patching mr robot: mitigating iot-related cyber-social-disasters by getting fog computing to work

getting Fog Computing to work

PATCHING MR. ROBOTMitigating IoT-related Cyber-Social-Disasters by

EUGENE SIOW

A hit TV-Series portraying realistic hacking and bleeding-edge technology

fsociety E CORP

Raspberry Pi Thermostat Hack

PROGRESSION OF HACKS

HVAC Hack

Wipe Debts

Jailbreak

Grand Theft AutoSmart Home Hack

DDOS

72°F

200°F

Smart Home Hack

SMART HOME HACK

WHAT AM I SUPPOSED TO DO?NOTHING IS WORKING

UNPLUG WHAT?EVERYTHING IS INSIDE THE WALLS

INSTEON HACK

NO OR DEFAULT USERNAME & PASSWORD

FROM A NOW DISCONTINUED INSTEON PRODUCT

CIRCUMVENT PASSWORD BY GOING DIRECT TO PORT

E.G. http://ip/dash to http://ip:port/console

REMOTELY SWITCHED LIGHTS OFF

A PASSWORD ON THE PORT-ACCESSED PORTAL THE NEXT DAY

COMPROMISED“ALL YOUR BASE ARE BELONG TO

US”

CALLED AN INSTEON CONSULTANT

HE INSISTED THAT THE PORTAL WAS READ-ONLY AND PASSWORD

PROTECTED FOR ACTUATION

Forbes, 2013

GOOGLED A PHRASE

FOUND A LIST OF ‘SMART HOMES’

FORBES REPORTER

KASHMIR HILL

ACCESSED WEB PORTALCONTROLS FOR LIGHTS, HEATING,

PARENTAL CONTROLS, DOORS

Resource constrained sensors & devices might be and unable to store, process or implement appropriate security.

DEVICE CONSTRAINTS

WHAT’S WRONG WITH THE IOT?An IoT predominantly consisting of device-to-cloud setups

It can be prohibitively expensive to move big data through the Internet and to store it on the cloud.

MOVING & STORING“The IoT suffers from a lack of interoperability… developers are faced with data silos, high costs and limited market potential.” – W3C Web of Things

DATA SILOSCan we trust vendors to keep data private and secure on public clouds? Encrypting the data increases processing required and decreases interoperability.

CLOUD PRIVACY

Internet based transmissions may increase the probability of information leakage.

LARGER AREA FOR LEAKAGESInternet access may be

unavailable, unreliable, and slow e.g. natural disasters, poor infrastructure, remote areas.

CONNECTION ISSUES

APPL

E

GOOG

LE

HONE

YWEL

L

CISC

O

HUAW

EI

GENE

RAL E

LECT

RIC

IBM

AMAZ

ON

INTE

LLET’S TALK FOG COMPUTING

MICR

OSOF

T

A REAL-WORLD

FOG COMPUTINGINFRASTRUCTUREFog Computing utilises the space between the “Ground” and “Cloud”

Irrigation Application

Soil Moisture Analytics

Lightweight Computer Hub

Data Stream

Environmental Sensors

GROUND

National Disaster Monitoring Application

WeatherData

State InclementWeather PlanningApplication

CLOUD

Distributed Queries

OUR RESEARCHBuilding ”Pillars” to support Fog Computing

Sustainable & Secure

INTEROPERABILITY

DISTRIBUTIONEFFICIENCY

Linked Data

Faster Queries

eugenesiow.github.io/iot

INTRODUCING

LINKED DATAFOR INTEROPERABILITY

URI and ontologiesEstablish common data structures & References

ENABLES RICH METADATAwhat, where, WHEN, HOW of DATA

PERFORMANCE CHALLENGESSTORES DON’T SCALE & PERFORM WELL ON WEB YETBuil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action? ISWC 2013

TRAFFIC SENSOR

POLLUTION SENSOR

Semantic Sensor Ontology

EVENTS STREAM

Smart City Ontology

LOCATION

GeoNames Ontology

THE SHAPE OF IOT TIME-SERIES DATA

{timestamp : 1467673132,temperature : {

max: 22.0,min: 15.0,current: 17.0,error: {

percentage: 5.0}

}}

FLAT{

timestamp : 1467673132,temperature : 32.0,wind_speed : 10.5,pressure : 1016

}

COMPLEX

20kUNIQUE DEVICES

dweet.io99.5%FLAT SCHEMATA

0.5%COMPLEX SCHEMATA

1

2,3

4

5

6+

Width

{timestamp : 1467673132,temperature : 32.0,humidity : 10.5,pressure : 1016,light: 120.0,

