Denial of Service Attacks against Hospitals and Emergency Rooms

One of the most frightening possibilities of cyber warfare is to use remotely deployed , or timed intrusion malware to disturb, distort, deny health care services.

Computer Virus Shuts Down Georgia Hospital

A doctor in an Emergency Room depends on critical information that may save lives if it is electronic and comes on time. However this electronic information can be distorted (which is more severe than deleting it)

The electronic system of a Hospital can also be overwhelmed. If there can be built Stuxnet worms on   nuclear centrifuge systems (like those by Siemens), then the widespread availability of health care systems means these can be reverse engineered for particularly vicious cyber worms.

An example of prime area for targeting is Veterans Administration for veterans of armed forces, but also cyber attacks against electronic health records.

Consider the following data points-

http://threatpost.com/en_us/blogs/dhs-warns-about-threat-mobile-devices-healthcare-051612

May 16, 2012, 9:03AM

DHS’s National Cybersecurity and Communications Integration Center (NCCIC) issued the unclassfied bulletin, “Attack Surface: Healthcare and Public Health Sector” on May 4. In it, DHS warns of a wide range of security risks, including that could expose patient data to malicious attackers, or make hospital networks and first responders subject to disruptive cyber attack

http://publicintelligence.net/nccic-medical-device-cyberattacks/

National Cybersecurity and Communications Integration Center Bulletin

The Healthcare and Public Health (HPH) sector is a multi-trillion dollar industry employing over 13 million personnel, including approximately five million first-responders with at least some emergency medical training, three million registered nurses, and more than 800,000 physicians.

(U) A significant portion of products used in patient care and management including diagnosis and treatment are Medical Devices (MD). These MDs are designed to monitor changes to a patient’s health and may be implanted or external. The Food and Drug Administration (FDA) regulates devices from design to sale and some aspects of the relationship between manufacturers and the MDs after sale. However, the FDA cannot regulate MD use or users, which includes how they are linked to or configured within networks. Typically, modern MDs are not designed to be accessed remotely; instead they are intended to be networked at their point of use. However, the flexibility and scalability of wireless networking makes wireless access a convenient option for organizations deploying MDs within their facilities. This robust sector has led the way with medical based technology options for both patient care and data handling.

(U) The expanded use of wireless technology on the enterprise network of medical facilities and the wireless utilization of MDs opens up both new opportunities and new vulnerabilities to patients and medical facilities. Since wireless MDs are now connected to Medical information technology (IT) networks, IT networks are now remotely accessible through the MD. This may be a desirable development, but the communications security of MDs to protect against theft of medical information and malicious intrusion is now becoming a major concern. In addition, many HPH organizations are leveraging mobile technologies to enhance operations. The storage capacity, fast computing speeds, ease of use, and portability render mobile devices an optimal solution.

(U) This Bulletin highlights how the portability and remote connectivity of MDs introduce additional risk into Medical IT networks and failure to implement a robust security program will impact the organization’s ability to protect patients and their medical information from intentional and unintentional loss or damage.

(U) According to Health and Human Services (HHS), a major concern to the Healthcare and Public Health (HPH) Sector is exploitation of potential vulnerabilities of medical devices on Medical IT networks (public, private and domestic). These vulnerabilities may result in possible risks to patient safety and theft or loss of medical information due to the inadequate incorporation of IT products, patient management products and medical devices onto Medical IT Networks. Misconfigured networks or poor security practices may increase the risk of compromised medical devices. HHS states there are four factors which further complicate security resilience within a medical organization.

1. (U) There are legacy medical devices deployed prior to enactment of the Medical Device Law in 1976, that are still in use today.

2. (U) Many newer devices have undergone rigorous FDA testing procedures and come equipped with design features which facilitate their safe incorporation onto Medical IT networks. However, these secure design features may not be implemented during the deployment phase due to complexity of the technology or the lack of knowledge about the capabilities. Because the technology is so new, there may not be an authoritative understanding of how to properly secure it, leaving open the possibilities for exploitation through zero-day vulnerabilities or insecure deployment configurations. In addition, new or robust features, such as custom applications, may also mean an increased amount of third party code development which may create vulnerabilities, if not evaluated properly. Prior to enactment of the law, the FDA required minimal testing before placing on the market. It is challenging to localize and mitigate threats within this group of legacy equipment.

