Partial Disclosure - The Good

The responsible disclosure process tends to break down in rare occasions where the vendor doesn’t want to fix the issue. When this occurs, the researcher is put into a difficult position whereby full disclosure could put users’ systems at high risk of compromise. The other case where partial disclosure becomes an alternative is when the researcher has discovered a design flaw in a protocol or underlying multiple vendor component. Examples of this case include the DNS flaws published this past summer by Dan Kaminsky and the TCP denial of service condition discovered by Robert E. Lee and Jack Louis that is currently in the disclosure process. When the flaw affects a very large number of vendors and the actual problem is located within the underlying protocols that support the communications of the Internet as a whole, one possible solution is to follow a partial disclosure model where phasing the details to the general public can be used to encourage adoption and creation of patches throughout the enormous target audience.

Partial Disclosure - The Bad

What is driving the fear surrounding partial disclosure is the potential for abuse. When a major flaw is partially disclosed, a number of potential issues may occur. First and foremost, the further along the partial disclosure path we are, the more details will be released to the public, and the higher the probability that someone (either good or bad intentioned) will figure out the exploit and disclose the details. Second, when partially disclosing, the vendor’s hand is being forced into a situation that could speed up fixes, reduce testing, and cause ripple problems elsewhere within the infrastructure. It is difficult enough to dance the fine time line when doing responsible disclosure, but if we are escalated to the point of partial disclosure, additional fuel is added to the fire.

The Ugly

The real ugly part of partial disclosure is when we add to the equation the ability to spread fear, uncertainty, and doubt into the normal user community. It is generally well accepted that FUD can be used to drive additional revenue. If it is possible to increase the perceived magnitude of the “problem” that your product or service solves, it is possible to directly impact the demand for that product or service. That is the major fear imposed by the growing trend of partial disclosure. By releasing just enough information to trigger wide scale speculation into the flaw, it is possible to create buzz and garner media attention resulting in a lot of speculation and very little hard facts around the issue. The potential for abuse by the security industry at large is enormous.

The Fix

Some have suggested a group of security researchers be convened to vet the requirement of partial disclosure and to allow for independent peer review of any security research that requires the partial disclosure process. This suggestion leaves questions regarding who would stand on this group and who would be impartial enough to ensure that the right thing was always done regardless of profit potential. It also leaves open the opportunity for member researchers to utilize the information gathered during the vetting process to position themselves to profit from the data upon release. It might be wiser to rely on a higher level authority or government entity to manage this process and use the services of security researchers as required for subject matter expertise. While a group of this type wouldn’t ensure that all partial disclosure is appropriate, it would hopefully limit the potential for abuse and the ever present chance that people try to profit from the FUD that surrounds the current partial disclosure process.

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.

This use case is a clear example of a subfunction of complex event processing, folks in the mult-sensor data fusion field (and here at The CEP Blog) refer to as event (object) refinement, sometimes called “track and trace.”

The reason we call this processing function “event (object) refinement” is that, in the way the use case was described in the press release, the medical staff are basically tracking body temperature and comparing it to a key indicator to generate an alarm, in this case “body temperature too high.”   This is a simple event, not complex, because the level of inference is quite very low in an overall knowledge hierarchy.

For example, we cannot infer from the alarm that “body temperature too high” is caused by a previous medical condition.  There is no causality at this stage of the game.   We cannot infer from the alarm that the walker has embarked up a steep hill, and the body temperature is expected to exceed a key indicator for a period of time.

Looking at another complex event model,  the system does not (yet) combine all of the body temperatures of the entire group of walkers, correlated by the situation of an approaching thunderstorm, and infer that the walkers have increased their pace because they don’t want to be caught in a driving rainstorm with high winds.

In other words, tracking a single object like “body temperature” is a basic-step in a CEP application, but not really a CEP application yet, because to really be a complex event, there should be some inference of higher knowledge, or estimated situation.    For example, tracking and tracing the position of an aircraft is good data, but being able to infer the complex situation “potential mid-air crash” between two airplanes is better (defining a complex event vs simply tracking state changes).

