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!

Cablevision will ultimately spend about $300m in building a Wi-Fi network exclusively for its customers; 2.4m of these customers qualify to use the service at no cost. There's no pay as you go option, no monthly subscription; you're either a subscriber of theirs, or not. It's a fascinating strategy, because they're leveraging all these dollars as a tool to crack its competitors in the market. With increasing competition from telephone companies that are offering television service, cable companies need to compete on voice, data, and video, as well as well as on mobile offerings. When the network is built, Cablevision can conceivably offer Wi-Fi telephony service, too.

I'm dying to know what the reduced churn rate and increase in subscriptions will be in six months. Given that hotspot access costs $10 to $30 per month depending on the network, Cablevision is delivering something of value. It's great honey for new subscribers and glue to keep current subscribers.

The company is claiming that with this latest activation, they have the largest Wi-Fi network for consumers in the U.S. They're likely correct. The only other public access network of scale that's being used by large numbers is in Minneapolis, and based on what I know about both networks, Cablevision probably deserves bragging rights. The network in Taipei, Taiwan, is likely still larger, but I haven't heard any usage number in nearly two years; at that point, subscription rates were 10 percent of what had been projected.

HANSEI - WHAT IS “RELENTLESS REFLECTION?” - And why we’re talking about it in the context of Risk Analysis.

Recall from yesterday’s post about how I got to thinking about the concept of Hansei-Kaizen, “relentless reflection” and “continuous improvement” and how we might apply that to risk management.  It’s a concept born of Toyota and is, in some way, the foundation for “Lean” production.

Call me biased, but I think that Hansei - the act of ‘relentless reflection’ made structured is the analytical function.  And I hate to debate (post-mortem) the father of Toyota quality success when he says that Hansei is the “check” in Plan/Do/Check/Act, but I think that Hansei also applies to the “Plan” of the P/D/C/A or Deming cycle.

You’ll recall the P/D/C/A cycle can be thought of even as an implementation of Scientific Method, in that it is Observation & Hypothesis Creation (P), Experiment (D), Analysis (Check), and Act (Revise/New Hypothesis, etc…).  Well then as such, the Hypothesis creation involves creating a model or creating an expected outcome for data using the currently accepted model.

So in our industry there is an opportunity for Relentless Reflection in both the Observation and Hypothesis (Plan) creation steps, and the Check step.  We create an estimate for control strength, or probable losses in the context of risk- then we go to Experiment step.  That hypothesis can be put it into production, have an audit, have a penetration test, whatever, in the context of the Do step.  BTW - using Hansei/Analytics in Plan is one way that strong analytical functions can really make penetration testing more useful - as a means to test the estimates and inputs into a model.  It’s Penetration Testing 2.0!  (<- tongue fully in cheek, yes)

Those who are versed in the reasons to merge Six Sigma and Lean together are probably already seeing where I’m going with this today.  But before you think that a simple DMAIC function is all that is needed to create proper “Hansei”, let me encourage you to keep reading.

Now if the analytical function can said to be “reflection”, why must it be relentless?

One word.  Change. There are essentially four separate “landscapes” or sources of change that we face (more on those tomorrow).  But anyone who has tried to manage system compliance, log management or policy exceptions knows that change is possibly the most difficult thing we security professionals must manage.  And when you think about it, there aren’t too many other business functions like information security where significant visibility and insight about the environment is needed for “complete” information (get bullish on Log Management is my recommendation).

HANSEI STEPS ADAPTED TO INFORMATION SECURITY

This is one of those quality control concepts that we can mangle adopt.  At Toyota, Hansei-Kaizen includes the following basic steps:

1. Initial problem perception
2. Clarify the problem
3. Locate area/point of cause
4. Investigate root cause (using an ask why 5 times approach)
5. Countermeasure
6. Evaluate
7. Standardize

Now it’s important to note that part of this includes the concept of Go See For Yourself, called “Gemba“.  Gemba can be translated as “the actual place” or “the place where virtue or truth is found.” At Toyota this might mean going to the shop floor to see the issue at hand in the production line.  But for us, that’s a problem because we live in the virtual world.  There’s usually not much use in hanging out in the wiring closets to try to see the problems.

But if you combine the concept of Gemba with the concept of “Nemawashi” –the process of discussing problems and potential solutions with all those affected- we can forge a similar concept using risk analysis.  That is discussing the issue and the risk associated with an issue (what some people would call “risk management”) with the business/LOB/data owner and let them accept authority and the risk decision.  We, the risk analyst, our goal is simply to perform items 1-5 (presenting countermeasure options that include transferring or accepting risk).  By going to the line of business and involving them, responsibility is shared.  Also, if you structure organizational behavior right, personal risk is transferred!

