While genetics is concerned with the observation of specific sections of DNA, genomics is about studying the entire genome of an organism, something that has only become practically possible in recent years. In forensic genetics, which is the technology behind the large national DNA databases being built in several countries including notably UK and USA (Wallace’s outstanding article lucidly exposes many significant issues), investigators compare scene-of-crime samples with database samples by checking if they match, but only on a very small number of specific locations in the genome (e.g. 13 locations according to the CODIS rules). In our paper we explore what might change when forensic analysis moves from genetics to genomics over the next few decades. This is a problem that can only be meaningfully approached from a multi-disciplinary viewpoint and indeed our combined backgrounds cover computer security, bioinformatics and law.

(Image from Wikimedia commons, in turn from NIST.)

Sequencing the first human genome (2003) cost 2.7 billion dollars and took 13 years. The US’s National Human Genome Research Institute has offered over 20 M$ worth of grants towards the goal of driving the cost of whole-genome sequencing down to a thousand dollars. This will enable personalized genomic medicine (e.g. predicting genetic risk of contracting specific diseases) but will also open up a number of ethical and privacy-related problems. Eugenetic abortions, genomic pre-screening as precondition for healthcare (or even just dating…), (mis)use of genomic data for purposes other than that for which it was collected and so forth. In various jurisdictions there exists legislation (such as the recent GINA in the US) that attempts to protect citizens from some of the possible abuses; but how strongly is it enforced? And is it enough? In the forensic context, is the DNA analysis procedure as infallible as we are led to believe? There are many subtleties associated with the interpretation of statistical results; when even professional statisticians disagree, how are the poor jurors expected to reach a fair verdict? Another subtle issue is kin privacy: if the scene-of-crime sample, compared with everyone in the database, partially matches Alice, this may be used as a hint to investigate all her relatives, who aren’t even in the database; indeed, some 1980s murders were recently solved in this way. “This raises compelling policy questions about the balance between collective security and individual privacy” [Bieber, Brenner, Lazer, 2006]. Should a democracy allow such a “driftnet” approach of suspecting and investigating all the innocents in order to catch the guilty?

This is a paper of questions rather than one of solutions. We believe an informed public debate is needed before the expected transition from genetics to genomics takes place. We want to stimulate discussion and therefore we invite you to read the paper, make up your mind and support what you believe are the right answers.

There is another reason why the article got my attention; my manager, Steve Lipner, is one of the few people to have designed and built a TCSEC A1 assured system and lived to tell the tale. None were sold, but they built one!

The NSA-directed project, the Tokeneer ID Station (TIS), involved building a low-defect system that conforms "to the Common Criteria requirements for Evaluation Assurance Level 5 (EAL5)" in a "cost effective manner." I'm all for this, because building high-assurance solutions is not cheap.

There's a paper with more technical detail about the project that is worth a read.

In my opinion, the project is only a science project, an experiment, for the following reasons:

• It's tiny. Weighing in at a little under 10 KLOC.
• It's only a very small portion of a much larger solution which has not been developed using the same rigor. This bit of context makes the solution as a whole moot. Call me cynical, but my question is "can the entire solution be built with same rigor in a ‘cost effective manner'?" Perhaps it can, but that is not what is presented.
• It sits on top of many operating systems (Windows, Mac OS X and Linux) that are not EAL5 certified. So it would be a little like having an EAL5 certified CharMap application running on EAL4 Windows Vista.
• It's written in a subset of Ada called SPARK, and SPARK skills are not common in the marketplace. Interestingly, SPARK makes use of annotations to help drive the static analysis process. While not a total analog, we also recommend Microsoft development teams use annotations (SAL) to help drive the required static analysis process.
• The application has a large number of dependencies that are not part of the project:

In the SDL we call these files ‘giblets' because they are components needed for your application to operate, but they do not belong to your team. Some of the files look old and highly vulnerable, such as libpng-3.dll from 2001! OSVDB lists 23 vulnerabilities since 2002 in libpng!

