The 7 habits of highly successful intelligence analysts

I just love these kinds of lists, that boil things down to the essentials: In a September 11 post in the Digimind blog, Orlaith Finnegan let Monica Nixon of NICS and “Bob A.”, ex Navy intelligence, put down the following 7 habits of highly successful intelligence analysts:

1) Be Organized and Disciplined
2) Communicate with Confidence, Clarity and Credibility
3) Find Meaningful Patterns in Meaningless Noise
4) Adopt a Patient, Methodical Approach
5) See the Bigger Picture
6) Be Flexible and Responsive to Change
7) Learn from Mistakes

For each point in this list, read the full description in the original article:
http://digimind.com/blog/market-industries/the-7-habits-of-highly-successful-intelligence-analysts/

Make your sources talk: elicitation, motivation, provocation… investigative journalists do it too

If you understand Swedish, you must listen to this presentation titled “The ABC of investigative journalism”, by Nils Hanson from Swedish national television (SVT). It was made during the 2012 seminar on the topic of investigative journalism held in Malmö, Sweden, during the week-end of March 23-25. This was the 16:th time Nils Hanson made this presentation.

The interesting thing here is that Nils Hanson represents the community of investigative journalists and reporters, who think of them selves as being among “the good guys”, revealing the truth to the public, uncovering what corrupt politicians hide and even sometimes shedding light on dodgy activities of government intelligence and security organizations.

However, when listening to Nils Hanson, you will hear him describe to his audience of journalists how they should go about in order to make an unwilling human source talk, how they should go about in order to make an unwilling private person agree to becoming the subject of a news story and so on.

If you have government or military intelligence training in the field of HUMINT, you will immediately notice that the methods recommended by Nils Hanson are spot-on similar to the methods used by government and military intelligence operators. The key words are elicitation, motivation, provocation, flattery, favors and favors in return and so on:

– Build trust and rapport by starting out talking about something irrelevant non-sensitive and/or slightly humouristic
– Reduce tension in a situation where the source is refusing to talk by asking for something trivial like a cigarette, and then a match and so on
– Motivate the source to talk by providing gifts without asking for anything in return and by making considerable and noticeable efforts. This will build confidence, and also a sense of indebtedness.
– When a source is refusing to be the subject of a news story or refusing to being interviewed in television, tell the source that full control is with him/her, and start moving in small steps while telling the source that he/she can back out at any time. Having committed to a recorded interview, where several people spent a lot of time, the source will seldom back out and tell them all that their efforts and work have been for nothing.

All of these methods push well-known and simple psychological buttons and leverage mechanisms of human nature such as our reluctance to jump of the band wagon once we have been on it for a while. Normal people have a strong inner voice that talks about commitment, promise, responsibility, duty, gratitude, debt, payback, fairness etc.

I am sure not many of the journalists at the Gräv 2012 seminar would feel comfortable to think of them selves as working with the same toolbox as an intelligence officer managing his human assets.

http://bambuser.com/v/2494983

Ex MI5 Annie Machon become whistleblower talks about disinformation and media manipulation

The Swedish association for investigative journalism today terminated their annual weekend event of seminars and presentations, in Malmö, Sweden: http://www.grav12.se

Among the more colorful  – and from an international perspective more relevant – presentations was the one made by British Annie Machon, ex MI5 operator and whistleblower, currently in involuntary exile. The topic of her talk was disinformation and the manipulation of the media. She talked on Friday, March 23.

She started her talk by mapping up the different bodies of the UK intelligence community (MI5, MI6, GCHQ etc), before going on to describe her way into the MI5, and back out again. She spent six years in the MI5, including 2-year postings in T-branch and G-branch.

Having decided to blow the whistle – partly due to MI6 financing of Libyan terrorists and unjustified MI5 registration of UK citizens – she found herself being hunted pray with UK police, MI5 and MI6 on her trail.

The big take-away of this lecture from an open source intelligence point of view are the challenges related to source credibility and source valuation. Annie Machon testifies about the regular use of agents of influence: people in the media who are on the payroll of the intelligence services, as well as the existence of i-ops departments (i-ops – information operations).  Basically, this is plain and simple a reminder that there is no such thing as an unbiased news article. However, the thing you don’t regularly suspect is that the editor of the paper you are reading has a strong personal bond and strong sympathies with some government intelligence organization with an agenda not necessarily in keeping with the actual truth.

http://bambuser.com/v/2494752

Mathematics is the infrastructure required by all branches of science

Chalmers Technical University in Gothenburg, Sweden, publish a magazine called “Chalmers magasin”, with the purpose of marketing the university.

In issue n:o 1 2012, assistant professor of mathematics Torbjörn Lundh is interviewed. He makes a number of notable statements:

“Mathematics is a support science, the infrastructure required by all branches of science.”

“When people from the industry are asked what the require of recently graduated engineers, they often reply: ‘They should have taken a lot of maths’. What kind of maths? ‘It doesn’t matter’. What they want is the logical thinking, the ability to read struktures and create arguments and models of their own. They are supposed to break new ground”, says Lundh.

“It is hard to tell which kind of mathematics that will be needed in the future. 40 years ago, nobody could foresee that algebraic geometry would become so central to the encryption industry as it is today.”

“The mathematics picked up by the industry [for commerical application] is often ‘old’, not uncommonly one or a couple of hundred years old.”

“Mathematics take a long time to learn and the subject has a long time to maturity before it is applicable in other sciences and in the industry. Therefore there are no shortcuts. If we want sustainable development in scientific research in Sweden we have to start thinking more long-term”, says Lundh. “Other countries have already understood this requirement, like the USA, Germany and South Korea.”

http://www.chalmers.se/sv/om-chalmers/alumni/cm/Documents/CM-1-12webb.pdf

(pages 28-29)

 

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Most of us rely on Wikipedia for step-in level information on all kinds of topics, several times a day, from our computers and smart phones. Wikipedia is free of commercial messages. Wikipedia does not charge you money.