This sort of approach is also in harmony with concepts like “mutual ownership of problems,” or “genchi genbutsu,” (solving problems at the source instead of behind desks), and the “kaizen mind,” (an unending sense of crisis behind the company’s constant drive to improve).

One of the criticisms I have with the way most people try to implement DMAIC into “Lean”

REQUIREMENTS

Now to get this done, I really see three significant requirements.

1.)  A change in political structure.

2.)  Models that provide consistent, defensible analysis.

3.)  A Quantitative approach.  This means using actual units of measurement (not just amorphous percents, ordinal scales, etc.)  for risk and it’s subsequent factors.  Sure there are times when Q&D qualitative approaches are acceptable, but policy should be to have quantitative analysis whenever and wherever possible.

That last item - the quantitative approach - is really quite important.  And the reasons why will be discussed further in tomorrow’s post:

“What should we be reflecting about? & What is needed for reflection?”

P.S.  Your comments and suggestions, as always, are welcome.

P.P.S  Those who may be familiar with Lean/SixSigma/Kaizen sorts of mashups may be thinking - “hey, an Analytical step is built into SixSigma”.  Well, yes there is some prevision for analytical functions based on statistics, but I find SixSigma geared towards creating a State of Knowledge about operational processes, not towards creating a State of Wisdom for CISO’s around security & risks “big questions”.  In otherwords, the analytical function in DMAIC is in the context of Kaizen, and a different step than “reflective” analytics.

adaptive process management (n.) an element of resource and business process management, adaptive search and event processing. Sometimes referred to as “Level 4” event processing or process refinement.

application concept (n.) a definition of a set of properties that represent the data fields of an application entity. An application concept can describe relationships among themselves. For example, an order concept might have a parent/child relationship with an item concept. A department concept might be related to a purchase requisition concept based on the shared property, department_id. Application concepts can include an application state model.

application state modeler (n.) a UML-compliant application that allows you to model the life cycle of a concept instance — that is, for each instance of a given concept, you can define which states it will pass through and how it will transition from state to state. States have entry actions, exit actions, and conditions, providing precision control over the behavior of an event processing agent. Transitions between states also may have rules. Multiple types of states and transitions maximize the versatility and power of the application state modeler.

derived event (n.) an event that is created as a result of processing one or more other events.

complex event (n.) an event that is a situation-entity abstraction of two or more simple, derived or other complex events.

complex event processing (n.) CEP is a technology for extracting information from message-based systems. CEP is primarily an event processing concept that deals with the task of processing multiple events from an event cloud with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes.

event (n.) a instance of an event definition. It is an immutable object that represents a business activity that happened at a single point in time. Just as one cannot change the fact that a given activity occurred, one cannot change an event — events are immutable.

event aggregation (n.) the aggregation of simple, derived or complex events into higher levels of event abstractions.

event definition (n.) a set of properties related to a given activity that represents an important or interesting change of state in a human, system or computational activity. An event definition includes event properties such as event priority, event time to live (TTL), and a description of the payload, which is comprehensive information related to the activity that occurred. Events expire when the TTL has elapsed, unless the event processing agent has instructions to consume them prior to that time.

event channel (n.) a communications channel in which events are transmitted from event source to event receivers, typically received as electronic messages. Each channel can have multiple destination and. events can be configured to transmit to a default destination. JMS is an example of an event channel.

event cloud (n.) a partially ordered set of events (poset), either bounded or unbounded, where the partial orderings are imposed by the causal, timing and other relationships between the events. Typically an event cloud is created by the events produced by one or more distributed systems. An event cloud may contain many event types, event streams and event channels. The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud.

event-driven (n.) the behavior of a human, system or computational entity whose execution or actuation is in response to events, typically received as electronic messages.

event-driven architecture (n.) an architectural style for distributed computing applications in which some of the components are event-driven and communicate by means of events.

event processing (n.) computing that performs operations on events, including modifying, creating and destroying events.

event-object (n.) an software object that represents an event, generally for the purpose of computer processing, that exhibits both encapsulation, inheritance and polymorphism.

event prediction (n.) computational activity where the impact of events, complex events, and situations caused by events identified, including both opportunity or threat. Sometimes referred to as “Level 2” event processing, impact assessment or predictive analytics.

event pre-processing (n.) computational activity where events are cleansed or normalized to produce semantically understandable data. Sometimes referred to as “Level 0” event processing.

