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httR by Hadley #rstats

October 24, 2012 1:03 pm / Leave a Comment

The awesome Hadley Wickham has just released the next version of httr package. Prof Hadley is currently on leave from Rice Univ and working with the tremendous geeks at R Studio . New things in the httr package-

 

http://blog.rstudio.org/2012/10/14/httr-0-2/

httr, a package designed to make it easy to work with web APIs. Httr is a wrapper around RCurl, and provides:

  • functions for the most important http verbs: GET, HEAD, PATCH, PUT, DELETE and POST.
  • support for OAuth 1.0 and 2.0. Use oauth1.0_token and oauth2.0_token to get user tokens, and sign_oauth1.0 and sign_oauth2.0to sign requests. The demos directory has six demos of using OAuth: three for 1.0 (linkedin, twitter and vimeo) and three for 2.0 (facebook, github, google).

I especially like the OAuth functionality as I occasionaly got flummoxed with existing R OAuth packages , and this should hopefully lead to awesome new social media analytics posts by the larger R blogger community. Also given the fact that unauthenticated API requests to Twitter are greatly expanded by OAuth authenticated requests- (see https://dev.twitter.com/docs/rate-limiting )

  • Unauthenticated calls are permitted 150 requests per hour. Unauthenticated calls are measured against the public facing IP of the server or device making the request.
  • OAuth calls are permitted 350 requests per hour and are measured against the oauth_token used in the request.

 

some creative use cases should see an incredible amount of cross social media analysis (not just one social media channel ) at a time.

R for Social Media Analytics ? Watch this space.. ;)

 

 

Related articles

 

Interview John Myles White , Machine Learning for Hackers

July 24, 2012 6:11 am / 2 Comments

Here is an interview with one of the younger researchers  and rock stars of the R Project, John Myles White,  co-author of Machine Learning for Hackers.

Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?

John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.

We once said that Machine Learning for Hackers  is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning  undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.

Ajay-  What are the key things that a potential reader can learn from this book?

John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.

Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?

John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?

Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?

John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.

The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
About-
John Myles White is a Phd Student in  Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is  the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.

(TIL he has played in several rock bands!)

—–
You can read more in his own words at his blog at http://www.johnmyleswhite.com/about/
He can be contacted via social media at Google Plus at https://plus.google.com/109658960610931658914 or twitter at twitter.com/johnmyleswhite/

Interview Alain Chesnais Chief Scientist Trendspottr.com

July 12, 2012 6:07 pm / Leave a Comment

Here is a brief interview with Alain Chesnais ,Chief Scientist  Trendspottr.com. It is a big honor to interview such a legend in computer science, and I am grateful to both him and Mark Zohar for taking time to write these down.
alain_chesnais2.jpg

Ajay-  Describe your career from your student days to being the President of ACM (Association of Computing Machinery http://www.acm.org/ ). How can we increase  the interest of students in STEM education, particularly in view of the shortage of data scientists.
 
Alain- I’m trying to sum up a career of over 35 years. This may be a bit long winded…
I started my career in CS when I was in high school in the early 70′s. I was accepted in the National Science Foundation’s Science Honors Program in 9th grade and the first course I took was a Fortran programming course at Columbia University. This was on an IBM 360 using punch cards.
The next year my high school got a donation from DEC of a PDP-8E mini computer. I ended up spending a lot of time in the machine room all through high school at a time when access to computers wasn’t all that common. I went to college in Paris and ended up at l’Ecole Normale Supérieure de Cachan in the newly created Computer Science department.
My first job after finishing my graduate studies was as a research assistant at the Centre National de la Recherche Scientifique where I focused my efforts on modelling the behaviour of distributed database systems in the presence of locking. When François Mitterand was elected president of France in 1981, he invited Nicholas Negroponte and Seymour Papert to come to France to set up the Centre Mondial Informatique. I was hired as a researcher there and continued on to become director of software development until it was closed down in 1986. I then started up my own company focusing on distributed computer graphics. We sold the company to Abvent in the early 90′s.
After that, I was hired by Thomson Digital Image to lead their rendering team. We were acquired by Wavefront Technologies in 1993 then by SGI in 1995 and merged with Alias Research. In the merged company: Alias|wavefront, I was director of engineering on the Maya project. Our team received an Oscar in 2003 for the creation of the Maya software system.
Since then I’ve worked at various companies, most recently focusing on social media and Big Data issues associated with it. Mark Zohar and I worked together at SceneCaster in 2007 where we developed a Facebook app that allowed users to create their own 3D scenes and share them with friends via Facebook without requiring a proprietary plugin. In December 2007 it was the most popular app in its category on Facebook.
Recently Mark approached me with a concept related to mining the content of public tweets to determine what was trending in real time. Using math similar to what I had developed during my graduate studies to model the performance of distributed databases in the presence of locking, we built up a real time analytics engine that ranks the content of tweets as they stream in. The math is designed to scale linearly in complexity with the volume of data that we analyze. That is the basis for what we have created for TrendSpottr.
In parallel to my professional career, I have been a very active volunteer at ACM. I started out as a member of the Paris ACM SIGGRAPH chapter in 1985 and volunteered to help do our mailings (snail mail at the time). After taking on more responsibilities with the chapter, I was elected chair of the chapter in 1991. I was first appointed to the SIGGRAPH Local Groups Steering Committee, then became ACM Director for Chapters. Later I was successively elected SIGGRAPH Vice Chair, ACM SIG Governing Board (SGB) Vice Chair for Operations, SGB Chair, ACM SIGGRAPH President, ACM Secretary/Treasurer, ACM Vice President, and finally, in 2010, I was elected ACM President. My term as ACM President has just ended on July 1st. Vint Cerf is our new President. I continue to serve on the ACM Executive Committee in my role as immediate Past President.
(Note- About ACM
ACM, the Association for Computing Machinery www.acm.org, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. )
Ajay- What sets Trendspotter apart from other startups out there in terms of vision in trying to achieve a more coherent experience on the web.
 