Steam processing engines are well suited for track and trace processing of individual event objects, like a walker’s body temperature, or a similar temperature monitoring application from a network device, as demonstrated by the Apama use case.  Tracking events such as “temperature in an object reaches critical threshold” have been going on for decades, in your network, in your car,  in your washing machine, in as spacecraft, just about everywhere we sense-and-respond to temperature changes.

The real marvel of this application was not the event processing on the back end, but in the sensor network, comprised of the human body, an RFID sensor, and a transmission network to a centeralized data collection facility.

We've probably all heard that you can't secure what you can't see and that statement is even more profound when it comes to virtual environments.  This is because it is extremely challenging to see what is going on at a micro vs. macro level within a virtual environments network.  The virtualization vendors such as VMWare and Citrix have provided embedded tools into their management consoles that show a macro level of visibility but its not enough to identify security events in the environment.  Take a look at the attached picture.  It simply shows VMWare's ability to monitor virtual network performance statistics from a bits per second perspective.

<-Click To Enlarge

With only this level of detail how can one determine which network applications are causing spikes.  Is it FTP traffic that is occuring at a high volume at an unuseal time of day?  If that were occuring, could that be indicative of either a breach or some sort of problem? What if FTP isn't even an authorized service in the virtual environment but there is a high volume of it?  Did someone install a rouge FTP service so they could steal information from the server at will?

These types of questions can't really be answered without a micro level of detail into the packets flowing in, out and within the virtual environment.  Now, what I am highlighting is not security in the traditional sense of prevention but using visibility as a means to first identify, then pin point the source of an issue so that it can properly be mitigated.  Having constant visibility can also ensure that other security products in the environment are performing as expected.  What if a Montego HyperSwitch with firewalling enabled is configured with many policies but someone forgot to create an FTP block policy.  One could think they are protected from rouge FTP services transmiting data out of the network, but without constant visibility monitoring, can you be certain?

Some vendors, namely Reflex Security will get you to believe that their IPS / IDS solution that is inline and running in the virtual environment is the right and only approach.  Or they will tell you to hang a virtual IDS off a span port in the virtual environment and you will at least have visibility into the attacks that are taking place.  Well, sure... You now have attack visibility but at the performance cost of your virtual environment.  Signature matching technologies are great, I'm a huge believer; however they don't scale very well in shared computing environments such as virtual ones.  IDS systems also don't typically track protocol and network service (FTP, HTTP, etc.) utilizations; which is another important part of visibility.

So, what do we do to gain visibility without the performance headache?  Well, for starters its probably best to put your IDS/IPS solutions in the physical environment where performance will be less of a concern.  In fact, you can span a virtual switch's traffic out to a physical NIC as easy as you can to a virtual one.  So why do it virtual and have to pay a 60% CPU utilization tax?  Another solution is to IDS inspect only the things you care about.  Why IDS inspect SSL traffic if you know your solution can't unencrypt SSL.  Its just a waste of compute cycles isnt it?  Policy based switching helps you with directing only the things you care about to an IDS (attack visualization product).  Montego's HyperSwitch also can help you with the traffic redirection of only the things you care about.

Another method of visibility which I tend to be a fan of is one of packet analysis (aka NetFlow).  NetFlow was invented by Cisco some time ago and has gained popularity in the physical world and definately has a use in the virtual world.  NetFlow is lightweight.  Let me say that again, its light weight!  It only sends a summation of packet detail to an analytical engine which can do some number crunching, packet comparison, etc. etc. to make some sense out of whats going on.  Lancope, an Atlanta based visibility company that provides Network Visibility, Security Visibility and User Visibility has this tool on their website that is a Netflow Bandwidth calculator.  You'll see from playing with this ( http://www.lancope.com/netflowcalculator.aspx ) calculator that it doesn't consume a lot of network bandwidth to transmit these network accounting records.  It also doesn't cause a lot of CPU overhead to send these records to an analytical engine sitting somewhere in the network.

Lancope's analytical engines have the ability to do the following for you within your virtual environment:

...and probably a slew of other things I'm not aware of.  A screen shot of their product is bellow:

<- Click to enlarge

You'll notice from the screenshot that you are able to visualize who is talking to who, how much traffic they have sent and received and something called a concern index (not seen on this screenshot).

