Data Magic by Ping
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Your eBay Password For Sale? How, Where And Why

Your eBay Password For Sale? How, Where And Why | Data Magic by Ping | Scoop.it
Since the eBay Inc announcement of a massive data breach a seller has turned up on Pastebin (a notorious site used for posting anonymously that is often used by hacktivists dumping data from hacks) offering a full copy of the 145,312,663 usernames,...
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10 Big Data Pros To Follow On Twitter - InformationWeek

10 Big Data Pros To Follow On Twitter - InformationWeek | Data Magic by Ping | Scoop.it
10 Big Data Pros To Follow On Twitter
InformationWeek
Twitter's kind of an ironic place to look for big data wisdom.

Via Gregory Piatetsky
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New York City is the home of the world’s first Bitcoin Center | Bitcoin Examiner

New York City is the home of the world’s first Bitcoin Center | Bitcoin Examiner | Data Magic by Ping | Scoop.it

The first Bitcoin Center was born just two days ago and is already shaking New York City’s crypto-scene. The organization, located at 40 Broad Street – right in the heart of Lower Manhattan’s Financial District – is providing a place where the users of digital currency can trade Bitcoin and learn more about it, among other things.


Via Coinboard
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8 Effective Data Visualization Methods For Security Teams - Dark Reading

8 Effective Data Visualization Methods For Security Teams - Dark Reading | Data Magic by Ping | Scoop.it
Getting the most out of security analytics data sets, large or small, by visualizing the information

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Here’s Where Some Former HP Big Data Engineers And Execs Have Ended Up | Brian Brown's Official Website

Here's Where Some Former HP Big Data Engineers And Execs Have Ended Up - , a Boston-based big data... http://t.co/DmYw9ErFNC
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Google, NASA quantum computing project could bring stronger machine learning to the masses

Google, NASA quantum computing project could bring stronger machine learning to the masses | Data Magic by Ping | Scoop.it
Google said Thursday it is establishing a Quantum Artificial Intelligence Lab to trigger the next phase of machine learning with the power of quantum computers. The efforts could trickle down to ordinary people.
ping yan's insight:

Every project Google takes on gets me excited. Eager to learn more on this one. 

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Web Analytics and Big Data: Hype or the Next Necessity? | Technology-Enabled Business Solutions

Web Analytics and Big Data: Hype or the Next Necessity? | Technology-Enabled Business Solutions | Data Magic by Ping | Scoop.it
Why “Big Data as it relates to web analytics feels like one of the trends that has a chance to transcend hype to become meaningful for marketers. (Web Analytics and Big Data: Hype or the Next Necessity?
ping yan's insight:

here and now instead of next necessity.

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Machine Learning : Naïve Bayes Rule for Malware Detection and Classification


Via cysap
ping yan's insight:

pretty good walk-through example of a naive bayes classifier 

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So It Begins: Darpa Sets Out to Make Computers That Can Teach Themselves

So It Begins: Darpa Sets Out to Make Computers That Can Teach Themselves | Data Magic by Ping | Scoop.it

The Pentagon’s blue-sky research agency is readying a nearly four-year project to boost artificial intelligence systems by building machines that can teach themselves — while making it easier for ordinary schlubs like us to build them, too.

 

When Darpa talks about artificial intelligence, it’s not talking about modeling computers after the human brain. That path fell out of favor among computer scientists years ago as a means of creating artificial intelligence; we’d have to understand our own brains first before building a working artificial version of one. But the agency thinks we can build machines that learn and evolve, using algorithms — “probabilistic programming” — to parse through vast amounts of data and select the best of it. After that, the machine learns to repeat the process and do it better.

 

But building such machines remains really, really hard: The agency calls it “Herculean.” There are scarce development tools, which means “even a team of specially-trained machine learning experts makes only painfully slow progress.” So on April 10, Darpa is inviting scientists to a Virginia conference to brainstorm. What will follow are 46 months of development, along with annual “Summer Schools,” bringing in the scientists together with “potential customers” from the private sector and the government.

 

Called “Probabilistic Programming for Advanced Machine Learning,” or PPAML, scientists will be asked to figure out how to “enable new applications that are impossible to conceive of using today’s technology,” while making experts in the field “radically more effective,” according to a recent agency announcement. At the same time, Darpa wants to make the machines simpler and easier for non-experts to build machine-learning applications too.


Via Ashish Umre
ping yan's insight:

isn't machine teaching themselves being the very concept of artificial intelligence? 

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Meet the algorithm that can learn "everything about anything"

Meet the algorithm that can learn "everything about anything" | Data Magic by Ping | Scoop.it
Researchers from Allen Institute for AI have built a computer system capable of teaching itself many facets of broad concepts by scouring and analyzing search engines using natural language processing and computer vision techniques.
ping yan's insight:

imagine that you learned about a concept by reading 10 books, hundreds of thousands of pics and 5 movies. You've surely become a self-taught expert in that concept or field (concept in much more complicated form). 