Alain- The Basic difference with other approaches that we are aware of is that we have developed an incremental solution that calculates the results on the fly as the data streams in. Our evaluators are based on solid mathematical foundations that have proven their usefulness over time. One way to describe what we do is to think of it as signal processing where the tweets are the signal and our evaluators are like triggers that tell you what elements of the signal have the characteristics that we are filtering for (velocity and acceleration). One key result of using this approach is that our unit cost per tweet analyzed does not go up with increased volume. Using more traditional data analysis approaches involving an implicit sort would imply a complexity of N*log(N), where N is the volume of tweets being analyzed. That would imply that the cost per tweet analyzed would go up with the volume of tweets. Our approach was designed to avoid that, so that we can maintain a cap on our unit costs of analysis, no matter what volume of data we analyze.
Ajay- What do you think is the future of big data visualization going to look like? What are some of the technologies that you are currently bullish on?
Alain- I see several trends that would have deep impact on Big Data visualization. I firmly believe that with large amounts of data, visualization is key tool for understanding both the structure and the relationships that exist between data elements. Let’s focus on some of the key things that are pushing in this direction:
  • the volume of data that is available is growing at a rate we have never seen before. Cisco has measured an 8 fold increase in the volume of IP traffic over the last 5 years and predicts that we will reach the zettabyte of data over IP in 2016
  • more of the data is becoming publicly available. This isn’t only on social networks such as Facebook and twitter, but joins a more general trend involving open research initiatives and open government programs
  • the desired time to get meaningful results is going down dramatically. In the past 5 years we have seen the half life of data on Facebook, defined as the amount of time that half of the public reactions to any given post (likes, shares., comments) take place, go from about 12 hours to under 3 hours currently
  • our access to the net is always on via mobile device. You are always connected.
  • the CPU and GPU capabilities of mobile devices is huge (an iPhone has 10 times the compute power of a Cray-1 and more graphics capabilities than early SGI workstations)
Put all of these observations together and you quickly come up with a massive opportunity to analyze data visually on the go as it happens no matter where you are. We can’t afford to have to wait for results. When something of interest occurs we need to be aware of it immediately.
Ajay- What are some of the applications we could use Trendspottr. Could we predict events like Arab Spring, or even the next viral thing.
 
Alain- TrendSpottr won’t predict what will happen next. What it *will* do is alert you immediately when it happens. You can think of it like a smoke detector. It doesn’t tell that a fire will take place, but it will save your life when a fire does break out.
Typical uses for TrendSpottr are
  • thought leadership by tracking content that your readership is interested in via TrendSpottr you can be seen as a thought leader on the subject by being one of the first to share trending content on a given subject. I personally do this on my Facebook page (http://www.facebook.com/alain.chesnais) and have seen my klout score go up dramatically as a result
  • brand marketing to be able to know when something is trending about your brand and take advantage of it as it happens.
  • competitive analysis to see what is being said about two competing elements. For instance, searching TrendSpottr for “Obama OR Romney” gives you a very good understanding about how social networks are reacting to each politician. You can also do searches like “$aapl OR $msft OR $goog” to get a sense of what is the current buzz for certain hi tech stocks.
  • understanding your impact in real time to be able to see which of the content that you are posting is trending the most on social media so that you can highlight it on your main page. So if all of your content is hosted on common domain name (ourbrand.com), searching for ourbrand.com will show you the most active of your site’s content. That can easily be set up by putting a TrendSpottr widget on your front page

Ajay- What are some of the privacy guidelines that you keep in  mind- given the fact that you collect individual information but also have government agencies as potential users.