But Wikipedia cannot run on air.

Read this message from Wikipedia founder Jimmy Wales, and consider donating a few dollars per year.

Knowing where the action is, where the crowds are going

We all have our personal, inner conception or mental map of the Internet: which are the important places, where can you find what you are looking for, what are people doing when using the web and so on.

It is safe to say that each of us has a false image – or at least a very far from complete image. We are guided by habit, hazard and home: you are socialized IRL into the understanding and inner image you have of what there is and what is going on – on the world wide web.

Let’s stop for a minute and rethink this. Imagine you heard about the WWW for the first time just now. Someone tells you that it is a network that thousands of millions of people fill with text, images and video 24-7. And searching that content works pretty good thanks to various tools at hand.

If you were to take in that information and assess the possibilities offered by such a source, would you then not want to get a birds-eye view of where the users are hanging out? Which parts of this network that see a lot of activity, that get a lot of attention from the users? Think of it like looking up the number of book volumes in the different topic departments of a library – it is useful to know where there are a lot of sources, and where there are fewer.

Whether you agree or not, here is where you can check out which the top 100, top 1000 and top 1 000 000 websites are based on number of visitors during a month (visitors from the United States):
http://www.quantcast.com/top-sites
As the numbers will tell you, the top 3 sites generate on average 10 times more visitors than sites ranked 101-103, which gives a hint that the distribution of visitors follow a Power Law curve:
http://en.wikipedia.org/wiki/Power_law

One point to consider is also that there is a world outside of the United States, which means that the list would likely change considerably if internet users in India and China were also measured.

So, where people are going when using the Internet is one thing – but there is more.  Royal Pingdom tells you how many the user are, how much content they consume, how much content they create etc etc…

http://royal.pingdom.com/2011/01/12/internet-2010-in-numbers/

http://royal.pingdom.com/2013/01/16/internet-2012-in-numbers/

Learn statistics or stay stupid, misinformed and foolish

Clive Thompson of WIRED published an excellent article on April 19, 2010, on the importance of understanding probability, coincidence, correlation, causation, snap-shot samples versus trendlines, anecdotal information vs statistically valid samples, and that it is just as important as literacy.

I’m quoting some highlights form the text:

“If you don’t understand statistics, you don’t know what’s going on — and you can’t tell when you’re being lied to. Statistics should now be a core part of general education.”

“Of course, as anyone with any exposure to statistics knows, correlation is not causation. And individual stories don’t prove anything; when you examine data on the millions of vaccinated kids, even the correlation vanishes.”

“There are oodles of other examples of how our inability to grasp statistics — and the mother of it all, probability — makes us believe stupid things. Gamblers think their number is more likely to come up this time because it didn’t come up last time. Political polls are touted by the media even when their samples are laughably skewed.”

“Granted, thinking statistically is tricky. We like to construct simple cause-and-effect stories to explain the world as we experience it. “You need to train in this way of thinking. It’s not easy,” says John Allen Paulos, a Temple University mathematician.”

“That’s precisely the point. We often say, rightly, that literacy is crucial to public life: If you can’t write, you can’t think. The same is now true in math. Statistics is the new grammar.”

http://www.wired.com/magazine/2010/04/st_thompson_statistics/

Face-recognition added in Picasa 3.6 – great OSINT processing tool

In release 3.6 of Picasa, Google added the Name Tags functionality. This means they put face recognition logic into Picasa, and added a special tag for specifying person identity by name, in addition to the previously available metadata types Labels and Caption.

So what does this mean? Well, it means that anyone with a personal computer can build a searchable library of portrait photos. Picasa will automatically locate faces in the pictures, and build a library of cropped pictures showing faces only, one by one.

As you identify a face by adding a name to it, Picasa will automatically apply the same name tag to all other pictures where a face has been detected and where the software finds high enough resemblance. This means that as new photos are added later on, Picasa will automatically name tag them, provided that there are previously tagged face images that allow for a comparison and identification to be made.

When Picasa finds a possible but not certain match, the image is tentatively tagged with the name, and you can later press green to confirm or press red to deny.

The face-recognition algorithm in Picasa is more likely to give false positives than to miss anything, in terms of finding faces in pictures. Below are a couple of examples of “false positives”, i.e. parts of images that Picasa suspected might be faces of people, but are not. As you can see, the software is not likely to miss a face where there is one.

So what are the potential use cases? Well, let’s see if we can invent a few.

Use case 1
You are assigned with the mission to collect biographic information including portrait photos of industry specialists and key decision makers at a trade show. While you don’t really know much about who is who when the trade show starts, you can start by taking massive amounts of photos of people and crowds, where ever you see them. Let Picasa index the photos, and list the faces detected in the pictures. As a first step, tag the faces with some code or number with the aim of indicating which face pictures have the same identity. As the trade show goes on, you may increasingly be able to connect names and faces. As you do, you replace the dummy name tag codes with real names. In this way, you will not have wasted any opportunity to take pictures of people just because you didn’t know who they were from the start.

Use case 2
You have a large – 100 000 images – collection of digital photos of people, that are not tagged or indexed in any way. Without being able to search for a name of someone, and as a result see pictures of that person, the photo collection certainly has limited value. Manually evaluating, classifying and tagging each photo – for each of the persons in each photo – is simply not feasible. But if Picasa does the job of framing faces in the pictures and understanding which faces are the same, the situation changes. You will definitly avoid double and tripple work caused by photo duplicates, and you may well find that many faces are automatically identified and name tagged by Picasa once you have identified a few different pictures of the same person.