Internet Watch Fund Teams with Blockchain Forensics Startup
December 29, 2016
A British charity is teaming up with an online intelligence startup specializing in Bitcoin. The Register reports on this in their piece called, Bitcoin child abuse image pervs will be hunted down by the IWF. The Internet Watch Foundation, with the help of a UK blockchain forensics start-up, Elliptic, aims to identify individuals who use Bitcoin to purchase child abuse images online. The IWF will provide Elliptic with a database of Bitcoin addresses and Elliptic takes care of the rest. We learned,
The IWF has identified more than 68,000 URLs containing child sexual abuse images. UNICEF Malaysia estimates two million children across the globe are affected by sexual exploitation every year. Susie Hargreaves, IWF CEO, said, “Over the past few years, we have seen an increasing amount of Bitcoin activity connected to purchasing child sexual abuse material online. Our new partnership with Elliptic is imperative to helping us tackle this criminal use of Bitcoin.” The collaboration means Elliptic’s clients will be able to automatically monitor transactions they handle for any connection to proceeds of child sex abuse.
Machine learning and data analytics technologies are used by Elliptic to collect actionable evidence for law enforcement and intelligence agencies. The interesting piece of this technology, and others like it, is that it runs perhaps as surreptitiously in the background as those who use the Dark Web and Bitcoin for criminal activity believe they do.
Megan Feil, December 29, 2016
Exit Shakespeare, for He Had a Coauthor
November 22, 2016
Shakespeare is regarded as the greatest writer in the English language. Many studies, however, are devoted to the theory that he did not pen all of his plays and poems. Some attribute them to Francis Bacon, Edward de Vere, Christopher Marlowe, and others. Whether Shakespeare was a singular author or one of many, two facts remain: he was a dirty, old man and it could be said he plagiarized his ideas from other writers. Shall he still be regarded as the figurehead for English literature?
Philly.com takes the Shakespeare authorship into question in the article, “Penn Engineers Use Big Data To Show Shakespeare Had Coauthor On ‘Henry VI’ Plays.” Editors of a new edition of Shakespeare’s complete works listed Marlowe as a coauthor on the Henry VI plays due to a recent study at the University of Pennsylvania. Alejandro Ribeiro used his experience researching networks could be applied to the Shakespeare authorship question using big data.
Ribeiro learned that Henry VI was among the works for which scholars thought Shakespeare might have had a co-author, so he and lab members Santiago Segarra and Mark Eisen tackled the question with the tools of big data. Working with Shakespeare expert Gabriel Egan of De Montfort University in Leicester, England, they analyzed the proximity of certain target words in the playwright’s works, developing a statistical fingerprint that could be compared with those of other authors from his era.
Two other research groups had the same conclusion with other analytical techniques. The results from all three studies were enough to convince the lead general editor of the New Oxford Shakespeare Gary Taylor, who decided to list Marlowe as a coauthor to Henry VI. More research has been conducted to determine other potential Shakespeare coauthors and six more will also be credited in the New Oxford editions.
Ribeiro and his team created “word-adjacency networks” that discovered patterns in Shakespeare’s writing style and six other dramatists. They discovered that many scenes in Henry VI were non-written in Shakespeare’s style, enough to prove a coauthor.
Some Shakespeare purists remain against the theory that Shakespeare did not pen all of his plays, but big data analytics proves many of the theories that other academics have theorized for generations. The dirty old man was not old alone as he wrote his ditties.
Whitney Grace, November 22, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Google Cloud, Azure, and AWS Differences
October 18, 2016
With so many options for cloud computing, it can be confusing about which one to use for your personal or business files. Three of the most popular cloud computing options are Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure. Beyond the pricing, the main differences range from what services they offer and what they name them. Site Point did us a favor with its article comparing the different cloud services: “A Side-By-Side Comparison Of AWS, Google Cloud, And Azure.”
Cloud computing has the great benefit of offering flexible price options, but they can often can very intricate based on how much processing power you need, how many virtual servers you deploy, where they are deployed, etc. AWS, Azure, and Google Cloud do offer canned solutions along with individual ones.
AWS has the most extensive service array, but they are also the most expensive. It is best to decide how you want to use cloud computing because prices will vary based on the usage and each service does have specializations. All three are good for scalable computing on demand, but Google is less flexible in its offering, although it is easier to understand the pricing. Amazon has the most robust storage options.
When it comes to big data:
This requires very specific technologies and programming models, one of which is MapReduce, which was developed by Google, so maybe it isn’t surprising to see Google walking forward in the big data arena by offering an array of products — such as BigQuery (managed data warehouse for large-scale data analytics), Cloud Dataflow (real-time data processing), Cloud Dataproc (managed Spark and Hadoop), Cloud Datalab (large-scale data exploration, analysis, and visualization), Cloud Pub/Sub (messaging and streaming data), and Genomics (for processing up to petabytes of genomic data). Elastic MapReduce (EMR) and HDInsight are Amazon’s and Azure’s take on big data, respectively.
Without getting too much into the nitty gritty, each of the services have their strengths and weaknesses. If one of the canned solutions do not work for you, read the fine print to learn how cloud computing can help your project.
Whitney Grace, October 18, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Apache Sparking Big Data
April 3, 2015
Apache Spark is an open source cluster computing framework that rivals MapReduce. Venture Beat says that people did not pay that much attention to Apache Spark when it was first invented at University of California’s AMPLAB in 2011. The article, “How An Early Bet On Apache Spark Paid Off Big” reports the big data open source supporters are adopting Apache Spark, because of its superior capabilities.
People with big data plans want systems that process real-time information at a fast pace and they want a whole lot of it done at once. MapReduce can do this, but it was not designed for it. It is all right for batch processing, but it is slow and much to complex to be a viable solution.
“When we saw Spark in action at the AMPLab, it was architecturally everything we hoped it would be: distributed, in-memory data processing speed at scale. We recognized we’d have to fill in holes and make it commercially viable for mainstream analytics use cases that demand fast time-to-insight on hordes of data. By partnering with AMPLab, we dug in, prototyped the solution, and added the second pillar needed for next-generation data analytics, a simple to use front-end application.”
ClearStory Data was built using Apache Spark to access data quickly, deliver key insights, and making the UI very user friendly. People who use Apache Spark want information immediately to be utilized for profit from a variety of multiple sources. Apache Spark might ignite the fire for the next wave of data analytics for big data.
Whitney Grace, April 3, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
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