 
Alain- We take privacy very seriously and anonymize all of the data that we collect. We don’t keep explicit records of the data we collected through the various incoming streams and only store the aggregate results of our analysis.
About-
Alain Chesnais is immediate Past President of ACM, elected for the two-year term beginning July 1, 2010.Chesnais studied at l’Ecole Normale Supérieure de l’Enseignement Technique and l’Université de Paris where he earned a Maîtrise de Mathematiques, a Maitrise de Structure Mathématique de l’Informatique, and a Diplôme d’Etudes Approfondies in Compuer Science. He was a high school student at the United Nations International School in New York, where, along with preparing his International Baccalaureate with a focus on Math, Physics and Chemistry, he also studied Mandarin Chinese.Chesnais recently founded Visual Transitions, which specializes in helping companies move to HTML 5, the newest standard for structuring and presenting content on the World Wide Web. He was the CTO of SceneCaster.com from June 2007 until April 2010, and was Vice President of Product Development at Tucows Inc. from July 2005 – May 2007. He also served as director of engineering at Alias|Wavefront on the team that received an Oscar from the Academy of Motion Picture Arts and Sciences for developing the Maya 3D software package.

Prior to his election as ACM president, Chesnais was vice president from July 2008 – June 2010 as well as secretary/treasurer from July 2006 – June 2008. He also served as president of ACM SIGGRAPH from July 2002 – June 2005 and as SIG Governing Board Chair from July 2000 – June 2002.

As a French citizen now residing in Canada, he has more than 20 years of management experience in the software industry. He joined the local SIGGRAPH Chapter in Paris some 20 years ago as a volunteer and has continued his involvement with ACM in a variety of leadership capacities since then.

About Trendspottr.com

TrendSpottr is a real-time viral search and predictive analytics service that identifies the most timely and trending information for any topic or keyword. Our core technology analyzes real-time data streams and spots emerging trends at their earliest acceleration point — hours or days before they have become “popular” and reached mainstream awareness.

TrendSpottr serves as a predictive early warning system for news and media organizations, brands, government agencies and Fortune 500 companies and helps them to identify emerging news, events and issues that have high viral potential and market impact. TrendSpottr has partnered with HootSuite, DataSift and other leading social and big data companies.

R for Business Analytics- Book by Ajay Ohri

June 13, 2012 6:45 am / 1 Comment

So the cover art is ready, and if you are a reviewer, you can reserve online copies of the book I have been writing for past 2 years. Special thanks to my mentors, detractors, readers and students- I owe you a beer!

You can also go here-

http://www.springer.com/statistics/book/978-1-4614-4342-1

 

R for Business Analytics

R for Business Analytics

Ohri, Ajay

2012, 2012, XVI, 300 p. 208 illus., 162 in color.

Hardcover
Information

ISBN 978-1-4614-4342-1

Due: September 30, 2012

(net)

approx. 44,95 €
  • Covers full spectrum of R packages related to business analytics
  • Step-by-step instruction on the use of R packages, in addition to exercises, references, interviews and useful links
  • Background information and exercises are all applied to practical business analysis topics, such as code examples on web and social media analytics, data mining, clustering and regression models

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics.

 

This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.

Content Level » Professional/practitioner

Keywords » Business Analytics - Data Mining - Data Visualization - Forecasting - GUI - Graphical User Interface - R software - Text Mining

Related subjects » Business, Economics & Finance - Computational Statistics - Statistics

TABLE OF CONTENTS

Why R.- R Infrastructure.- R Interfaces.- Manipulating Data.- Exploring Data.- Building Regression Models.- Data Mining using R.- Clustering and Data Segmentation.- Forecasting and Time-Series Models.- Data Export and Output.- Optimizing your R Coding.- Additional Training Literature.- Appendix

Possible Digital Disruptions by Cyber Actors in USA Electoral Cycle

June 8, 2012 9:31 am / Leave a Comment

Some possible electronic disruptions  that threaten to disrupt the electoral cycle in United States of America currently underway is-

1) Limited Denial of Service Attacks (like for 5-8 minutes) on fund raising websites, trying to fly under the radar of network administrators to deny the targeted  fundraising website for a small percentage of funds . Money remains critical to the world’s most expensive political market. Even a 5% dropdown in online fund-raising capacity can cripple a candidate.