Now, a concern index is a number that increases as Lancopes analytical engines monitor suspicious activity on a session.  A high counter can be indicative of a security problem.  Its another way of identifying (visualizing) compromised hosts (virtual machines) without having to do signature matching like a heavy weight IPS engine.  Example:  Lets say you have a VM that has a BOT on it and is "owned".  The Lancope product is monitoring this long life session.  Let's say that session is established for several hours or maybe even days or months.  Lets also say that the conversation appears to be mostly unidirectional from a public ip address not belonging to your enterprise.  Lancope would increase a the concern index on this since this server hasn't typically had this type of behavior.  Once the concern index reached a certain level it could then fire off an email, send you a text message or something saying:  Warning, Warning, Danger, Danger Will Robinson!!! You're virtual server may be infected with a BOT, please investigate immediately!!!

This example is VISIBILITY which helps you with SECURITY.  There are a number of other things you can do with NetFlow and Lancope products that have less to do with security and more to do with operational efficiencies.  Things like, helping you answer questions of:  How do I know what network applications are taking up the most bandwidth?  When should I move those applications over to a server with more horsepower?  When did these VM's vmotion over here and was there a traffic condition / CPU condition that caused that to occur?  I could go on and on but thats a topic for another blog entry.

So, my suggestion is to take a look at what NetFlow has to offer.  Montego Networks supports NetFlow transmission and Lancope supports NetFlow analytics and with both you can regain what was lost visibility.

I hope this was helpful to you all!

-John Peterson

We've probably all heard that you can't secure what you can't see and that statement is even more profound when it comes to virtual environments.  This is because it is extremely challenging to see what is going on at a micro vs. macro level within a virtual environments network.  The virtualization vendors such as VMWare and Citrix have provided embedded tools into their management consoles that show a macro level of visibility but its not enough to identify security events in the environment.  Take a look at the attached picture.  It simply shows VMWare's ability to monitor virtual network performance statistics from a bits per second perspective.

<-Click To Enlarge

With only this level of detail how can one determine which network applications are causing spikes.  Is it FTP traffic that is occuring at a high volume at an unuseal time of day?  If that were occuring, could that be indicative of either a breach or some sort of problem? What if FTP isn't even an authorized service in the virtual environment but there is a high volume of it?  Did someone install a rouge FTP service so they could steal information from the server at will?

These types of questions can't really be answered without a micro level of detail into the packets flowing in, out and within the virtual environment.  Now, what I am highlighting is not security in the traditional sense of prevention but using visibility as a means to first identify, then pin point the source of an issue so that it can properly be mitigated.  Having constant visibility can also ensure that other security products in the environment are performing as expected.  What if a Montego HyperSwitch with firewalling enabled is configured with many policies but someone forgot to create an FTP block policy.  One could think they are protected from rouge FTP services transmiting data out of the network, but without constant visibility monitoring, can you be certain?

Some vendors, namely Reflex Security will get you to believe that their IPS / IDS solution that is inline and running in the virtual environment is the right and only approach.  Or they will tell you to hang a virtual IDS off a span port in the virtual environment and you will at least have visibility into the attacks that are taking place.  Well, sure... You now have attack visibility but at the performance cost of your virtual environment.  Signature matching technologies are great, I'm a huge believer; however they don't scale very well in shared computing environments such as virtual ones.  IDS systems also don't typically track protocol and network service (FTP, HTTP, etc.) utilizations; which is another important part of visibility.

So, what do we do to gain visibility without the performance headache?  Well, for starters its probably best to put your IDS/IPS solutions in the physical environment where performance will be less of a concern.  In fact, you can span a virtual switch's traffic out to a physical NIC as easy as you can to a virtual one.  So why do it virtual and have to pay a 60% CPU utilization tax?  Another solution is to IDS inspect only the things you care about.  Why IDS inspect SSL traffic if you know your solution can't unencrypt SSL.  Its just a waste of compute cycles isnt it?  Policy based switching helps you with directing only the things you care about to an IDS (attack visualization product).  Montego's HyperSwitch also can help you with the traffic redirection of only the things you care about.

