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 posts from this blog

Beyond NSA, the intelligence community has a big technology footprint

While all through the past few days the focus has been on NSA activities, the discussion has often veered around the technologies and products used by NSA. At the same time, a side discussion topic has been the larger technical ecosystem of intelligence units. CIA has been one of the more prolific users of Information Technology by its own admission. To that extent, CIA spinned off a venture capital firm In-Q-Tel in 1999 to invest in focused sector companies. Per Helen Coster of Fortune Magazine, In-Q-Tel (IQT) has been named “after the gadget-toting James Bond character Q”.
In-Q-Tel states on its website that “We design our strategic investments to accelerate product development and delivery for this ready-soon innovation, and specifically to help companies add capabilities needed by our customers in the Intelligence Community”. To that effect, it has made over 200 investments in early stage companies for propping up products. Being a not-for-profit group, unlike Private Venture capi…

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…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…