In summary, the TIS project is very interesting to a small number of important but specialized customers, such as the NSA, for whom this kind of research is critical. I too found it interesting, but the process is far from a set of "secrets to writing secure code" and the tools are certainly not within reach of day-to-day applications and not applicable to developing complete solutions.

The sky is falling! Trip Chowdhry, the analyst with Global Equities Research who claimed Red Hat was ‘rubbish and the entire LAMP stack is potty, too’ published some eye-opening predictions, predominantly negative, about tech business in Silicon Valley. Now Chowdhry claims that “almost every VC funded open-source company is struggling and will run out of money within the next six months.” (Probably not the most unbiased guy about open source) Matt Asay argues that organizations in general are struggling, but open-source companies are not that high on the list. (But are they high on the VC “axe” list??) He notes Alfresco, Pentaho and JasperSoft are some of the players with ‘millions in the bank and growing revenue.’ Asay also says Chowdhry has a responsibility to do real due diligence and not create myths. Take that, Chicken Little! (img from Disney-Clipart)

We’re not as far behind as we thought we were. Google presented the results of a study they conducted about how IPv6- capable “ordinary users” are at the RIPE meeting in Dubai a few weeks ago. Turns out Apple Macs drive IPv6 penetration in the US. Fifty-two percent of all IPv6 users in the U.S. own a Mac and use 6to4 (creating IPv6 addresses from an IPv4 address and tunneling packets) – making the US fifth in the list of countries using IPv6. Russia and France took first and second place with .76 and .65 percent IPv6-enabled traffic . The US is at .45 percent. Worldwide, 0.238 percent of Google users’ systems are IPv6-enabled and prefer to use IPv6 over IPv4.

Obama’s win = Google’s win? Apparently Google CEO Eric Schmidt and President-Elect Obama are very good buddies and “this terrifies Microsoft”. Now competitors are more on guard against Google’s growing empire and popularity. Although Schmidt was mentioned as a possible candidate for the country’s new national CTO position, he said he would not accept the post if asked. I guess that’s one less thing Microsoft has to worry about.

After 26 days, and almost 350 million e-mail messages, only 28 sales resulted -- a conversion rate of well under 0.00001%. Of these, all but one were for male-enhancement products and the average purchase price was close to $100. Taken together, these conversions would have resulted in revenues of $2,731.88 -- a bit over $100 a day for the measurement period or $140 per day for periods when the campaign was active. However, our study interposed on only a small fraction of the overall Storm network -- we estimate roughly 1.5 percent based on the fraction of worker bots we proxy. Thus, the total daily revenue attributable to Storm's pharmacy campaign is likely closer to $7000 (or $9500 during periods of campaign activity). By the same logic, we estimate that Storm self-propagation campaigns can produce between 3500 and 8500 new bots per day.

Under the assumption that our measurements are representative over time (an admittedly dangerous assumption when dealing with such small samples), we can extrapolate that, were it sent continuously at the same rate, Storm-generated pharmaceutical spam would produce roughly 3.5 million dollars of revenue in a year. This number could be even higher if spam-advertised pharmacies experience repeat business. A bit less than "millions of dollars every day," but certainly a healthy enterprise.

Of course, the authors point out that it's dangerous to make these sorts of generalizations:

We would be the first to admit that these results represent a single data point and are not necessarily representative of spam as a whole. Different campaigns, using different tactics and marketing different products will undoubtedly produce different outcomes. Indeed, we caution strongly against researchers using the conversion rates we have measured for these Storm-based campaigns to justify assumptions in any other context.

Spam is all about economics. When sending junk mail costs a dollar in paper, list rental, and postage, a marketer needs a reasonable conversion rate to make the campaign worthwhile. When sending junk mail is almost free, a one in ten million conversion rate is acceptable.

News articles.

UPDATE:  @MYRCURIAL from the great site Liquidmatrix says that I need to post the following warning:

YOU MAY NOT WANT TO PROCESS THIS PRIOR TO YOUR 11TH CUP OF COFFEE

==================================

Carrying on from yesterday’s post a bit, I’m happy to admit that Chris’ poem is right: we don’t have nearly the information we need now when we’re supposed to have “control” over our assets, putting things in a hosted/asp/cloud/buzzword model ain’t going to help our quest for visibility. My intention was/is to show that you need visibility (in part one) and then today explain that unfortunately, that’s only half the picture.

Today’s follow-on is about the fact that whatever visibility we can contractually enforce (be it in the “cloud” or in our own perimeter) has to be informative (Amrit, this is why I was plugging you with those variance questions on Twitter yesterday).  That is, we can ask whatever IT department (ours, theirs, whomever) for all sorts of information, and maybe they’ll even give it to us.  But we’re not really ready to:

• Know what to ask for
• Use it to create wisdom

A really salient example of this from outside IT hit my browser this morning.  Now it’s not at all my intention to be political or endorse one candidate over another.  Those who know me know I’m fiercely independent.  But this morning there’s a headline on a well-read news website about how one candidate is now “+2″ over another in a Gallup poll of “likely voters”. The source is here.

That is a screen grab from Gallup’s website that shows the “+2″.   I have to ask - how informative is this information?  Part of the problem is that Gallup’s methods are hidden as some sort of “secret sauce” (their FAQ section doesn’t help much, either).  But regardless of the quality of the measurement, this “+2″ has no context - we don’t really know what this information means with regards to an actual election.  Nor is there any predictive element (I hate the using the word predictive, but it’s common nomenclature - so there you go).  We don’t have what we need from this Gallup poll to create wisdom about the ability of either candidate to be elected.

Allow me show you what I mean by way of contrast.  Take a look at Nate Silver’s work at http://www.fivethirtyeight.com/.  Now I’ve been long familiar with Nate due to his work in baseball.  He’s been at these sorts of ‘predictive’ analytics around our shared passion: creating wisdom from baseball statistics.

What Nate is doing at 538 is applying that acumen from his baseball work to the political process.  He’s breaking down the vote not just on popularity among likely voters, but in the context of the electoral college, accounting for variance and uncertainty, running Monte Carlo simulations and taking into account all sorts of polling information.  The result is really quite amazing. Here’s just one graph he presents - it’s the most similar to the Gallup one above, but you should really visit the site to understand the difference in quality of information and to check out the predictive elements he creates.

NOT ALL INFORMATION IS CREATED EQUAL, AND NOT ALL  JUDGMENTS ARE CREATED EQUALLY

And take a look at the contrast, here:

On one hand you have Gallup giving us a “+2″ advantage to a particular candidate.  Now Gallup themselves draws no conclusion but, as digested, how many readers do you think take this as evidence that the election is *really* close?

On the other hand, 538’s predictions show a 348/189 electoral college split, and one candidate winning 96% of the time in simulated elections.  That doesn’t seem close at all!

RISK MANAGEMENT

It is these predictive elements that we need in order to make better strategy and decisions.  I’ve been talking in the past about risk management’s inability to link current state to systemic causes, and this “context” is what predictive analytics provide.  We might have all sorts of visibility into our environment, and measurement of various amounts of variability that visibility gives us. But unless we have context to create wisdom, it’s all just, as Chris says, “machinations”.  We have to move beyond “+2″.

So Cloud/Grid/Utility/ASP/TimeShare/Whatever you want to call it - security will have to clean up our own mess first before we can do a good job with or without a perimeter.  Once we can start moving beyond “+2″ statements, then we can know what sort of visibility we require into an ability to Prevent, Detect, and Respond.

According to comments allegedly made by Howard Cox, a US Department of Justice official in a closed-door meeting last week, after being frustrated with the disk encryption employed by Yastremskiy, Turkish law enforcement may have resorted to physical violence to force the password out of the Ukrainian suspect.

Mr Cox's revelation came in the context of a joke made during his speech. While the exact words were not recorded, multiple sources have verified that Cox quipped about leaving a stubborn suspect alone with Turkish police for a week as a way to get them to voluntarily reveal their password. The specifics of the interrogation techniques were not revealed, but all four people I spoke to stated that it was clear that physical coercion was the implied method.

