Download pdf - Mark Horowitz - Stanford Engineering - Securing the Internet of Things

Securing the Internet of Things

Mark Horowitz

Stanford School of Engineering

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Secure Internet of Things

It's Worse Than You Think

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Our Goal

• Embark on a 5-year research project to secure the Internet of Things▶ Collaboration between Stanford, Berkeley, and Michigan

• Rethink building IoT systems from the ground up▶ Systems, cryptography, applications, analytics, networks,

hardware, software, HCI

• Data security: novel cryptography that enables analytics on confidential data

• System security: a software framework for safe and secure IoT applications

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Outline

• What is the "Internet of Things"?• Why IoT security is so hard• What we plan to do about it

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The Internet of Things

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Internet(s) of Things

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NetworkedDevices

Tens/personUncontrolled Environment

Unlicensed spectrumConvenience

Powered

WiFi/802.11TCP/IP

IEEE/IETF

Personal AreaNetworks

Tens/personPersonal environmentUnlicensed spectrum

InstrumentationFashion vs. function

Bluetooth, BLE3G/LTE

3GPP/IEEE

Home AreaNetworksHundreds/person

Uncontrolled EnvironmentUnlicensed spectrum

ConvenienceConsumer requirements

ZigBee, Z-Wave6lowpan, RPL

IETF/ZigBee/private

IndustrialAutomation

Thousands/personControlled Environment

High reliabilityControl networks

Industrial requirements

WirelessHART, 802.15.46tsch, RPL

IEEE/IIC/IETF


Internet(s) of Things

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NetworkedDevices

Tens/personUncontrolled Environment

Unlicensed spectrumConvenience

Powered

WiFi/802.11TCP/IP

IEEE/IETF

Personal AreaNetworks

Tens/personPersonal environmentUnlicensed spectrum

InstrumentationFashion vs. function

Bluetooth, BLE3G/LTE

3GPP/IEEE

Home AreaNetworksHundreds/person

Uncontrolled EnvironmentUnlicensed spectrum

ConvenienceConsumer requirements

ZigBee, Z-Wave6lowpan, RPL

IETF/ZigBee/private

IndustrialAutomation

Thousands/personControlled Environment

High reliabilityControl networks

Industrial requirements

WirelessHART, 802.15.46tsch, RPL

IEEE/IIC/IETF

Secure Internet of Things 9

IoT: MGC Architecture


IoT: MGC ArchitectureeMbedded

devices


Gateways

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ZigBee,ZWave,

Bluetooth,WiFi

eMbeddeddevices


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

GatewaysCloud

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IoT: MGC ArchitectureeMbedded

devices


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

eMbeddeddevices

GatewaysCloud

13User device



embedded C(ARM, avr, msp430)

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ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP



IoT: MGC Architectureembedded C(ARM, avr, msp430)

ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Obj-C/C++, Java, Swift, Javascript/HTML





ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Ruby/Rails,Python/Django,J2EE, PHP, Node.js





ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Ruby/Rails,Python/Django,J2EE, PHP, Node.js

IoT Security is Hard

• Complex, distributed systems▶ 103-106 differences in resources across tiers▶ Many languages, OSes, and networks▶ Specialized hardware

• Just developing applications is hard• Securing them is even harder

▶ Enormous attack surface▶ Reasoning across hardware, software, languages, devices, etc.▶ What are the threats and attack models?

• Valuable data: personal, location, presence

• Rush to development + hard ➔ avoid, deal later17

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What We're Going To Do About it


Two Goals

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1.Research and define new cryptographic computational models for secure data analytics and actuation on enormous streams of real-time data from embedded systems.

2.Research and implement a secure, open source hardware/software framework that makes it easy to quickly build Internet of Things applications that use these new computational models.


Two Kinds of Security

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• Data security: data collected and processed by IoT applications remains safe▶ Home occupancy▶ Medical data▶ Presence/location

• System security: elements of MGC architecture are hard to compromise▶ eMbedded devices▶ Gateways▶ Cloud systems▶ End applications


Data Security

• Security limits what you (or an attacker) can do• What do IoT applications need to do?