event processing (n.) computational activities on events dealing with the association, correlation, and combination of event data and information from single and multiple event sources to achieve refined identity and situation estimates for observed event objects, and to achieve complete and timely assessments of opportunities, threats, and their significance. Event processing is characterized by continuous refinements of event estimates and assessments and by evaluation of the need for additional sources, or modification of the process itself, to achieve improved results.

event processing agent (n.) an EPA is a computational entity that performs event processing.

event processing network (n.) a set of event processing agents and a set of event channels connecting them.

event properties (n.) data representation of an event, typically by name-value pairs of type string, integer, real, boolean or a complex data type.

event refinement (n.) filter, identify and track events & make initial processing decisions based on association, correlation and state estimation. Sometimes referred to as “Level 1” event, or event-object, track and trace.

event stream (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

event stream processing (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

rule (n.) defines what triggers unusual, suspicious, problematic, or advantageous activity within an event processing agent and what the EPA does when it discovers these types of activities. Rules execute actions based on certain conditions on events, instances, or a combination of both. A rule includes a group of condition-rule statements and action-rule statements. The condition statements instruct the EPA what to look for in events, and action statements instruct the EPA how to respond when conditions are met. If all the conditions in a rule are satisfied by events or instances or both, the EPA fires the actions. The action might be to execute tasks, create an event instance, modify property values in an event instance, create and send an event, or something else.

rules engine (n.) a type of event processing agent that uses a declarative programming model to process events. Formally described as “an abstract structure that describes a formal language precisely, i.e., a set of rules that mathematically delineates a (usually infinite) set of finite-length strings over a (usually finite) alphabet“. Informally, it can be any system that uses rules, in any form, that can be applied to data to produce outcomes.

rule language (n.) is an artificial language that is used to control the behavior of an event processing agent. Rules languages, like human languages, have syntactic and semantic rules to define meaning.

situation refinement (n.) identify situations, or complex events, based on event clustering, event-event relationships and relationship analysis and context. Sometimes referred to as “Level 2” event processing.

simple event (n.) an event that is not an abstraction or composition of other events.

virtual event (n.) an event that is imagined, modeled or simulated.

In particular, SSL certificates (as used to identify https websites) might contain one of these weak keys — and so it would be possible for an attacker to successfully impersonate a secure website. Of course the attacker would need to persuade you to mistakenly visit their site — but it just so happens that one of the more devastating attacks on DNS has recently been discovered; so that’s not as unlikely as it must have seemed back in May.

Anyway, my old friend Ben Laurie (who is with Google these days) and I have been trawling the Internet to determine how many certificates there are containing these weak keys — and there’s a lot: around 1.5% of the certs we’ve examined.

But more of that another day! because earlier this week, Ben spotted that one of the weak certs was for Sun’s “OpenID” website, and that two more OpenID sites were weak as well (by weak we mean that a database lookup could reveal the private key!)

OpenID, for those who are unfamiliar with it, is a scheme for allowing you to prove your identity to site A (viz: provide your user name and password) and then use that identity on site B. There’s a queue of people offering the first bit, but rather less offering the second : because it means you rely on someone else’s due diligence in knowing who their users are — where “who” is a hard sort of thing to get your head around in an online environment.

The problem that Ben and I have identified (advisory here), is that an attacker can poison a DNS cache so it serves up the wrong IP address for openid.sun.com. Then, even if the victim is really cautious and uses https and checks the cert, their credentials can be phished. Thereafter, anyone who trusts Sun as an identity provider could be very disappointed. There’s other attacks as well, but you’ve probably got the general idea by now.

In principle Sun should make a replacement certificate and that should be it (and so they have — read Robin Wilton’s comments here). Except that they need to put the old certificate onto a Certificate Revocation List (CRL) because otherwise it will still be trusted from now until it expires (a fair while off). Sadly, many web browsers, and most of the OpenID codebases haven’t bothered with CRLs (or they don’t enable their checking by default so it’s as if it wasn’t there for most users).

One has to conclude that Sun (and the other two providers) should not be trusted by anyone for quite a while to come. But does that matter ? Since OpenID didn’t promise all that much anyway, does a serious flaw (which does require a certain amount of work to construct an attack) make any difference? At present this looks like the modern equivalent of a small earthquake in Chile.

ScienceLogic: Mike, what’s your background working with network and management system tools?

Mike Lawson: Before joining Apptis, I worked for the Air Force, mainly in satellite communications for almost nine years. I’m probably most familiar with HP OpenView and BMC Remedy. I managed a team that used them but wasn’t involved in tool selection; like many other federal IT workers, we didn’t have a choice of tools because there were existing enterprise licenses and maintenance contracts.

