Wednesday, June 17, 2020

insight.tech: Avoid IoT Project Failure with Better Simulators

MIMIC IoT Simulator is featured in this Intel insight.tech article

"Proper network simulation is essential to a successful scale-up.
...
Rather than attempting to simulate physical hardware, Gambit simulates
the traffic that IoT devices generate when they communicate across
the network.
...
When end users can model their network and sensors, they can determine
resource requirements before deploying a single device.
  "

Check out MIMIC IoT Simulator on the Intel Marketplace to
get your IoT Simulation on the right track.








Friday, March 13, 2020

MIMIC Simulator: Simulated heterogeneous network

When you want to test your network management application
customizations, nothing beats running against your production network.
But, when you cannot impact your production environment with your
experimentation, you need to run in a lab, with a facsimile of your network.
In that case, an SNMP Simulator like MIMIC Simulator allows you to record
parts of your network to simulate in your lab. Once everything works in the
lab, you can deploy changes on your production network.

For capacity planning or training, you might need sample networks that
represent features that you currently have not deployed, and are planning
to implement. In that case, a MIMIC SNMP Simulator can simulate the
network you need.



Figure 1 - Auvik

MIMIC ships with various sample networks, including this heterogeneous
network which represents a current, multi-vendor, multi-function, multi-site
environment containing many features to be managed.

The network has two interconnected sites of 25 devices in total, namely,
New York City and London. This network has a variety of devices from
different vendors. It contains Routers, Switches, Firewalls, Storage devices,
Wireless Controllers, Wireless Access Points, Phones, Printers and Windows
Servers from vendors Cisco, Juniper, Fortigate, PaloAlto, Aruba, NetApp, HP,
Avaya and Microsoft.

In our lab, the topology is mapped by various common NMS applications,
such as Auvik, Entuity, OpenNMS, Spectrum, etc.



Figure 2 - Entuity

New York has 13 devices starting with Cisco ASR-9000 as Edge router with
2 Firewalls connected to core router Cisco ASR-1000 that connects to the
inside network. London has 12 devices starting with Juniper MX960 as
Edge router with one Firewall connected to core router Juniper T4000 that
connects to the inside network.



Figure 3 - OpenNMS

This network can be customized with all other advanced MIMIC features,
such as
  • random interface statistics,
  • CPU/memory statistics,
  • SNMP link/down for root cause analysis,
  • CBQOS,
  • IPSLA,
  • IOS configuration management
  • NetFlow
  • Topology Wizard
For example, this network was augmented to 1000 agents:


Figure 4 - Augmented network

MIMIC Simulator accelerates testing and training by providing relevant, 
customizable, scalable, dynamic, reproducible network scenarios in your
lab.


Figure 5 - OpsRamp


Monday, January 13, 2020

WebNMS Simulation Toolkit: End of Life

WebNMS has announced end-of-life for the WebNMS Simulation Toolkit as
detailed on their site as of December 31, 2019.

For customers requiring the latest features of an SNMP simulator we offer half off
on an upgrade to the latest version of MIMIC Simulator (ie. 50% discount)
until March 31, 2020.

Take advantage of a first-class simulation environment geared towards large-scale,
custom, rapid development, testing, prototyping, demonstration of network management
applications. For more details, see the videos on our Youtube channel or contact us at
sales@gambitcomm.com .

Wednesday, October 30, 2019

MIMIC MQTT Simulator integrates with Alibaba IoT Platform

We have added MIMIC MQTT Simulator integration with the
Alibaba IoT Platform for getting started, evaluation, development,
testing and proof-of-concept on this platform.

Here a simulated sensor is updating its device shadow:


Thursday, October 17, 2019

Dynamic, real-time, predictable testing of Amazon Greengrass

Efficient testing of Amazon IoT Greengrass with lots of devices is difficult to
achieve, unless you use simulation techniques as everywhere else in
engineering.



We setup a lab of 100 simulated sensors in MIMIC MQTT Simulator
publishing telemetry in real-time to one instance of Greengrass, simulating
an IoT edge scenario where telemetry is processed at the edge, without
needing to go to the cloud. Most of the telemetry is uninteresting, unless an
anomaly occurs, such as a temperature value above a certain threshold.

