPLab, analytics, BDAS, Big Data, BlinkDB, Cisco, custom, database, Hadoop, innovation, mapreduce, Mesos, NoSQL, Scala, security, Shark, Spark, Stack, TRAC, TRAC Big Data Analysis
Recently 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.
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Tags: analytics, API, Big Data, Cisco, database, Hadoop, HDFS, innovation, Intelligence, java, mapreduce, NoSQL, operations, security, Shark, Spark, SQL, telemetry, TRAC, TRAC Big Data Analysis
We are witnessing the growth of the Internet of Everything (IoE), the network of embedded physical objects accessed through the Internet, and it’s connecting new devices to the Internet which may not traditionally have been there before. Unfortunately, some of these devices may be deployed with a security posture that may need improvement.
Naturally when we saw a few posts about multi-architecture malware focused on the “Internet of Things”, we decided to take a look. The issue being exploited in those posts is CVE-2012-1823, which has both an existing Cisco IPS signature as well as some for Snort. It turns out this vulnerability is actually quite heavily exploited by many different worms, and it took quite a bit of effort to exclude all of the alerts generated by other pieces of malware in Cisco IPS network participation. Due to the vulnerability-specific nature of the Cisco IPS signature, the same signature covers this issue as well as any others that use this technique; just one signature provides protection against all attempts to exploit this vulnerability. As you can see in the graph below this is a heavily exploited vulnerability. Note that these events are any attack attempting to exploit this issue, not necessarily just the Zollard worm.
The graph below is derived from both Cisco IPS and Sourcefire IPS customers. The Cisco data is from customers who have ‘opted-in’ to network participation. This service is not on by default. The Sourcefire data below is derived from their SPARK network of test sensors. This graph is showing the percent increase of alert volume from the normal for each dataset at the specified time.
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Tags: #IoE, clamAV, Internet of Everything, IPS, IPS signatures, malware, Sourcefire, TRAC
Security intelligence, threat intelligence, cyber threat intelligence, or “intel” for short is a popular topic these days in the Infosec world. It seems everyone has a feed of “bad” IP addresses and hostnames they want to sell you, or share. This is an encouraging trend in that it indicates the security industry is attempting to work together to defend against known and upcoming threats. Many services like Team Cymru, ShadowServer, ThreatExpert, Clean MX, and Malware Domain List offer lists of known command and control servers, dangerous URIs, or lists of hosts in your ASN that have been checking-in with known malicious hosts. This is essentially outsourced or assisted incident detection. You can leverage these feeds to let you know what problems you already have on your network, and to prepare for future incidents. This can be very helpful, especially for organizations with no computer security incident response teams (CSIRT) or an under-resourced security or IT operations group.
There are also commercial feeds which range anywhere from basic notifications to full-blown managed security solution. Government agencies and industry specific organizations also provide feeds targeted towards specific actors and threats. Many security information and event management systems (SIEMs) offer built-in feed subscriptions available only to their platform. The field of threat intelligence services is an ever-growing one, offering options from open source and free, to commercial and classified. Full disclosure: Cisco is also in the threat intelligence business
However the intent of this article is not to convince you that one feed is better than another, or to help you select the right feed for your organization. There are too many factors to consider, and the primary intention of this post is to make you ask yourself, “I have a threat intelligence feed, now what?” Read More »
Tags: cisco sio, CSIRT, csirt-playbook, cybersecurity, incident response, infosec, operational security, security, security intel