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Part 3 Whitepaper - How to Approach Implementation : A Practical Guide Big Data Security Analytics

Part 3 Whitepaper - How to Approach Implementation : A Practical Guide Big Data Security Analytics | Data Magic by Ping | Scoop.it
In the last chapter, we talk about how our CyberReveal, a Big Data cyber security analytics capability, can help accelerating the applications of Big Data to cyber security analytics

Via cysap
ping yan's insight:

great stuff as I am working on a whitepaper on the same topic right now. 

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EURASIP Journal on Information Security | Full text | How can sliding HyperLogLog and EWMA detect port scan attacks in IP traffic?

IP networks are constantly targeted by new techniques of denial of service attacks (SYN flooding, port scan, UDP flooding, etc), causing service disruption and considerable financial damage. The on-line detection of DoS attacks in the current high-bit rate IP traffic is a big challenge. We propose in this paper an on-line algorithm for port scan detection. It is composed of two complementary parts: First, a probabilistic counting part, where the number of distinct destination ports is estimated by adapting a method called ‘sliding HyperLogLog’ to the context of port scan in IP traffic. Second, a decisional mechanism is performed on the estimated number of destination ports in order to detect in real time any behavior that could be related to a malicious traffic. This latter part is mainly based on the exponentially weighted moving average algorithm (EWMA) that we adapted to the context of on-line analysis by adding a learning step (supposed without attacks) and improving its update mechanism. The obtained port scan detecting method is tested against real IP traffic containing some attacks. It detects all the port scan attacks within a very short time response (of about 30 s) and without any false positive. The algorithm uses a very small total memory of less than 22 kb and has a very good accuracy on the estimation of the number of destination ports (a relative error of about 3.25%), which is in agreement with the theoretical bounds provided by the sliding HyperLogLog algorithm.
ping yan's insight:

More on hyperloglog http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf

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The Netflix Tech Blog: STAASH - STorage As A Service over Http - A multi-storage abstraction layer

The Netflix Tech Blog: STAASH - STorage As A Service over Http - A multi-storage abstraction layer | Data Magic by Ping | Scoop.it

Netflix’s Astyanax project, and the recipes contained therein, have been a tremendous tool in helping Java developers adopt Apache Cassandra (C*), both within Netflix and outside of Netflix.  A common request we have gotten from non-Java, or non-JVM based (eg: Python, Ruby, BASH, JavaScript), developers is that they would like to take advantage of the recipes provided in Astyanax such as All-Rows-Query or Chunked-Object-Store.

 

STAASH’s short-term goal is to recreate the most popular Astyanax recipes as a service, providing a REST based API to Cassandra. This would allow ANY language to consume those recipes.


Via Nicolas Weil
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Two forthcoming R books

Two forthcoming R books | Data Magic by Ping | Scoop.it

The first is Applied Predictive Modeling by Max Kuhn and Kjell Johnson. Max Kuhn is the author of the caret package, an extremely useful and powerful R package for fitting and optimizing all kinds of predictive models in R. It's available now on Amazon Kindle and will be published in hardcover by Springer in July.

The second is Dynamic Documents with R and knitr by Yihui Xie, the author of the knitr package. With knitr you can easily create beautiful documents and reports, with text, tables and figures all dynamically generated by R. It will also be available in July.

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Two years of stealth and $11M in VC later, EdgeSpring launches a data analytics platform for the average man

Two years of stealth and $11M in VC later, EdgeSpring launches a data analytics platform for the average man | Data Magic by Ping | Scoop.it
It’s not everyday that you hear of a company manages to quietly raise two venture rounds from A-list investors while spending two years developing its technology in stealth mode. But that is exactl...
ping yan's insight:

isn't MS Excel the data analytics flatform for an average man? wondering what is unique about this one. 

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A programmer's guide to big data: 12 tools to know

A programmer's guide to big data: 12 tools to know | Data Magic by Ping | Scoop.it
Whether they’re building big data applications or just trying to gather some insights from their mobile apps, developers have more need than ever for analytics tools.
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Google Uses Reputation To Detect Malicious Downloads

Researchers use data about websites, IP addresses, and domains to detect 99 percent of malicious executables downloaded by users -- outperforming antivirus and URL-reputation services

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10 Big Data Trends From the GigaOM Structure Data Conference - eWeek

10 Big Data Trends From the GigaOM Structure Data Conference - eWeek | Data Magic by Ping | Scoop.it
eWeek
10 Big Data Trends From the GigaOM Structure Data Conference
eWeek
If you are going to implement big data in your enterprise, start with the applications.
ping yan's insight:

very high level, but liked it. 

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