Pentest via cellular network (I): Global IoT SIM Card

PENTEST VIA CELLULAR NETWORK (I): GLOBAL IOT SIM CARD

- Layout for this exercise (Smartphone and Raspberry Pi / SIM card / Modem):






1 - Introduction

- The goal of this series of five exercises is to demonstrate how to perform a remote penetration testing (port scanning) with a Raspberry Pi device and a modem using an SMS (Short Message Service) message via a cellular network, instead of an usual Internet connection.

- For that purpose a smartphone communicates with a SIM card incorporated to a modem that is inserted into the USB port of a Raspberry Pi 3 Model B device.

- This project could be applied in scenarios where no reliable connectivity to the Internet is available, since cellular networks ensure connectivity at all times and in any place.

- The project is presented through successive steps, beginning with the introduction of the hardware elements used, and continuing with the applications and programming needed to complete it.

2 - Raspberry Pi 3  with Raspbian Stretch

- As said before, in this exercise a Raspberry Pi 3 Model B device is used, loaded with the operating system Raspbian Stretch:

https://www.raspberrypi.org/products/raspberry-pi-3-model-b/




3 - Hologram Global IoT SIM Card

- Hologram is an USA based company that provides flexible tools for securely connecting IoT devices: 

https://hologram.io


- The Hologram IoT SIM platform sells the Global IoT SIM Card: 

https://hologram.io/store/developer-global-iot-sim-card










































- The Global SIM Card can be bought online at a price of $5 and it is delivered to home via the usual postal service:




- Once the SIM Card is available an online activation is required. 

- To activate the SIM card, first of all a Hologram account must be created:

https://dashboard.hologram.io/account/login

-  Once logged in, from the Dashboard the SIM is activated by first time:

https://dashboard.hologram.io/activate





- The SIM number is written on the back of the card and it must be entered (added) as below:





- Starting with the Developer data plan:






- Taking Zone 1 for the USA:


 

- The cost is $0 for the first month:





- Activating the card:








- The activation takes some minutes until the card reaches the Live status:








- Setting the name whitelist to the device:




- Regarding the Billing, some money should be added to the balance for later buying the number of the phone, that will be necessary for sending/receiving SMS messages:






- After some money is in the balance, a phone number can be purchased at a cheap price of $1:














- A phone number is achieved, linked to the recently activated SIM card:

IBM Bluemix: checking the UltraViolet Index with Raspberry Pi / Sense Hat connected to IBM Weather Company Data

IBM BLUEMIX: CHECKING THE ULTRAVIOLET INDEX WITH RASPBERRY PI / SENSE HAT CONNECTED TO IBM WEATHER COMPANY DATA

- Layout for this exercise:



1 - Creating an instance of Weather Company Data IBM services

- The goal of this exercise is to build an Ultraviolet (UV) rays indicator using a Raspberry Pi device with Sense Hat and NODE-Red accessing the Weather Company Data IBM Bluemix.

- Signing into an IBM Bluemix account:





- Going to Catalog and searching for "Weather" the result is Weather Company Data:





- Weather Company Data is a service that allows the integration with IBM Bluemix:





- This service has a great range of features related with weather forecast, for instance Current Conditions provides data about UV Index:





- Clicking Create:




- Some Service credentials are generated that must be written down for later usage:




- Copying to the Clipboard the Service Credentials:





2 - Creating a NODE-Red flow

- Installing additional NODE-Red nodes necessary for accessing IBM Weather Company Data for Bluemix:




- Starting NODE-Red:




- Now, a NODE-Red flow must be created composed of 6 nodes:

1) Inject



- The payload of the inject node is the geo position (latitude and longitude) of the location of New York City:

http://mygeoposition.com/














- Entering as payload the Latitude and Longitude of NYC:




2) Weather Insights







- Entering Username and Password obtained at point 1 (Service credentials) and Current Observations as a Service:





3) Function







- The function node uses JavaScript code to process the payload coming from the previous node weather insights.

- Editing the node, the conditional statements (if ... else) are based on the Colour Index Scale provided by the United States Environmental Protection Agency.

- Also, when an "off" payload is injected the Sense Hat LED is turned off (we''ll see in next nodes):






https://www.epa.gov/sunsafety/uv-index-scale-1




4) Sense Hat



- This node corresponds to the Sense Hat module, and no special configuration is needed aside from giving a name (LED, because its color varies according to the UV Index):





5) Debug

- The Debug module displays the output obtained from the function and the weather insights nodes:







6) Inject

- The last node injects the string "off" to turn off the Sense Hat LED node:








3 - Testing the system

- Deploying the flow:






- Starting the MyGeoLocation node:






- The flow starts working:





- The LED turns on RED because at the time of the test the UV Index at New York City is closed to 8:




- The Debug tab displays the RED value and the associated New York City's geolocation:




- To check that the test is correct let's consult another UV Index source for New York City:

http://legacy.weather.weatherbug.com








- Finally the node can be turn off: