I find that legacy code poses a unique challenge for organizations rolling out a new security process.  Often, the resources dedicated to maintaining older code are a small fraction of those devoted to new features or products.  Furthermore, the original developers for such features have often moved on, leaving no subject matter experts to drive reviews.  The astute reader will ask “How do I apply the principles of the Microsoft SDL to legacy code when I have no development resources and nobody knows how it works?”

The answer is “Start small, and build expertise over time.”

A Rising Tide Lifts All Boats

The best thing a security engineering team can do to improve security in the short term is to drive code quality, and the first step in this process is to define and enforce a secure coding standard.  This helps on two fronts: 

1.       It will improve code quality and reduce implementation flaws across the entire code base.  Unlike other security processes, driving a secure coding standard is relatively easy to accomplish across an entire code base, regardless of the code’s age, by a focused security team.  That is not to say that it is easy without qualification – a large batch of spaghetti code will require a lot of work to untangle!  Such an effort can only be called “easy” when compared to, say, comprehensive identification and remediation of design flaws across legacy features.  Even so, improving code quality through the use of secure coding standards offers a unique combination of high impact, applicability to features, and ability to be carried out by a core team that makes it a sensible first step.

2.       The security team might notice that some sections of code have more standards violations or outright flaws than others.  This is an instance of vulnerability clustering, a concept that has been used to predict vulnerability rates and improve quality in the functional realm.  The evidence is anecdotal, but it stands to reason that portions of code that consistently violate secure coding standards are good places to start looking for other classes of security flaw.  These are security hotspots, and should be high on the prioritized list for further review.

Security testing may also be applied to legacy code, but initial activities should be considered on a case-by-case basis based on the expected return on investment.  Such testing ranges from using inexpensive off-the-shelf tools to exercise common interfaces to rather expensive custom testing and formal analysis.  It is worthwhile to begin with off-the-shelf tools, such as those that target file parsers or web applications, and tools created as part of your greater secure development efforts.  These can help identify easily-found flaws and suggest improvements to the coding standards.  Comprehensive security testing, on the other hand, is best tackled after the Legacy Security Push.

The Legacy Security Push

Coding standards and basic testing provide bang for the buck, but formal security processes seek to provide security assurance.  The challenge for legacy code is that it needs to play catch-up.  Security processes that occur early in the development cycle, such as requirements analysis, design review, and threat modeling, are particularly difficult to achieve years after the fact.  The main goal of the Legacy Security Push is to create the deliverables from these efforts, the most important of which are security requirements and a full risk analysis.

It may sound trivial, but security requirements are essential.  Not only do they define proper operation for the system in question, they also define assumptions that are suitable for relying systems.   It is very common to find security flaws in legacy systems that arise from well-intentioned but incorrect assumptions such as “I assume that the Foo authenticates server Bar when initiating a bank transfer.”  It stands to reason that Foo would do so for such an important activity, but this assumption must be validated.  It is very common for older features to have been deployed in and written for different environments where the security assumptions that are "obvious" today just didn't apply at the time.

When reviewing legacy systems, the first step is to identify such requirements.  If the original architects, developers or managers are available, they can provide valuable insight at this stage.  More often than not this is not the case, and analysis must instead rely on what documentation is present and interaction between the software and its consumers.  The goal is the same as in requirements analysis during project inception, except that in this case one must turn the process on its head and reverse engineer requirements from system behavior.  At the conclusion of this effort, requirements can be theorized – “Foo must authenticate its server Bar before initiating a bank transfer.” 

Risk analysis can be performed once a plausible set of requirements have been identified.  Threat modeling is a more structured means of performing such an analysis, with the eventual goal of identifying means by which requirements can be violated by an attacker. 

As with requirements analysis, original developers would be a valuable resource to consult.  With or without such help, the first step is to identify how the software works.  In many cases, help is not available and performing this task requires a great deal of effort.  For features of moderate size, this author has spent upwards of a month reading code, using process profiling tools, and walking through the software with a debugger to identify program flow and security-sensitive functionality.

Once completed, actual system behavior should be documented and compared against the requirements theorized.   It might be that the requirements should be re-evaluated (New requirement:  Do not assume that Foo requires server authentication) or the system may need to be changed (New bug:   Foo does not verify the CN for Bar).  At the end, this information should be sufficient to support a comprehensive threat modeling exercise where security requirements, risks, and their mitigations can be documented.

Next Steps

Bringing a legacy feature up to par with its newer kin requires a relatively small number of items:  improved code quality, clear security requirements, and a thorough threat model.  As we have seen, performing even these tasks is quite the effort!  I am sure that it is little comfort to be reminded that accomplishing these tasks has simply laid the foundation, and that the true benefit is that the newly-reviewed legacy feature is able to participate fully in the security processes that remain: reviewing cross-component security requirements and assumptions, comprehensive testing, and incident planning, to name a few.

Unfortunately, there is no silver bullet in security assurance.  The soundness of the design and implementation of legacy software is just as important as in newer software, which is why any complete secure software development process will look backwards as well as forwards.  Feature by feature, from higher priority to lower, the overall security of the software improves as legacy code receives the full security treatment it deserves.

1. Great paper that complements the whole "SIEM is dead?" saga - "Most enterprises are looking for a product that will solve all of their problems in some sort of off-the-shelf miracle, and when they find out that the currently available tools can't do it, they either postpone their deployment or put them on the back burner. "
2. "The Mess: looking for someone to blame?" is an awesome piece on Internet security and its architecture - and so is Gunnar's follow-up ("If a tree falls in someone else's silo...")
3. Mike call to "Rise up against Mediocrity."  - "Dilbert makes the risk of the lowest common denominator approach abundantly clear."; in other words, you say 'best practices', I say 'mediocrity!' Mike also remind us, in vain, to do "Security FIRST!" (and compliance second)
4. A great piece from Burton: "On Response" - I think the world needs another 10-20 million reminders that PREVENTION FAILS. This is definitely a good one for those still in the "we'll just block the threat world" - "we will not win a continuing war of escalation" and "using response can be more cost effective than installing the latest and greatest preventative tool"
5. More on metrics, including the highly-awaited ISO27004.
6. Pretty dumb paper by a person confused by why PCI DSS exists (the guy needs to read this). PCI doesn't "fall short," it helps people who will otherwise not do anything and their systems will "power" those botnets of the future...
7. While we are on this subject: a really good coverage of PCI 1.2. changes, released Oct 1st. More PCI fun here. And more here ("PCI Compliance - dispelling some common myths"). And, more PCI myths. And more good ideas on PCI from Mike R. Sorry, can't stop thinking about PCI :-)  - also this is good.
8. Adrian on behavioral monitoring; mostly in DAM, but also elsewhere in security.
9. "Premature Chasm-Crossing"  - a must-read for all security vendors and especially their marketing (and  their easily-excitable PR teams...) - "Shouldn't vendors be spending more time fighting the problems that security managers are facing today, right this minute?" (Mike R also comments on that). A related - and  just as interesting point is made here: "Security is not a solution"
10. More on compliance and security checklists, good and bad: "I think this is a dangerous trend unless the "checklist" is all inclusive." (how can a checklist include ALL? :-))
11. "SANS Top 7 New IR/Forensic Trends In 2008"
12. Read "The three approaches to computer security!"  Why? Come on, it is from Joanna! :-)
13. A fun discussion about a hot new technology: network IDS. Is IDS absolutely indispensable to ALL companies? No. Can it be incredibly useful? You bet. End of discussion.
14. On an unrelated note, are lasers the future of warfare? Some say no.
15. Finally, some security humor from Gartner (!): "Get Rich Quick With Network Security"

Enjoy!

Previous security reading.

When a data owner's (a business) sensitive data is breached it is difficult to quantify the monetary loss. According to respectable survey sources, the average cost of sensitive data breach for a large size company is about $50,000. I am attempting here to think about this in simple mathametical terms:

There is a data breach. From the data owner's perspective the loss is:

Loss = Cost to protect data + Loss of business due to data theft aka cost of competitive disadvantage

From the data thief's perspective

Net Gain= [Cost of producing the data  *  Data freshness factor] - Cost to steal the data + Profit of business due to data aka gain of competitive advantage

From the above two equations it is very clear that this is not a zero sum game. There is a clear cost asymmetry for a data owner and for a data thief. When there is an asymmetry there is an opportunity. Data owner would not even know that the data is lost because the original copy of the data may be still intact - data thief could have simply copied the data. Data theft does not look like a car theft, there is no vacuum left behind. 

This motivates a data thief to keep the cost to steal low, steal highly valuable data that has a long shelf life and in a way that data owner will never even be aware of theft.

From a data thief's perspective, the cost to steal data if kept high would disincentive him. Moreover, Data freshness factor, i.e. how valuable this data is over period of time plays an important role. A good example is content of today's newspaper is hardly valuable tomorrow, but the content of newspaper two days ahead (if can be procured)would be invaluable. Data relevance is a function of time and other marketplace variables -  Data freshness Factor accounts for that variable. A good way to discourage data thief is to increase his/her cost to steal the data. There are other inferences from the above equation. If there exists no competitive advantage with the stolen data, hardly any thief would even venture to steal the data in the first place. If the cost of producing data is very low, then probably thief can just produce the data himself and would not attempt to steal the data. If the cost of theft is kept high, it would definitely deter the data thief from stealing data using technical mechanisms, then the data thief would exploit weak links in data security such as use of social engineering to get access to the data.

From data owner perspective protecting data becomes very important. How much would the owner be willing to spend? Not definitely the cost equal to cost of producing the data. 1% to 10% of cost of producing data is considered prudent. For a data owner it is difficult to estimate cost of data protection of a specific data, because it is not easy to chunkify data protection costs. Moreover, as Dan Geer says in his book, a data owner has to protect himself from number of intruders not just one.

It pays for a data owner to: be aware of data breaches (or data leaks), employ appropriate mechanisms to protect the data; the cost of protection which is fractional cost of the valuable data and enhance information security awareness of personnel who handle the data.

Data loss is not a zero sum game. The advantage is in favor of a data thief (data thieves rather). Data owner does not give much thought on the value of data unless there is a data theft. But, a data thief has every reason to think about economics of data theft before he acts to steal the data else data thief won't survive in this game and he is very well aware of his advantageous position.

Even thought TIBCO’s BusinessEvents does not yet support Bayesian Classifiers, Artificial Neural Networks and other advanced decision support algorithms, it is just a matter of time before TIBCO will add these advanced features “out of the box”.  On the other hand, the extensible nature of TIBCO’s BE makes it possible to add probabalistic computing functionality, however this requires quite a lot of programming and integration work.

When I see a great release like this for TIBCO, it makes me a little nostalgic for “the good old days” travelling the world in the front of the aircraft for TIBCO.   TIBCO has a rich and diverse customer base.  This customer base includes financial services companies; however, TIBCO is much less dependent on financial services than other event processing companies.   So, with TIBCO you not only get great technology, but rock-solid stability in an unstable and uncertain business world.

As a side note, an S&P analyst recently downgraded TIBCO’s stock (TIBX), primarily due to chao in the financial services sector.    Because of TIBCO’s global reach and stability, plus forward vision, advanced technologies and many years of commericial success, the S&P downgrade will create a buying opportunity for TIBCO stock.

Turns out a Petabyte is 1000 Terbytes. So Google processes over 20,000 Terabytes of data per day. Supporting all of this impossibly massive data crunching is a huge network of proprietary servers and custom made storage. It's the mythical Google grid.

Google conceals the exact nature of the grid; it's one of their trade secrets.

So, what if I told you Google is abandoning its mythical, proprietary, custom-made processing and storage grid, and is moving to an off-the-shelf third party processing platform?

Any boffin would have choked on this scoop.

OK, relax. Google isn't ditching its proprietary grid. But its eCrime equivalent is certainly doing exactly that.

A recent article in the London Review of Books revealed that a number of private companies now sell off-the-shelf data-mining solutions to government spies interested in analyzing mobile-phone calling records and real-time location information. These companies include ThorpeGlen, VASTech, Kommlabs, and Aqsacom--all of which sell "passive probing" data-mining services to governments around the world.

ThorpeGlen, a U.K.-based firm, offers intelligence analysts a graphical interface to the company's mobile-phone location and call-record data-mining software. Want to determine a suspect's "community of interest"? Easy. Want to learn if a single person is swapping SIM cards or throwing away phones (yet still hanging out in the same physical location)? No problem.

In a Web demo (PDF) (mirrored here) to potential customers back in May, ThorpeGlen's vice president of global sales showed off the company's tools by mining a dataset of a single week's worth of call data from 50 million users in Indonesia, which it has crunched in order to try and discover small anti-social groups that only call each other.

Jon used a desktop computer attached to a GPS satellite simulator to create a fake GPS signal. Portable GPS satellite simulators can fit in the trunk of a car, and are often used for testing. They are available as commercial off-the-shelf products. You can also rent them for less than $1K a week -- peanuts to anyone thinking of hijacking a cargo truck and selling stolen goods.

In his first experiments, Jon placed his desktop computer and GPS satellite simulator in the cab of his small truck, and powered them off an inverter. The VAT used a second truck as the victim cargo truck. "With this setup," Jon said, "we were able to spoof the GPS receiver from about 30 feet away. If our equipment could broadcast a stronger signal, or if we had purchased stronger signal amplifiers, we certainly could have spoofed over a greater distance."

During later experiments, Jon and the VAT were able to easily achieve much greater GPS spoofing ranges. They spoofed GPS signals at ranges over three quarters of a mile. "The farthest distance we achieved was 4586 feet, at Los Alamos," said Jon. "When you radiate an RF signal, you ideally want line of sight, but in this case we were walking around buildings and near power lines. We really had a lot of obstruction in the way. It surprised us." An attacker could drive within a half mile of the victim truck, and still override the truck's GPS signals.

Marc’s initial shoplifting model in his post is based on John Colapinto’s concepts of matching a pattern of customer movements in the store with their estimated patterns of shoplifting behavioral patterns.    Marc’s asks how Coral8 might address this.   We are not ready to seek a vendor solution.  We do not yet have a workable detection model.

As indicated above, I don’t think the example situation cited by John and Marc is a viable model for automated processing.    Tracking the behavior of customer’s movements, by machine, would require some very sophisticated image processing technology that would be too expensive compared to any possible loss at most retails stores.    This type of behavioral pattern recognition. in retail stores, is performed by people (security personnel), not machines, observing people.  

To develop a machine pattern recognition application to detect retail shoplifting we need to build detection models that are economically feasible.  If we are going to use a model of shoplifting pattern recognition versus anomaly detection, we need to define the objects we must track.  

In the most simple model, we have merchandise-objects.   Stores normally (physically) track merchandise-objects only at the exit/entry points of the store using some electromagnetic proximity detection technology.   In this model, the detection configuration is a combination of simple alerting with humans watching the store (”minding the store”).    This is not complex event processing.

However, if we added another object to our model, the customer-object, then we start to get more “complex,” but we have not defined “complexity” yet because we have not defined the object properties, the possible states of the objects, and the relationships between the objects that are the basis for estimated situations.

Hence, model building is constrained by available resources, simple economics and risk (cost-benefit).  If we are detecting shoplifting in Walmart the cost-benefit model for implementing an automated shoplifting detection system would be different than at a top diamond store on 5th Avenue in NYC.   Protecting loss at a weapons-grade uranium respository follows a different model than protecting loss at a handicraft shop, naturally.

Like Marc, I find models to automatically detect shoplifting interesting, so permit me to close with a general discussion of shoplifting in the context of our CEP/EP reference model.

One approach would be do determine what objects will be represented in our model.   For example, if we are going to track merchandise, we need to model the ”merchandise-object”.  If we are going to track people, we need to define the properties of this “person object.”  If we are going to represent the store layout, we need to define all these objects (store-object, table-object, shelf-object, entry-object and so forth).  The model can get “complex” quite quickly. 

Editorial Note:  An object-oriented approach greatly assists complex model building because we can benefit from OO properties such as encapsulation and polymorphism.  For example, we can define a basic “person object class” and then create superclasses of this object for “customer-object”, “manager-object”, “or criminal-object.”

Generally speaking, each object we define will require a state-model, for example, in Marc’s example of a customer moving around the store, we would need to model the possible states (customer at the entrance, at table 1, at table 2, at shelf 1, in the bathroom, at the cashier, etc.)  Indeed Marc, this is complex event processing if we have modelled multiple objects and defined object-object relationships that indicate situations of interest.   For example, customer-object at table2 where merchandise-object has the property of  ”very expensive, high risk” and then customer-object changes state to “in bathroom”.  Of course, we need more key indicators, but you get the idea.

Right now, I am typing from the Taste from Heaven Vegetarian Restaurant in Chiang Mai and my battery is running low.  The owner of this excellent restaurant also runs the Elephant Nature Park, a non-profit organization advocating and acting on behalf of the rights of the mighty elephants in Thailand.  Would be great if we could also automatically detect the situation of “elephant abuse” by poachers and other crimes against nature.   Time to get back to my delicious mushroom salad, Northeastern Thai style.

As always, thanks for reading, time for me to get back to eating!