}

1234

EFFICIENT QUERIES WITH

TIME-SERIESDATA

THING

TEMPERATURE OBS

HUMIDITY OBS

WIND SPEED OBS

13.0

2016-01-01 06:00:00

CELCIUS

93.0

2016-01-01 06:00:00

PERCENT

10.5

2016-01-01 06:00:00

MPH

LOCATION

produces

produces

located

produces

has value

unit

time

RDF GRAPH

Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference

THING

TEMPERATURE OBS

HUMIDITY OBS

WIND SPEED OBS

13.0

LOCATION

produces

produces

located

produces

has value

THING

THING

THING

TEMPERATURE OBS

timeTEMPERATURE OBS 2016-01-01 06:00:00

unitTEMPERATURE OBS celcius

93.0has valueHUMIDITY OBS

timeHUMIDITY OBS 2016-01-01 06:00:00

unitHUMIDITY OBS PERCENT

10.5has valueWIND SPEED OBS

timeWIND SPEED OBS 2016-01-01 06:00:00

unitWIND SPEED OBS MPH


TIME-SERIESDATA

RDF TRIPLES


OURAPPROACH


TIME-SERIESDATA

THING

TEMPERATURE OBS WIND SPEED OBS

CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit

TEMPERATURE HUMIDITY WIND SPEED

13.0 93.0 10.5

TIME

2016-01-01 06:00:00


DESIGNING OUR ENGINE

THING


CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit

TEMPERATURE HUMIDITY WINDSPEED

13.0 93.0 10.5

TIME

2016-01-01 06:00:00

Table1

TABLE1.TEMPERATURE

has value has value

TABLE1.HUMIDITY

has value

TABLE1.WINDSPEED



THING


CELCIUS PERCENT MPH

LOCATION

produces

located

HUMIDITY OBS

unit

TEMPERATURE HUMIDITY WINDSPEED

13.0 93.0 10.5

TIME

2016-01-01 06:00:00

Table1

TABLE1.TEMPERATURE

has value has value

TABLE1.HUMIDITY

has value

TABLE1.WINDSPEED

MAX( )?TEMPERATURESELECT

?OBS TEMPERATURE OBSa

has value?OBS ?TEMPERATURE

has unit?OBS ?uom

{

}


𝞹

𝞬 (max ( ))?TEMPERATURE



has unit?OBS ?uom BGP


TEMPERATURE OBS

CELCIUS

TEMPERATURE

13.0 10.5

TABLE1.TEMPERATURE

has value




has unit?OBS ?uom

{

}

𝞹




has unit?OBS ?uom


BGP

SPARQL





has unit?OBS ?uom

{

}

SELECT

MAX( )?TEMPERATURE

?OBS ?TEMPERATURE ?uom

TABLE1.TEMPERATURE CELCIUSNODE_TEMP

𝞹




has unit?OBS ?uom BGP


SPARQL





has unit?OBS ?uom

{

}

SQL SELECT MAX( )TEMPERATURE FROM TABLE1


EVALUATION WITH BENCHMARKS

SRBENCH

~20,000 Stations

100 – 300k triples

Wind, Rainfall, etc.

10 SRBench Queries

Zhang, Y, et al. (2012) "SRBench: a streaming RDF/SPARQL benchmark.”The 11th International Semantic Web Conference.

SMART HOME BENCH

Siow, E., Tiropanis, T., Hall, W. (2016). "Interoperable and Efficient: Linked Data for the Internet of Things." The 3rd International

Conference on Internet Science.