I also saw a large systems integrator do a full Remedy/Crystal Systems/OpenView installation. It took 6 weeks to stand up and customize to meet just the basic monitoring requirements, and it cost something like half a million dollars. At the time, I thought that wasn’t bad and was a pretty typical experience.

ScienceLogic: Coming from where you did, what’s your take on EM7?

Mike Lawson: Honestly, I didn’t believe that EM7 could really do all that it claimed. In many ways, it was the complete opposite of what I had seen first-hand with other monitoring solutions. Could it really cover that much functionality? At relatively much lower cost to the customer and without the licensing nightmare?

That quickly changed when I needed to understand the system enough to run it at a customer’s site. I went back over the training docs I received during my initial training class and jumped in; now, 6 months later, I’m the EM7 expert and can tell you that it delivers on all those promises. (But I still need to show people to get them to believe it too)

I preach the “EM7 gospel” and when anyone wants to talk monitoring, I ask about the universal pain points: cost, maintenance contracts and licensing, and then I explain EM7. The cost difference is real; the solution is based on capacity, so there’s no licensing and it’s easy to use. They are shocked to learn that they can buy multiple EM7 appliances and years of maintenance for what they paid for most other tools.

ScienceLogic: Apptis won the contract for monitoring aboard the USNS Mercy. We love that you’re using EM7 for one of the Navy’s hospital ships. Can you tell us more?

Mike Lawson: The USNS Mercy is a Military Sealift Command hospital ship. Some stats:

• 849 feet long (nearly the size of a football field)
• 12 fully-equipped operating rooms, a 1,000 bed hospital facility, digital radiological services, a diagnostic and clinical laboratory, a pharmacy, an optometry lab, a CAT scan and two oxygen producing plants
• Crew: 61 civilian mariners, 956 Naval medical staff, and 259 Naval support staff

The USNS recently departed on a five-month humanitarian mission in the Western Pacific and Southeast Asia in support of Pacific Partnership 2008. The partnership provides international medical, dental and engineering teams this summer to provide humanitarian support and conduct joint, combined, and cooperative Civil-Military Operations in order to improve regional stability and build partner capacity to respond to natural disasters and pandemic.

For the most part, the ship’s network is self-contained, but can also use a landline when docked. The network covers 400 devices, including Windows/Exchange servers and VMware for server virtualization. Prior to using EM7, none of the monitoring was integrated; each system was independently monitored through individual vendor-specific consoles.

Out of the box, EM7 provided integrated systems, application and network management for all network gear, applications and virtual machines in one solution. We didn’t have to do a lot of customization – EM7 includes best-practice based thresholds, event and monitoring templates and this covered what USNS Mercy needed to monitor.

ScienceLogic: You’re a systems integrator with a very useful “customer point of view” when it comes to looking at tools. From that perspective, can you share what you think are the biggest benefits that EM7 provides?

Mike Lawson: First of all, EM7 stands up right away. We’re talking days, not weeks. In contrast to the lengthy installation of OpenView and Remedy I witnessed during my military career, I was able to configure, customize, and implement the EM7 solution for the USNS Mercy in three days.

Second, it’s easy to train people on and the support is outstanding. This judgment is from first-hand experience. Right before the USNS Mercy departed on its latest voyage, the system administrator I had trained on EM7 left, so I had all of a day to train some new EM7 admins. I prepared a seven-page “cheat sheet” and over a 3-hour conference call, we walked through the entire EM7 solution; I haven’t gotten a support call since.

And when a problem did crop up with a device being discovered incorrectly, ScienceLogic was very responsive. We contacted ScienceLogic support on a Saturday and they created and emailed us a video to help troubleshoot the same day. Within 30 seconds of watching the video, the problem was resolved.

Finally, EM7 helps us be good stewards of the government’s money. This is very important to me personally and to Apptis as a company. Because EM7 is cheaper and deploys so quickly and easily, you might think that it’s just the opposite of what a system integrator would want to use. But that’s short-term thinking. We believe in deliver the most value for customers every time. It’s what creates trust and long-term relationships with our customers. Instead of that half million spent on standing up the solution and basic setup, I’d much rather (and I know the customer would rather) spend that on fine-tuning or extending the solution to do much, much more.

As a former government employee, I know what it’s like to use a tool that doesn’t fit my needs. EM7 proves that the best solution can totally break the old model of costly, lengthy installations. EM7 has the right model: the right solution and the right price delivered as an appliance that is easy to deploy, train on and use.

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Springfield, Mich., puts in its first antennas for a city-wide network: The network is being built with a $750,000 grant from a state development corporation to extend access and improve the business climate. Access will cost $10 per month for residents after an initial free period while the service powers up.