Skip to main content

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 Na├»ve 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.


Comments

Popular articles

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs.
To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground.
1)      Many Eyes: Many Eyes is a data visualization experiment by IBM Researchandthe IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Map, Tag/Word cloud and ge…

Data deduplication tactics with HDFS and MapReduce

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.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort: Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban: A batch job scheduling system with a friendly UI, Azkab…