3. (U) In an era of budgetary restraints, healthcare facilities frequently prioritize more traditional programs and operational considerations over network security.

4. (U) Because these medical devices may contain sensitive or privacy information, system owners may be reluctant to allow manufactures access for upgrades or updates. Failure to install updates lays a foundation for increasingly ineffective threat mitigation as time passes.

(U) Implantable Medical Devices (IMD): Some medical computing devices are designed to be implanted within the body to collect, store, analyze and then act on large amounts of information. These IMDs have incorporated network communications capabilities to increase their usefulness. Legacy implanted medical devices still in use today were manufactured when security was not yet a priority. Some of these devices have older proprietary operating systems that are not vulnerable to common malware and so are not supported by newer antivirus software. However, many are vulnerable to cyber attacks by a malicious actor who can take advantage of routine software update capabilities to gain access and, thereafter, manipulate the implant.

(U) During an August 2011 Black Hat conference, a security researcher demonstrated how an outside actor can shut off or alter the settings of an insulin pump without the user’s knowledge. The demonstration was given to show the audience that the pump’s cyber vulnerabilities could lead to severe consequences. The researcher that provided the demonstration is a diabetic and personally aware of the implications of this activity. The researcher also found that a malicious actor can eavesdrop on a continuous glucose monitor’s (CGM) transmission by using an oscilloscope, but device settings could not be reprogrammed. The researcher acknowledged that he was not able to completely assume remote control or modify the programming of the CGM, but he was able to disrupt and jam the device.

http://www.healthreformwatch.com/category/electronic-medical-records/

February 7, 2012

Since the data breach notification regulations by HHS went into effect in September 2009, 385 incidents affecting 500 or more individuals have been reported to HHS, according to its website.

http://www.darkdaily.com/cyber-attacks-against-internet-enabled-medical-devices-are-new-threat-to-clinical-pathology-laboratories-215#axzz1yPzItOFc

February 16 2011

One high-profile healthcare system that regularly experiences such attacks is the Veterans Administration (VA). For two years, the VA has been fighting a cyber battle against illegal and unwanted intrusions into their medical devices

 

http://www.mobiledia.com/news/120863.html

 DEC 16, 2011
Malware in a Georgia hospital’s computer system forced it to turn away patients, highlighting the problems and vulnerabilities of computerized systems.

The computer infection started to cause problems at the Gwinnett Medical Center last Wednesday and continued to spread, until the hospital was forced to send all non-emergency admissions to other hospitals.

More doctors and nurses than ever are using mobile devices in healthcare, and hospitals are making patient records computerized for easier, convenient access over piles of paperwork.

http://www.doctorsofusc.com/uscdocs/locations/lac-usc-medical-center

As one of the busiest public hospitals in the western United States, LAC+USC Medical Center records nearly 39,000 inpatient discharges, 150,000 emergency department visits, and 1 million ambulatory care visits each year.

http://www.healthreformwatch.com/category/electronic-medical-records/

If one jumbo jet crashed in the US each day for a week, we’d expect the FAA to shut down the industry until the problem was figured out. But in our health care system, roughly 250 people die each day due to preventable error

http://www.pcworld.com/article/142926/are_healthcare_organizations_under_cyberattack.html

Feb 28, 2008

“There is definitely an uptick in attacks,” says Dr. John Halamka, CIO at both Beth Israel Deaconess Medical Center and Harvard Medical School in the Boston area. “Privacy is the foundation of everything we do. We don’t want to be the TJX of healthcare.” TJX is the Framingham, Mass-based retailer which last year disclosed a massive data breach involving customer records.

Dr. Halamka, who this week announced a project in electronic health records as an online service to the 300 doctors in the Beth Israel Deaconess Physicians Organization,

Google Plus API- statistical text mining anyone

For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.

And it would also be  a nice benchmark to do sentiment analysis across multiple social networks.

Some interesting use cases of using Twitter that have been used in R.

