Battling the bots with MapReduce

One of the major battles that the systems fight today is against the bots. Be it search zombies, spam email bots or registration bots, there is a big one upmanship battle between the bots and the systems.

To detect bots, a generic architecture approach utilizing MapReduce and Hadoop is shown in the figure below. This architecture is adaptable for most algorithmic techniques where MapReduce is employed currently. Custom variations may be done based on individual tool sets that the architect may be adopting.

Many e-mail systems like Hotmail have successfully implemented such techniques with Dryad as well. There are some commercial service providers which utilize Cassandra, Hive etc. on top of HDFS. Further on top of it, there is a presentation layer combined with analysis dashboard utilizing the traditional portals and BI approach.

Most systems today employ techniques like CAPTCHAs. However, there are organized attacks where the CAPTCHA have been manually or with automated prior retrieval been compromised by bots. Similarly, spam constitutes a major chunk of emails and most of these e-mails originate from Hotmail (Outlook), Gmail and Yahoo Email – the biggest fighters against spam.

Algorithms have been tuned to counter the varying refinements in spamming technology. The algorithmic approach may vary with each implementation depending on use case. For example, one e-mail provider may tend to analyse based on IP address and user ids. Another published approach mentions use of PageRank to detect P2P botnets. Other algorithms may employ Naïve Bayesian classifier or apriori rules. With computation a major bottleneck to analyze massive data sets, MapReduce offers a significant reduction in processing time with its parallel processing architecture.

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In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…

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As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
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The Gora 0.3 Toolbox We briefly digress to discuss some other noticeable additions to Gora in 0.3, namely: Modification of the Query interface: The Query interface was amended from Query<K, T> to Query<K, T extends Persistent> to be more precise and explicit for developers. Consequently all implementors and users of the Query interface can only pass object's of Persistent type. Logging improvements for data store mappings: A key aspect of using Gora well is the establishment and accurate definitio…

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