▶ Generate data samples▶ Process/filter these samples▶ Analytics on streams of data, combined with historical data▶ Produce results for end applications to view

• Goal: end-to-end security▶ Embedded devices generate encrypted data▶ Only end applications can fully decrypt and view data▶ Gateways and cloud operate on data without knowing what it is

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End-to-End Security

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ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


ZigBee,ZWave,

Bluetooth,WiFi

3G/4G,TCP/IP

Data

End-to-End Security


End-to-End Security

• Sensing device samples data, encrypts it• Each processing stage can decrypt or operate on

encrypted data (increases storage requirements, limits potential operations)

• Possible that only end user can fully view data

data encrypted encrypted data

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Homomorphic Encryption(Gentry, 2009)

• Take a sensor value S, encrypt it to be Se• It is possible to perform arbitrary computations on Se

▶ But 1,000,000 slower than computations on S

• So confidential analytics possible, but not yet practical▶ But can be fast for specific computations (e.g., addition)

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New Computational Models

• Is it possible for devices to compute aggregate statistics without revealing their own data?▶ You’re in the 85th percentile for saving water today!▶ Your house consumed 120% of its average energy today

• Is it possible to compute complex analytics?• Need new cryptographic computation models

▶ Support computations that IoT applications need

• Faculty working in this area:▶ Christopher Ré on analytics▶ Dan Boneh on cryptographic computational models

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Two Goals

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1.Research and define new cryptographic computational models for secure data analytics and actuation on enormous streams of real-time data from embedded systems.

2.Research and implement a secure, open source framework that makes it easy to quickly build Internet of Things applications that use these new computational models.


Building an Application• Write a data processing pipeline

▶ Consists of a set of Models, describing data as it is stored▶ Transforms move data between Models▶ Instances of Models are bound to devices▶ Views can display Models▶ Controllers determine how data moves to Transforms

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Motion!

10Hz !Sampling!

Recent!History!

Activity!

Long!History!

Behavior!

Analytics,!Suggestions!

Health!

Views!

Controllers!

Models and!Transforms!

Recent!History!

Activity!

Sensor! Gateway! PC/Server! App/Web!

security and privacy !Alarm! Schedule!


Code Generation• Framework generates (working) skeleton code for

entire pipeline▶ All Models, Transforms, and Controllers are written in a

platform-independent language▶ Views are device specific (although many are HTML/JS)

• Developer can modify this generated code▶ Framework detects if modifications violate pipeline description▶ E.g., data types, information leakage, encryption▶ Generated code compiles down to device OS/system

• Faculty working in this area:▶ David Mazières: software abstractions for security▶ Phil Levis: Ravel software system

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The Internet of Things

• Networking is one of the hardest development challenges in IoT applications▶ Ultra-low power protocols▶ Difficult link layers (4G, BLE)▶ Protocol stack mismatches▶ Data packing/unpacking

• Framework handles this automatically▶ Novel network algorithms

• Faculty working in this area:▶ Keith Winstein, reliability in challenged networks▶ Prabal Dutta, low power wireless

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Software-defined Hardware

• Hardware (boards, chips, power) is a daunting challenge to software developers▶ It easier to modify something than create it from scratch

• The data processing pipeline is sufficient information to specify a basic embedded device▶ Sensors, networking, storage, processing needed

• Faculty working in this area:▶ Mark Horowitz: automating constrained hardware design▶ Prabal Dutta: embedded device design▶ Björn Hartmann: prototyping new applications

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Making It Easy

• If it's hard to use, people will work around it▶ Set password to "password"▶ Just store data in the clear

• Must understand development model▶ Embrace modification, incorporation, low barrier to entry▶ Do so such that prototypes can transition to production

• Faculty working in this area:▶ Björn Hartmann: prototyping new applications

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