2)  Limited Man of the Middle  Attacks on ground volunteers to disrupt ,intercept and manipulate communication flows. Basically cyber attacks at vulnerable ground volunteers in critical counties /battleground /swing states (like Florida)

3) Electro-Magnetic Disruptions of Electronic Voting Machines in critical counties /swing states (like Florida) to either disrupt, manipulate or create an impression that some manipulation has been done.

4) Use search engine flooding (for search engine de-optimization of rival candidates keywords), and social media flooding for disrupting the listening capabilities of sentiment analysis.

5) Selected leaks (including using digital means to create authetntic, fake or edited collateral) timed to embarrass rivals or influence voters , this can be geo-coded and mass deployed.

6) using Internet communications to selectively spam or influence independent or opinionated voters through emails, short messaging service , chat channels, social media.

7) Disrupt the Hillary for President 2016 campaign by Anonymous-Wikileak sympathetic hacktivists.

 

 

Analytics for Cyber Conflict

January 30, 2012 7:56 am / 2 Comments

 

The emerging use of Analytics and Knowledge Discovery in Databases for Cyber Conflict and Trade Negotiations

 

The blog post is the first in series or articles on cyber conflict and the use of analytics for targeting in both offense and defense in conflict situations.

 

It covers knowledge discovery in four kinds of databases (so chosen because of perceived importance , sensitivity, criticality and functioning of the geopolitical economic system)-

  1. Databases on Unique Identity Identifiers- including next generation biometric databases connected to Government Initiatives and Banking, and current generation databases of identifiers like government issued documents made online
  2. Databases on financial details -This includes not only traditional financial service providers but also online databases with payment details collected by retail product selling corporates like Sony’s Playstation Network, Microsoft ‘s XBox and
  3. Databases on contact details – including those by offline businesses collecting marketing databases and contact details
  4. Databases on social behavior- primarily collected by online businesses like Facebook , and other social media platforms.

It examines the role of

  1. voluntary privacy safeguards and government regulations ,

  2. weak cryptographic security of databases,

  3. weakness in balancing marketing ( maximized data ) with privacy (minimized data)

  4. and lastly the role of ownership patterns in database owning corporates

A small distinction between cyber crime and cyber conflict is that while cyber crime focusses on stealing data, intellectual property and information  to primarily maximize economic gains

cyber conflict focuses on stealing information and also disrupt effective working of database backed systems in order to gain notional competitive advantages in economics as well as geo-politics. Cyber terrorism is basically cyber conflict by non-state agents or by designated terrorist states as defined by the regulations of the “target” entity. A cyber attack is an offensive action related to cyber-infrastructure (like the Stuxnet worm that disabled uranium enrichment centrifuges of Iran). Cyber attacks and cyber terrorism are out of scope of this paper, we will concentrate on cyber conflicts involving databases.

Some examples are given here-

Types of Knowledge Discovery in -

1) Databases on Unique Identifiers- including biometric databases.

Unique Identifiers or primary keys for identifying people are critical for any intensive knowledge discovery program. The unique identifier generated must be extremely secure , and not liable to reverse engineering of the cryptographic hash function.

For biometric databases, an interesting possibility could be determining the ethnic identity from biometric information, and also mapping relatives. Current biometric information that is collected is- fingerprint data, eyes iris data, facial data. A further feature could be adding in voice data as a part of biometric databases.

This is subject to obvious privacy safeguards.

For example, Google recently unveiled facial recognition to unlock Android 4.0 mobiles, only to find out that the security feature could easily be bypassed by using a photo of the owner.

 

 

Example of Biometric Databases

In Afghanistan more than 2 million Afghans have contributed iris, fingerprint, facial data to a biometric database. In India, 121 million people have already been enrolled in the largest biometric database in the world. More than half a million customers of the Tokyo Mitsubishi Bank are are already using biometric verification at ATMs.

Examples of Breached Online Databases

In 2011, Playstation Network by Sony (PSN) lost data of 77 million customers including personal information and credit card information. Additionally data of 24 million customers were lost by Sony’s Sony Online Entertainment. The websites of open source platforms like SourceForge, WineHQ and Kernel.org were also broken into 2011. Even retailers like McDonald and Walgreen reported database breaches.

 

The role of cyber conflict arises in the following cases-

  1. Databases are online for accessing and authentication by proper users. Databases can be breached remotely by non-owners ( or “perpetrators”) non with much lesser chance of intruder identification, detection and penalization by regulators, or law enforcers (or “protectors”) than offline modes of intellectual property theft.