Another method of visibility which I tend to be a fan of is one of packet analysis (aka NetFlow).  NetFlow was invented by Cisco some time ago and has gained popularity in the physical world and definately has a use in the virtual world.  NetFlow is lightweight.  Let me say that again, its light weight!  It only sends a summation of packet detail to an analytical engine which can do some number crunching, packet comparison, etc. etc. to make some sense out of whats going on.  Lancope, an Atlanta based visibility company that provides Network Visibility, Security Visibility and User Visibility has this tool on their website that is a Netflow Bandwidth calculator.  You'll see from playing with this ( http://www.lancope.com/netflowcalculator.aspx ) calculator that it doesn't consume a lot of network bandwidth to transmit these network accounting records.  It also doesn't cause a lot of CPU overhead to send these records to an analytical engine sitting somewhere in the network.

Lancope's analytical engines have the ability to do the following for you within your virtual environment:

...and probably a slew of other things I'm not aware of.  A screen shot of their product is bellow:

<- Click to enlarge

You'll notice from the screenshot that you are able to visualize who is talking to who, how much traffic they have sent and received and something called a concern index (not seen on this screenshot).

Now, a concern index is a number that increases as Lancopes analytical engines monitor suspicious activity on a session.  A high counter can be indicative of a security problem.  Its another way of identifying (visualizing) compromised hosts (virtual machines) without having to do signature matching like a heavy weight IPS engine.  Example:  Lets say you have a VM that has a BOT on it and is "owned".  The Lancope product is monitoring this long life session.  Let's say that session is established for several hours or maybe even days or months.  Lets also say that the conversation appears to be mostly unidirectional from a public ip address not belonging to your enterprise.  Lancope would increase a the concern index on this since this server hasn't typically had this type of behavior.  Once the concern index reached a certain level it could then fire off an email, send you a text message or something saying:  Warning, Warning, Danger, Danger Will Robinson!!! You're virtual server may be infected with a BOT, please investigate immediately!!!

This example is VISIBILITY which helps you with SECURITY.  There are a number of other things you can do with NetFlow and Lancope products that have less to do with security and more to do with operational efficiencies.  Things like, helping you answer questions of:  How do I know what network applications are taking up the most bandwidth?  When should I move those applications over to a server with more horsepower?  When did these VM's vmotion over here and was there a traffic condition / CPU condition that caused that to occur?  I could go on and on but thats a topic for another blog entry.

So, my suggestion is to take a look at what NetFlow has to offer.  Montego Networks supports NetFlow transmission and Lancope supports NetFlow analytics and with both you can regain what was lost visibility.

I hope this was helpful to you all!

-John Peterson

The complainant was a nurse at a Finnish hospital, and also HIV-positive. Word of her condition spread among colleagues, and her contract was not renewed. The hospital’s access controls were not sufficient to prevent colleages accessing her record, and its audit trail was not sufficient to determine who had compromised her privacy. The court’s view was that health care staff who are not involved in the care of a patient must be unable to access that patient’s electronic medical record: “What is required in this connection is practical and effective protection to exclude any possibility of unauthorised access occurring in the first place.” (Press coverage here.)

A “practical and effective” protection test in European law will bind engineering, law and policy much more tightly together. And it will have wide consequences. Privacy compaigners, for example, can now argue strongly that the NHS Care Records service is illegal. And what will be the further consequences for the Transformational Government initiative - the “Database State”?

"There is no research guideline. There is no validated protocol for this. There's not even a clear set of indications for when this is to be used except when people are agitated. By saying that it's done by the emergency medical personnel, they basically are trying to have it both ways. That is, they’re trying to use a medical protocol that is not validated, not for a police function, arrest and detention," Miles said. "The decision to administer Versed is based purely on a paramedic decision, not a police decision," Slovis said. It's up to the officer to call an ambulance and determine if a person is in a condition called excited delirium. "I don't know if I would use the word diagnosing, but they are assessing the situation and saying, 'This person is not acting rationally. This is something I've been trained to recognize, this seems like excited delirium.' I don't view delirium in the field as a police function. It is a medical emergency. We're giving the drug Versed that's routinely used in thousands of health care settings across the country in the field by trained paramedics. I view what we're doing as the best possible medical practice to a medical emergency," Slovis said.