Setting the context for this post, it is a tough economic situation all over the world. IT spending has reduced and will reduce significantly. In one of earlier posts, I had referred to information security as an overhead of an overhead (IT). What is a good approach for security practice in this type of economy?

I don't have a magic wand to pull a rabbit out of a hat. I have always been told that: tough economy is the time for real smart people to make money. Coming back to information security topic, with a bit of common sense, it is wise for information security professionals to offer services in those areas that does not involve capital expenditure. As a Security Manager, you may be already aware that your people are willing to go an extra mile in the current economic times.

- No budget or lack of budget, means no new capital expenditure. Spend time wisely in building a future technology strategy and keep it in the back pocket when the economy turns around.

- This is a good time to create roles/responsibilities and ownership for various areas. Create operating procedures. Make your team to automate tasks. This will help your operations become more efficient.

- This is time for security awareness  education. Create pamphlets/brochures/presentations for an online or classroom training. Engage your and your team's time to impart training.

- Leverage already invested technology platforms. Leverage utilized features that reduce costs. If you have already invested in technology such as VMware, this is the time to get the best out of it. You can use VMware's toolkit to build your lab and staging environment and optimize on hardware cost.

- Off shoring has been the mantra of senior executives, this is the time to revisit those services and measure their performance closely and assess your satisfaction level. This is a good time to build a case for not off shoring if it makes sense.

- Companies are more vulnerable in bad economic times. You are in a better position to influence senior management about information security risks under these circumstances and drive home the value of protecting your intellectual property under these kinds of circumstances. management will be all ears for such a pitch.

- Time to engage your architect to optimize your security architecture, revisit standards and optimize design for cost efficiency.

- Revisit various controls and see if there are some risks that you could optimize spending on.

- Training budget is an unfortunate victim of this type of economy. Encourage employees to take free webinars offered by various security vendors and encourage them to share the summary across the team. This will put your employees in touch with latest happenings in security at the same time there is some learning that is imparted despite zero training budget.

- Since there are very few projects in action, this is a good time to have conversations with cross functional teams and educate them about your services and solicit feedback on how to do better.

- Revisit your vendor logistics and identify whether you can renegotiate some of your already existing contracts.

The above are some good ways by which you can optimize costs, this will also enhance your team's competence level in the long run. And this approach is better than letting people go, if you can pull this.

Sounds like the beginning of a joke, right?  So these two guys walk into a bar…

“The” Bruce Schneier and Marcus Ranum have an article up on TechTarget/Information Security Magazine called, creatively enough, “Bruce Schenier, Marcus Ranum debate risk management“.

Unfortunately, to get to the article, you’ll have to either already be a subscriber to IT Security, a subscriber to TechTarget, or go through the 20 minute process of signing up by giving TechTarget all sorts of “market information” about how you’re really Brandon Walsh, CSO of “The Peach Pit” Industries in Beverly Hills, CA 90210 (phone 714-867-5309).

For those of you who are already a TechTarget person, the link is above.  For those who aren’t, or those who just don’t have the time, I’ll summarize.  The “debate” is kind of awkward because both authors seem come to the same conclusion:

Risk Management, it’s something our profession should do, something humans do naturally, it’s necessary in business, but gosh - we don’t have enough data.

I’m not a cryptographer.  I don’t *nearly* have the insight on privacy and politics that Bruce has.  I’m not deep in IP communications.  I haven’t got a proven track record of innovation in IP Security products like Marcus has.  But here’s the thing, I hope you’ll never hear me pretend that I have the skill set to speak authoritatively on those subjects.  Heck, I wouldn’t claim to be a “risk” expert because I have a some insight into my shortcomings and what is needed to tackle such a complex problem.  But such a tepid article on something that (at least I think) is so important kind of, well, confuses me.

Why is it such a boring article?  I’m not sure.  Maybe because they’re just two guys who would rather debate the merits of specific controls or control activities (after all, their penetration testing debate was a huge success), but there’s no new information in the “debate”.  It’s the same old “insurance companies know risk because they have scads of data and we don’t have that” complaint. You know what?  I’m tired of hearing that line, so let’s talk about it.