In this 2-minute Youtube video 10 of those sensors are started, and monitored
by a subscriber application based on NODE-RED. You can see how it
tracks the temperature and light values of the sensors. We dynamically
and predictably create the anomaly in a matter of seconds.



Then we expanded the number of active sensors to 100, but the NODE-RED
application would not easily show the number of sensors on the graph
(even 10 cannot be definitively shown, and 100 hung the app).

So, we wrote a small Python MQTT subscriber client, which monitors each
sensor reported at the Greengrass local shadow, and displays the number
of sensors detected, and whether any of them exceeds the arbitrary
threshold (our anomaly).

This 2-minute Youtube video shows the interesting parts of the setup,
and the successful completion of the test.

When testing with Greengrass, make sure to use MQTT simulation to
verify your application.

Monday, October 14, 2019

Anomaly detection scenario with Google IoT Core

The traditional anomaly detection scenario involves detecting unusual
behavior in a vast sea of normal data. To empirically test your anomaly-
detection IoT application requires you to setup a large environment of
sensors sending data to your Google Pubsub application.
To do this in real-time to verify response time constraints is specially hard
to do.

To demonstrate how simulation can simplify this task, we improved on the
Google Cloud IoT Core End-to-end example tutorial by making 10 lines of
code change to their pubsub Python server code.

The changes merely count the number of devices detected (by their
serial number), and the anomaly is detected if any temperature value
exceeds the threshold, printing a WARN message.

When we start 10 simulated sensors in MIMIC MQTT Simulator configured
to connect to Google IoT Core, they are detected by the listening Pubsub
application. We can then change in real-time the temperature of one of them,
causing the pubsub application to detect the exceeded threshold.

This 1-minute Youtube video shows the entire demo in real-time.

We then used their Python manager client to define 90 more devices for
a total of 100. Then we ran the same scenario again. Notice the lag between
when the event occurred and the detection by the Pubsub application.

Here we show the entire demo in this 1-minute Youtube video



Wednesday, August 28, 2019

Guard your IoT Application against hackers using IoT Simulation

The Internet of Things opens up many vectors for security vulnerabilities
as detailed in RFC 8576.

Vulnerabilities in all stages of a IoT device's life cycle include malware baked in
during manufacturing, or patched while operating by exploiting zero-day
vulnerabilities, specially after the manufacturer's support of the old device is
discontinued (end-of-life). This malware usually causes the IoT device to
deviate from its intended function for some nefarious purpose.

Part of any IoT Testing and Proof of Concept (PoC) includes addressing security
concerns by adding security monitoring solutions to prevent intrusions,
malware, etc in order to prevent the high failure rates of IoT projects.

While preventing malware through authentication, authorization and privacy is
a first defense, the IoT monitoring solution should detect behavior that is not
"normal". A usual test scenario then consists in reproducing cases of IoT devices
that deviate from their expected behavior. Unless you have a lab full of hacked
devices, this is not easy to do.



An IoT Simulator such as MIMIC IoT Simulator is  designed to easily recreate
scenarios meant to test your IoT monitoring solution for common hacking
scenarios, such as misbehaving IoT devices (eg. hacked devices sending unusual
Internet traffic, or accessing unauthorized resources), incorrectly configured
firewall or load-balancing rules, reported common vulnerabilities and exposures
(CVE) such as in this article .

The "normal" behavior of an IoT device can be characterized by the network
traffic it emits and the resources it accesses. Monitoring solutions can learn
this behavior and alert if it deviates from this pattern. MIMIC can control any
simulated device to behave differently at any point in time, and can easily create
different behaviors on demand. Thus, the monitoring solution can be exercised
to prove that it handles certain scenarios, such as higher traffic rates, network
traffic to different destinations and access to restricted resources. Since the
simulator creates reproducible scenarios, it can be part of regression tests
supporting an agile development cycle.

For example, this Youtube video demonstrates complete, dynamic, real-time
control of message generation rates in MIMIC.