Anton Chuvakin

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Anton Chuvakin
Research Director
1 year with Gartner
12 years IT industry

Anton Chuvakin is a research director at Gartner's IT1 Security and Risk Management group. Before Mr. Chuvakin joined Gartner, his job responsibilities included security product management, evangelist… Read Full Bio

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Big Data for Security Realities – Case 3: Elastic Search or Similar

by Anton Chuvakin  |  November 6, 2013  |  1 Comment

Part of my research this quarter focuses on assessing the reality of using big data approaches for security and providing practical, GTP-style recommendations for enterprises. So, what else IS real in this segment overrun by waves of bull?

One more case that occasionally (not as often as Case 1) shows up is “massive indexed pile.”

This is not just about data collection at scale, but indexing and text analysis of unstructured text data or barely structured data. The fact is, indexed search over vast quantities of data is very different that simply having a pile of such data, even if stored in HDFS. A 1TB file stored on disk(s) is much less usable (and thus less useful) compared to the same file with a well designed indexed search logic on topic of it. The difference is not in quantity (you can merely search faster), but in quality (here you can find it within the time you have, and there you cannot).

One may think ‘I can just grep it’, but do try running grep on a 10GB file one of one days. Sloooow! Now realize that if your daily log haul is 10TBs, the situation is much more than a 1000 times worse.

Admittedly, 10TB of log-like data a day is not that common (but not that rare nowadays either, especially if all logs – not just security relevant ones – are collected), but usually when the way to handle such data appears, the volume and data usage tend to grow.

The scenario goes something like this:

  1. the organization has no SIEM or log management (or only basic log management)
  2. it wants to be able to collect, access, and ultimately see the data and there is a lot of it already piling up in various places
  3. sometimes multiple stake holders get in the same room and compare notes on what data they need and what level of access/analysis
  4. given the volume, needs and available resources, the organization goes commercial or open source
  5. over time, some organizations move from searching to narrowly focused analysis (to be discussed in the Case 4 post in a week or so) of search result sets, and then sometimes to real analytics.

Now, is keyword searching of a huge pile of data really analytics? No, it really is not. However, after you find the data, you can structure it selectively, apply whatever schema du our or place it in whatever structured data storage (be it RDBMS or MongoDB). Big data will not end with your indexed pile, but it can definitely start there.

Reported volumes from the setups I’ve seen so far go well into single digit petabytes of total data volume. The way it is implemented may include Hadoop with Lucene/Solr, ElasticSearch, logstash (also see this), ELSA or a commercial tool (that you all know pretty well…) – the implementation details are a separate story and go beyond the scope of this post.

By the way, the difference between this case and case 1 is in the fact that for case 1, the big data-style system is used as expanded storage for a SIEM, while in this case it is the primary system in use (no SIEM). Admittedly, a hybrid case is entirely possible and was seen a few times as well.

Related posts on the topic of big data for security:

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