HOW DO YOU KNOW WE DON’T HAVE THE AMOUNT OF DATA WE NEED TO DO RISK MANAGEMENT WELL?

Not particularly picking on Marcus, but in the article he uses the common complaint, “We lack the data to do risk management well.”  This mantra is repeated to the point where I’m blase’ about it.  But for some reason, this sentence really jumped out at me this time for two reasons.  It made me ask:

1.)  How do you know we don’t have the proper amount of data?

2.)  Can we even define “well” (i.e. what “good” risk management is) yet?

I really don’t know that the industry, especially concerning IT risk, is mature enough to really conclude that we don’t know (in the case of the former), nor that we can define (latter), conclusively.

PLAYING THE CONTRARIAN

Just because I’m feeling kind of zany this morning, let me suggest something.  Maybe there actually is lots of evidence out there for us to use.  Maybe:

1.)  It’s just that we don’t have particularly good models that provide context.

2.)  When that evidence isn’t an obvious phenomena that lends itself to easy measurement, we throw our hands up in disgust and fall back on “lack of data”, “can’t quantify risk”, “best practices work just fine” or any other number of arguments, no, excuses we use to justify our inability to be precise about the past (more or less the present or future - apologies to Niels Bohr).

IT’S IN THE WAY THAT YOU USE IT

Now I actually am happy to acknowledge that we don’t have enough data to be precise.  You, me, even smart guys like Marcus and Bruce - we’ll never be able to “engineer” risk management.  But you know what?  Neither can Insurance companies.  Sure, there are plenty of places where they have enough data to apply a traditional frequentist approach to risk valuations.   But there are plenty of times Insurers actually insure and they don’t have centuries or decades of data.  There are plenty of times when they rely on the “estimates” of subject matter experts.  There are many times they have enough information to be accurate rather than precise, and that’s good enough for them.

For that matter, it’s worth noting that there are plenty of scientific disciplines that have to deal in imprecise prior information, or evidence that’s fraught with uncertainty (what Ranum calls “squishy”, and what I’ve heard real honest to goodness physicists call “noisy”).  Unfortunately, we’re going to be like them.  Until we can read minds and predict the future, there will always be uncertainty in our measurements and posterior conclusions.  The trick is in how you deal with it and express it.  And while I really don’t know how much time Marcus or Bruce have really spent in the deep end on the subject of risk and its management - I have seen people doing brilliant things around risk (though they just aren’t mainstream).  Whether the tools are Bayesian methods, Monte Carlo engines, reductionist models of complex problems, there are risk analysts trying to deal with the problem.  These analysts are applying scientific method(s) and developing reasonable approaches to a very complex problem.  There are people trying, and our body of knowledge is growing, growing well beyond “gee, I haven’t got an obvious solution so I’ll blame it on lack of data”.  Heck, I’ve seen readers of this blog suggest Douglas Hubbard’s book in other security forums!*

I’VE GOT YOUR DATA RIGHT HERE…

But we don’t have enough data?  I have to ask, how much more do we need?  I mean crikey, JPMC just visited our ISSA chapter claiming, like, a bajillion events an hour.  There’s not one, but several companies out there that will want to tell you about how they have deep “insight” into the attacker community.  The boundaries of IT Risk losses are pretty well established by events that happen to public companies.  We have pretty mature testing/assessment tools and methodologies now that help us test our ability to resist the force an attacker can apply to us.  So what part of the Threat Landscape, Asset (Controls) Landscape, or Loss Magnitude landscape is too incomplete (and what are you doing to find the information you need)?

SO WHY DO WE FAIL?