3 months, 1 home

~30k triples

Motion, energy, environment

4 Analytics Queries

GraphDB (OWLIM)

Ontop

Our Approach (S2S)

TDB

G

Morph

O

S

M

T

STORAGE SIZE

3ookHurricane Ike

1ookNEVADA BLIZZARD

3okSMART HOME

OUR APPROACH (s2S)

TDB

x15

x68

x112

GraphDB x9

x1352

x453

Get the rainfall observed in a particular hour from all stations01

02

SRBENCH QUERY RESULTS

Q01 with an optional clause on unit of measure

x5

S2S

S

TDB GraphDB

Ontop Morph

x3

x13

x4k

x2

x4x4

x5k

03

04

05

Detect if a hurricane has been observed

Get the average wind speed at the stations where the air temperature is >32

Join between wind observation and temperature observation subtrees time-consuming in low resource

environment (Raspberry Pi)

Detect if a station is observing a blizzard

x3

x6

x6

x88

x3

x3

06

07

08

Get the stations with extremely low visibility

Detect stations that are recently broken

Get the daily minimal and maximal air temperature observed by the sensor at a given location

x2

x14

x4

x6

x6x5

x2

09

10

Get the daily average wind force and direction observed by the sensor at a given location

Get the locations where a heavy snowfall has been observed

Our Approach (s2s) is shown to be faster on all queries in the Distributed Meteorological System with SRBench

Join between wind force and wind direction observation subtrees is time-consuming in low resource

environment (Raspberry Pi)

x3

x3k

x2

x7

Temperature aggregated by hour on a specified day01

02

SMART HOME RESULTS

Minimum and maximum temperature each day for a particular month

S2S TDB GraphDB

x7

x29

x3

x9

03

04

Energy Usage Per Room By Day

Diagnose unattended appliances consuming energy with no motion in room

Our Approach (s2s) is shown, once again, to be faster on all queries for Smart Home Analytics

Involves motion and meter data (much larger set), with space-time aggregations and joins between motion and

meter tables/subgraphs.

Involves meter data (larger set), with space-time aggregations.

x69

x13

x4

RDF STREAM PROCESSING

sparql2streamSame engine and

mappings but translates to EPL instead of SQL

TRANSLATE QUERY

2

Stream WindowSPARQL query specifying

stream window size

REGISTER QUERY

1

Stream SocketsSupports multiple

platforms and streams with ZeroMQ

STREAM DATA

3

Real-time analytics

RECEIVE PUSH RESULTS

4

STREAM PROCESSING EFFICIENCY

SMART HOME BENCHSRBench

100 to

106

100 to

200

CQELSPerformance Improvement Over

Le-Phuoc, D., et al. (2011) "A native and adaptive approach for unified processing of linked streams and linked data.” The 10th International Semantic Web Conference.

VELOCITY>99% <1ms latency increasing from 1 to 1000 rows/ms

VOLUME33.5million rows, projected ~2.5 billion triples!

SCALABILITY

PERSONAL IOT REPOSITORY

Siow, E., Tiropanis, T. and Hall, W. (2016) PIOTRe: Personal Internet of Things Repository: The 15th International Semantic Web Conference P&D

github.com/eugenesiow/piotresparql2streamsparql2sql github.com/eugenesiow/sparql2sql

PIOTRE

Apps

sparql2stream sparql2sql

Metadata

FOG RSP

Siow, E., Tiropanis, T. and Hall, W. (2017) A Fog Computing Framework for RDF Stream Processing.

Sensors

Node

Data Stream

Broker

Subscribe(URI_1)

Client

Publish ([Query_p1,Q_p2])𝞹

Push (Select_Stream),Access Control,

Bandwidth Control

Inverted pub-subQuery Broadcast, Nodes manage distributed processing

WORKLOAD DISTRIBUTIONNo single point of failure. Any RPi can serve as a broker. ‘Best effort’ for source nodes

ResultSet

MITIGATING CYBER-SOCIAL DISASTERS

LESS DEPENDENCY

ON CLOUDMORE ROBUST

REPOS FOR FOG COMPUTING

HUMAN STILL VUNERABLE

GOOD UI, SECURITY BY

DEFAULT

What are your latency-sensitive, security/privacy-sensitive, or geographically constrained applications & scenarios?

“Until they become conscious they will never rebel and until after they have rebelled they cannot become conscious.”

1984 by George Orwell

@eugene_siow

patching mr robot: mitigating iot-related cyber-social-disasters by getting fog computing to work

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