  2. Databases are valuable to external agents (or “sponsors”) subsidizing ( with finance, technology, information, motivation) the perpetrators for intellectual property theft. Databases contain information that can be used to disrupt the functioning of a particular economy, corporation (or “ primary targets”) or for further chain or domino effects in accessing other data (or “secondary targets”)

  3. Loss of data is more expensive than enhanced cost of security to database owners

  4. Loss of data is more disruptive to people whose data is contained within the database (or “customers”)

So the role play for different people for these kind of databases consists of-

1) Customers- who are in the database

2) Owners -who own the database. They together form the primary and secondary targets.

3) Protectors- who help customers and owners secure the databases.

and

1) Sponsors- who benefit from the theft or disruption of the database

2) Perpetrators- who execute the actual theft and disruption in the database

The use of topic models and LDA is known for making data reduction on text, and the use of data visualization including tied to GPS based location data is well known for investigative purposes, but the increasing complexity of both data generation and the sophistication of machine learning driven data processing makes this an interesting area to watch.

 

 

The next article in this series will cover-

the kind of algorithms that are currently or being proposed for cyber conflict, the role of non state agents , and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Citations-

  1. Michael A. Vatis , CYBER ATTACKS DURING THE WAR ON TERRORISM: A PREDICTIVE ANALYSIS Dartmouth College (Institute for Security Technology Studies).
  2. From Data Mining to Knowledge Discovery in Databases Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyt

C4ISTAR for Hacking and Cyber Conflict

January 24, 2012 6:32 am / 1 Comment

As per http://en.wikipedia.org/wiki/C4ISTAR

C2I stands for command, control, and intelligence.

C3I stands for command, control, communications, and intelligence.

C4I stands for command, control, communications, computers, and (military) intelligence.

C4ISTAR is the British acronym used to represent the group of the military functions designated by C4 (command, control, communications, computers), I (military intelligence), and STAR (surveillance, target acquisition, and reconnaissance) in order to enable the coordination of operations

I increasingly believe that cyber conflict will develop its own terminology and theory and paradigms in due time. In the meantime, it will adopt paradigms from existing military literature and adapt it to the unique sub culture of cyber conflict for both offensive, defensive as well as pre-emptive actions. Here I am theorizing for a case of targeted hacking attacks rather than massive attacks that bring down a website for a few hours and achieve nothing but a few press headlines . I would also theorize on countering such attacks.

So what would be the C4ISTAR for -

1) Media company supporting SOPA/PIPA/Take down Mega Upload-

Command and Control refers to the ability of commanders to direct forces-

This will be the senior executives including the members of board, legal officers, and public relationship/marketing people. Their name is available from corporate websites, and social media scraping can ensure both a list of contact addresses (online) as well as biases for phishing /malware attacks. This could also include phone (flooding or voicemail hacking ) attacks , and attacks against the email server of the company rather than the corporate website.

Communications- This will include all online and social media channels including websites of the media company , but also  those of the press relations firms handling communications , phones,websites- anything which the target is likely to communicate externally (and if possible internal communication)

Timing is everything- coordinating attacks immediately is juevenile, but it might be more mature to attack on vulnerable days like product launches or just before a board of directors meeting

Intelligence-

Most corporates have an in-house research team, they can be easily targeted using social media channels, but also offline research and digging deep. Targeting intelligence corps of the target corporate is likely to produce a much better disruption. Eventually they can be persuaded to stop working for that corporate.

Computers- Anything that runs on electricity and can be disabled – should be disabled. This might require much more creativity than just flooding.

 surveillance-  This can be both online as well as offline, and would be of electronic assets, likely responses for the attack, and the key people who are to be disrupted.

target acquisition-  at least ten people within each corporate can and should be ideally disrupted, rather than just the website. this would call for social media scraping, and prior planning. even email in-boxes can be disrupted (if all else fails)

and reconnaissance-

study your target companies, target employees, and their strategies.

Then segment and prioritize in a list of  matrix of 10  to 10, who is more vulnerable and who is more valuable to attack.

the C4ISTAR for -a hacker activist organization is much more complicated but forensics reveal that most hackers tend to leave a signature style (in terms of computers,operating systems,machine ids,communication, tools, or even port numbers used)

the best defense for a media rich company to prevent hacking attacks is to first identify its own C4ISTAR structure for its digital content strategy and then fortify as well as scrub vulnerabilities (including from online information regarding its own employees)

(to be continued)

http://www.catb.org/~esr/faqs/hacker-howto.html

The Hacker Attitude

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