  • Using R to search Twitter for analysis
http://www.franklincenterhq.org/2429/using-r-to-search-twitter-for-analysis/
  • Text Data Mining With Twitter And R
  • TWITTER FROM R… SURE, WHY NOT!
  • A package called TwitteR
  • slides from my R tutorial on Twitter text mining #rstats
  • Generating graphs of retweets and @-messages on Twitter using R and Gephi
But with Google Plus API now active

The Console lets you see and manage the following project information:

  • Activated APIs – Activate one or more APIs to enable traffic monitoring, filtering, and billing, and API-specific pages for your project. Read more about activating APIs here.
  • Traffic information – The Console reports traffic information for each activated API. Additionally, you can cap or filter usage by API. Read more about traffic reporting and request filtering here.
  • Billing information – When you activate billing, your activated APIs can exceed the courtesy usage quota. Usage fees are billed to the Google Checkout account that you specify. Read more about billing here.
  • Project keys – Each project is identified by either an API key or an OAuth 2.0 token. Use this key/token in your API requests to identify the project, in order to record usage data, enforce your filtering restrictions, and bill usage to the proper project. You can use the Console to generate or revoke API keys or OAuth 2.0 certificates to use in your application. Read more about keys here.
  • Team members – You can specify additional members with read, write, or ownership access to this project’s Console page. Read more about team members here.
Google+ API Courtesy limit: 1,000 queries/day

Effective limits:

API Per-User Limit Used Courtesy Limit
Google+ API 5.0 requests/second/user 0% 1,000 queries/day
API Calls
Most of the Google+ API follows a RESTful API design, meaning that you use standard HTTP methods to retrieve and manipulate resources. For example, to get the profile of a user, you might send an HTTP request like:

GET https://www.googleapis.com/plus/v1/people/userId

Common Parameters

Different API methods require parameters to be passed either as part of the URL path or as query parameters. Additionally, there are a few parameters that are common to all API endpoints. These are all passed as optional query parameters.

Parameter Name

Value

Description

callback

string

Specifies a JavaScript function that will be passed the response data for using the API with JSONP.

fields

string

Selector specifying which fields to include in a partial response.

key

string

API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.

access_token

string

OAuth 2.0 token for the current user. Learn more about OAuth.

prettyPrint

boolean

If set to “true”, data output will include line breaks and indentation to make it more readable. If set to “false”, unnecessary whitespace is removed, reducing the size of the response. Defaults to “true”.

userIp

string

Identifies the IP address of the end user for whom the API call is being made. This allows per-user quotas to be enforced when calling the API from a server-side application. Learn more about Capping Usage.

Data Formats

Resources in the Google+ API are represented using JSON data formats. For example, retrieving a user’s profile may result in a response like:

{
  "kind": "plus#person",
  "id": "118051310819094153327",
  "displayName": "Chirag Shah",
  "url": "https://plus.google.com/118051310819094153327",
  "image": {
    "url": "https://lh5.googleusercontent.com/-XnZDEoiF09Y/AAAAAAAAAAI/AAAAAAAAYCI/7fow4a2UTMU/photo.jpg"
  }
}

Common Properties

While each type of resource will have its own unique representation, there are a number of common properties that are found in almost all resource representations.

Property Name

Value

Description

displayName

string

This is the name of the resource, suitable for displaying to a user.

id

string

This property uniquely identifies a resource. Every resource of a given kind will have a unique id. Even though an id may sometimes look like a number, it should always be treated as a string.

kind

string

This identifies what kind of resource a JSON object represents. This is particularly useful when programmatically determining how to parse an unknown object.

url

string

This is the primary URL, or permalink, for the resource.

Pagination

In requests that can respond with potentially large collections, such as Activities list, each response contains a limited number of items, set by maxResults(default: 20). Each response also contains a nextPageToken property. To obtain the next page of items, you pass this value of nextPageToken to the pageTokenproperty of the next request. Repeat this process to page through the full collection.

For example, calling Activities list returns a response with nextPageToken:

{
  "kind": "plus#activityFeed",
  "title": "Plus Public Activities Feed",
  "nextPageToken": "CKaEL",
  "items": [
    {
      "kind": "plus#activity",
      "id": "123456789",
      ...
    },
    ...
  ]
  ...
}

To get the next page of activities, pass the value of this token in with your next Activities list request:

https://www.googleapis.com/plus/v1/people/me/activities/public?pageToken=CKaEL

As before, the response to this request includes nextPageToken, which you can pass in to get the next page of results. You can continue this cycle to get new pages — for the last page, “nextPageToken” will be absent.

 

it would be interesting the first wave of analysis on this new social network and see if it is any different from others, if at all.
After all, an API is only as good as the analysis and applications  that can be done on the data it provides

 

A Sacrifice of Statistics

From an advertisement placed by Govt of Pakistan in Wall Street Journal,

Only Pakistan= Making sacrifices statistics cannot reflect.