Which brings me to a final, somewhat depressing conclusion.  Maybe there’s data, and maybe we’re starting to see the means to use it.  But in the end I do have to agree with Marcus that the vast majority of the infosec world *is* doing a really, really bad job with regards to “risk” and “risk management”.  The majority of people I know consider GRC to be a cruel, expensive joke.  Risk Assessment Methodologies tend to be built on the faulty premise that if we create a repeatable process, our measurements and conclusions will magically become accurate and wise.  Risk models tend to be factors loosely measured by ordinal scales and then somehow “multiplied” together to create a relatively meaningless qualitative value.  The State of the Union here is not good.  But after reading such a superficial treatment of an important and complex subject, I am left wondering if Bruce and Marcus were the right people to write about risk management in a mainstream publication.  As Inspector Callahan says, “A man’s got to know his limitations.”

===============================

* Speaking of which, if you want to do one cost effective thing to address your uncertainty - go find Douglas Hubbard’s book. It’s even got a nice recommendation from Peter Tippett.  The book is called “How To Measure Anything” - the title sounds rather hyperbolic, but there are good techniques in it we can use to identify useful information and refine our ability to frame that qualitative information into quantitative values. The key is how Hubbard has you deal with your uncertainty.  For those of you who are more scientific minded and want to dig deep into the subject, I have on good authority that E.T. Jaynes “Probability Theory, The Logic of Science” is a rather under appreciated work.

"Risk management" is just a fancy term for the cost-benefit tradeoff associated with any security decision. It's what we do when we react to fear, or try to make ourselves feel secure. It's the fight-or-flight reflex that evolved in primitive fish and remains in all vertebrates. It's instinctual, intuitive and fundamental to life, and one of the brain's primary functions.

Some have hypothesized that humans have a "risk thermostat" that tries to maintain some optimal risk level. It explains why we drive our motorcycles faster when we wear a helmet, or are more likely to take up smoking during wartime. It's our natural risk management in action.

The problem is our brains are intuitively suited to the sorts of risk management decisions endemic to living in small family groups in the East African highlands in 100,000 BC, and not to living in the New York City of 2008. We make

systematic risk management mistakes -- miscalculating the probability of rare events, reacting more to stories than data, responding to the feeling of security rather than reality, and making decisions based on irrelevant context. And that risk cockpit of ours? It's not nearly as finely tuned as we might like it to be.

Like a rabbit that responds to an oncoming car with its default predator avoidance behavior -- dart left, dart right, dart left, and at the last moment jump -- instead of just getting out of the way, our Stone Age intuition doesn't serve us well in a modern technological society. So when we in the security industry use the term "risk management," we don't want you to do it by trusting your gut. We want you to do risk management consciously and intelligently, to analyze the tradeoff and make the best decision.

This means balancing the costs and benefits of any security decision -- buying and installing a new technology, implementing a new procedure or forgoing a common precaution. It means allocating a security budget to mitigate different risks by different amounts. It means buying insurance to transfer some risks to others. It's what businesses do, all the time, about everything. IT security has its own risk management decisions, based on the threats and the technologies.

There's never just one risk, of course, and bad risk management decisions often carry an underlying tradeoff. Terrorism policy in the U.S. is based more on politics than actual security risk, but the politicians who make these decisions are concerned about the risks of not being re-elected.

Many corporate security decisions are made to mitigate the risk of lawsuits rather than address the risk of any actual security breach. And individuals make risk management decisions that consider not only the risks to the corporation, but the risks to their departments' budgets, and to their careers.

You can't completely remove emotion from risk management decisions, but the best way to keep risk management focused on the data is to formalize the methodology. That's what companies that manage risk for a living -- insurance companies, financial trading firms and arbitrageurs -- try to do. They try to replace intuition with models, and hunches with mathematics.

The problem in the security world is we often lack the data to do risk management well. Technological risks are complicated and subtle. We don't know how well our network security will keep the bad guys out, and we don't know the cost to the company if we don't keep them out. And the risks change all the time, making the calculations even harder. But this doesn't mean we shouldn't try.

You can't avoid risk management; it's fundamental to business just as to life. The question is whether you're going to try to use data or whether you're going to just react based on emotions, hunches and anecdotes.

This essay appeared as the first half of a point-counterpoint with Marcus Ranum in Information Security magazine.