Oh dear! What would the statisticians say?

Also see http://blogs.wsj.com/indiarealtime/2011/09/13/pakistan-wsj-ad-unlikely-to-change-narrative/

The ad cites a series of statistics. Almost 22,000 Pakistani civilians have died or been seriously injured in the fight against terrorism, the ad said. The army has lost almost 3,000 soldiers. More than 3.5 million people have been displaced by the fighting and the damage to the economy over the past decade is estimated at $68 billion, it added.

People will quibble with these statistics from a country where reporters often find it difficult to get basic data.

 

 

Google Docs Templates

Google Docs has lots of templates but the funny part is they are not well integrated with the individual components, instead you almost have to go to the templates directory first and then to a particular class of document (like presentation)

Within Google Docs presentation, there is no way to go to templates library at https://docs.google.com/templates pictured above

and thats all it shows.

Instead you need to go to the Google Docs homepage and then choose templates. This is slightly opposite to the way people use Office software- you generally decide to use a software and then use a template. Not with Google Docs though- you need to choose template first using either of three methods-

 

 


	

#rstats -Basic Data Manipulation using R

Continuing my series of basic data manipulation using R. For people knowing analytics and
new to R.
1 Keeping only some variables

Using subset we can keep only the variables we want-

Sitka89 <- subset(Sitka89, select=c(size,Time,treat))

Will keep only the variables we have selected (size,Time,treat).

2 Dropping some variables

Harman23.cor$cov.arm.span <- NULL
This deletes the variable named cov.arm.span in the dataset Harman23.cor

3 Keeping records based on character condition

Titanic.sub1<-subset(Titanic,Sex=="Male")

Note the double equal-to sign
4 Keeping records based on date/time condition

subset(DF, as.Date(Date) >= '2009-09-02' & as.Date(Date) <= '2009-09-04')

5 Converting Date Time Formats into other formats

if the variable dob is “01/04/1977) then following will convert into a date object

z=strptime(dob,”%d/%m/%Y”)

and if the same date is 01Apr1977

z=strptime(dob,"%d%b%Y")

6 Difference in Date Time Values and Using Current Time

The difftime function helps in creating differences in two date time variables.

difftime(time1, time2, units='secs')

or

difftime(time1, time2, tz = "", units = c("auto", "secs", "mins", "hours", "days", "weeks"))

For current system date time values you can use

Sys.time()

Sys.Date()

This value can be put in the difftime function shown above to calculate age or time elapsed.

7 Keeping records based on numerical condition

Titanic.sub1<-subset(Titanic,Freq >37)

For enhanced usage-
you can also use the R Commander GUI with the sub menu Data > Active Dataset

8 Sorting Data

Sorting A Data Frame in Ascending Order by a variable

AggregatedData<- sort(AggregatedData, by=~ Package)

Sorting a Data Frame in Descending Order by a variable

AggregatedData<- sort(AggregatedData, by=~ -Installed)

9 Transforming a Dataset Structure around a single variable

Using the Reshape2 Package we can use melt and acast functions

library("reshape2")

tDat.m<- melt(tDat)

tDatCast<- acast(tDat.m,Subject~Item)

If we choose not to use Reshape package, we can use the default reshape method in R. Please do note this takes longer processing time for bigger datasets.

df.wide <- reshape(df, idvar="Subject", timevar="Item", direction="wide")

10 Type in Data

Using scan() function we can type in data in a list

11 Using Diff for lags and Cum Sum function forCumulative Sums

We can use the diff function to calculate difference between two successive values of a variable.

Diff(Dataset$X)

Cumsum function helps to give cumulative sum

Cumsum(Dataset$X)

> x=rnorm(10,20) #This gives 10 Randomly distributed numbers with Mean 20

> x

[1] 20.76078 19.21374 18.28483 20.18920 21.65696 19.54178 18.90592 20.67585

[9] 20.02222 18.99311

> diff(x)

[1] -1.5470415 -0.9289122 1.9043664 1.4677589 -2.1151783 -0.6358585 1.7699296

[8] -0.6536232 -1.0291181 >

cumsum(x)

[1] 20.76078 39.97453 58.25936 78.44855 100.10551 119.64728 138.55320

[8] 159.22905 179.25128 198.24438

> diff(x,2) # The diff function can be used as diff(x, lag = 1, differences = 1, ...) where differences is the order of differencing

[1] -2.4759536 0.9754542 3.3721252 -0.6474195 -2.7510368 1.1340711 1.1163064

[8] -1.6827413

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

12 Merging Data

Deducer GUI makes it much simpler to merge datasets. The simplest syntax for a merge statement is

totalDataframeZ <- merge(dataframeX,dataframeY,by=c("AccountId","Region"))

13 Aggregating and group processing of a variable

We can use multiple methods for aggregating and by group processing of variables.
Two functions we explore here are aggregate and Tapply.

Refering to the R Online Manual at
[http://stat.ethz.ch/R-manual/R-patched/library/stats/html/aggregate.html]

## Compute the averages for the variables in 'state.x77', grouped

## according to the region (Northeast, South, North Central, West) that

## each state belongs to

aggregate(state.x77, list(Region = state.region), mean)

Using TApply

## tapply(Summary Variable, Group Variable, Function)

Reference

[http://www.ats.ucla.edu/stat/r/library/advanced_function_r.htm#tapply]

We can also use specialized packages for data manipulation.

For additional By-group processing you can see the doBy package as well as Plyr package
 for data manipulation.Doby contains a variety of utilities including:
 1) Facilities for groupwise computations of summary statistics and other facilities for working with grouped data.
 2) General linear contrasts and LSMEANS (least-squares-means also known as population means),
 3) HTMLreport for autmatic generation of HTML file from R-script with a minimum of markup, 4) various other utilities and is available at[ http://cran.r-project.org/web/packages/doBy/index.html]
Also Available at [http://cran.r-project.org/web/packages/plyr/index.html],
Plyr is a set of tools that solves a common set of problems:
you need to break a big problem down into manageable pieces,
operate on each pieces and then put all the pieces back together.
 For example, you might want to fit a model to each spatial location or
 time point in your study, summarise data by panels or collapse high-dimensional arrays
 to simpler summary statistics.

Ethics and Writing

A particularly prominent technology blogger ( see http://www.readwriteweb.com/archives/michael_arrington_the_kingmaker_who_would_be_king.php )has now formalized his status as an investor (which he did even before) while relinquishing his editorial duties (which were not much given the blog’s acquisition by AOL and its own formidable line of writers, each one of whom is quite influential). Without going into either sermon mode (thou shall not have conflict of interests) or adulatory mode (wow he sold the blog for 30 mill and now he gets another 20 mill for his funds)- I shall try and present the case for ethics and ethical lapses while as a writer.

Continue reading “Ethics and Writing”

Contest : 2 free passes to Predictive Analytics World

I got some good news from the fine people at Predictive Analytics World.

 you qualify for 2 free passes to the PAW NYC event October 16-20, 2011.  I will be sending you a code to use for registration to receive these passes within the next couple of days.

If you cannot attend our PAW NYC event, please feel free to use these two free passes as a promotional tool within your blog.

Now I have been partnering with PAW for a long time, so it is nice to have free passes. I am grateful for their support of this blog. Therein lies my dilemma. I am in India, and a return ticket from NYC to India costs 1100$. Unless something drastic happens , I dont see myself with that kind of travel money.

Ergo.

I am offering two free passes to Predictive Analytics World . http://predictiveanalyticsworld.com/

All you need to do is – ahem- cough-

  1. like the Facebook Page of Decisionstats.  https://www.facebook.com/pages/Decisionstats/217450141605435 OR
  2.  Add me to a Google circle https://plus.google.com/116302364907696741272/posts OR
  3. Follow me on Twitter https://twitter.com/#!/0_h_r_1

AND


  1. Read one of my poems at my poetry blog at http://poemsforkush.wordpress.com/ and leave a comment with your email id please . It’s a promotion for my next book “Poets and Hackers” due for release in 2 weeks.
The 2 free passes are for any 2 days of the PAW NYC event.  This free pass may not be used for Text Analytics World conference being held the same week.  Please have your Contest winners use the Free Code:  XXXXXXXX.  This code will be good for two uses in registering. 
Thats ‘it. Two free passes , and go for it if you are around NYC in October. NY is a lovely place and I am wearing my red FDNY T shirt as I am typing this.

What do you get?

One of these –http://www.predictiveanalyticsworld.com/newyork/register.php (details awaited!) to

http://www.predictiveanalyticsworld.com/newyork/2011/

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