internet of things_by_economides_keynote_speech_at_ccit2014_final

Prof. Anastasios A. Economides

University of Macedonia, Thessaloniki, Greece

[email protected]

http://conta.uom.gr

Internet of Things (IoT) &

Sensor Network Security

mailto:[email protected]

http://conta.uom.gr/

Near to Thessaloniki…

Agio Oros Chalkidiki

Vergina - Phillip’s Tomb Nature

3

CONTA (COmputer Networks & Telematics Applications) lab

http://conta.uom.gr

University of Macedonia

Research on: • Networking Techno-Economics

• E-Services (E-learning, E-Commerce, …)

CCIT 2014 Keynote speaker Prof. A.A. Economides 4


• Definitions • Driving Forces • Characteristics • Forecasting & Economic Impact • Applications • Technology • Experiments & Standardization • Research challenges • Cisco IoT Challenges

Overview Internet of Things (IoT)


Wireless Sensor Networks (WSN) WSN Security Defensive Measures Security Visualization Research Challenges & Open Issues Conclusions

Overview Sensor Network Security


IoT Definitions, 1

8 CCIT 2014 Keynote speaker Prof. A.A. Economides

• things, especially everyday objects, that are readable, recognizable, locatable, addressable, and/or controllable via the Internet—whether via RFID, wireless LAN, wide-area network, or other means. NIC

• IoT will connect objects around us (electronic, electrical, non electrical) to provide seamless communication and contextual services provided by them. IETF

• An evolving convergent IoT and services that is available anywhere, anytime as part of an all-pervasive omnipresent socio–economic fabric, made up of converged services, shared data and an advanced wireless and fixed infrastructure linking people & machines to provide advanced services to business and citizens. UK Future Internet Strategy Group

• IoT enables the objects in our environment to become active participants, i.e.,

– they share information with other members of the network or with any other stakeholder,

– they are capable of recognizing events and changes in their surroundings and of acting and reacting autonomously in an appropriate manner. IERC (Internet of Things Research in Europe Cluster)

IoT Definitions, 2


• the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. Gartner

• A world-wide network of interconnected objects uniquely addressable, based on standard

communication protocol. Tata Consultancy • an evolution in which objects are capable of interacting with other objects. IBM

• Interconnected objects having an active role in what might be called the Future Internet.

INFSO

• A global, immersive, invisible, ambient networked computing environment built through the continued proliferation of smart sensors, cameras, software, databases, and massive data centers in a world-spanning information fabric. PEW

“Worldwide ICT infrastructure that supports ubiquitous applications among interacting humans, machines and objects/things” A.A. Economides

IoT Driving Forces

10

IoT Characteristics, 1


• Pervasive, Ubiquitous, Seamless,

• Mobile,

• Scalable, Extensible,

• Integrated Heterogeneity(Variability), Convergence,

• Open Standards, Interoperability,

• Resource Constrains (e.g. energy, bandwidth, processing, buffering),

• Security, Assurance, Safety, Privacy,

IoT Characteristics, 2


• Cost effective,

• Energy Efficient (Green),

• Reusable,

• Distributed, Decentralized,

• Dynamic, Adaptive,

• Resilience, Self-Healing, Fault Tolerance,

• Reliable, Maintainability,

• Automatic Upgrade/Reconfiguration/Management

IoT Forecast


Cisco: 25 billion devices connected to the Internet by 2015 and 50 billion by 2020.

IDC: 15 billion devices will be communicating over the network by 2015 and 212 billion devices or things connected to networks by 2020.

ABI Research: There are more than 10 billion wirelessly connected devices in

the market today; with over 30 billion devices by 2020. Gartner: 26 billion units installed by 2020. IBM: 1 trillion cloud-ready devices by 2015.

Ericsson: 50 billion connected devices by 2020.

IoT Economic Impact, 1


Harbor Research: Service Revenues for the IoT will reach $500 Billion by 2018, dwarfing the $33 Billion in revenue expected from devices in 2018.

McKinsey Global Institute: the potential economic impact of IoT will

be $2.7 trillion to $6.2 trillion per year by 2025. Across the health-care applications, IoT technology could have an economic impact of $1.1 trillion to $2.5 trillion per year by 2025.

GSMA & Machina Research: A $ 4.5 trillion global impact in 2020.

The global business impact of the IoT can be split into two broad categories: ‘revenues’ ($2.5 trillion) and ‘cost reduction’ ($1 trillion) and ‘service improvements’ ($1 trillion).

IoT Economic Impact, 2


Cisco: The IoE Value at Stake will be $14.4 trillion for companies and industries worldwide in the next decade (2013 – 2022).

IDC: The IoT technology and services revenue will expand from $4.8 trillion in 2012 to $7.3 trillion by 2017 and $8.9 trillion by 2020.

Gartner: IoT product and service suppliers will generate

incremental revenue exceeding $300 billion, mostly in services, in 2020. It will result in $1.9 trillion in global economic value-add through sales into diverse end markets. The verticals that are leading its adoption are manufacturing (15 percent), healthcare (15 percent) and insurance (11 percent).

• Standards. • Education & Training. • Business Models. • Partnerships. • Technology. • Applications. • Security & Privacy. • Technology Humanization. • Social changes, Ethics. • New consumer behavior. • Legislation & Regulation. • Affordability.

Needed:


75% of companies from across industries are already exploring the IoT.

15 % of organizations across the globe already have an IoT

solution in place. 53 % plan to implement one within the next 24 months,

and another 14 % in the next two to five years. 21 % of transportation and logistics companies already

have IoT solutions in place. (Zebra Technologies / Forrester Consulting).

APPLICATIONS


• Personal Health, • Personal Devices (e.g. glass, watch, mobile), • Clothes, • Personal Exercise, • Infant/ Elderly/Patient Monitoring, • Special needs persons Assistance, • Hospitals, Health Retreat, • Pharmaceuticals, • Emergency, • Recreational activities, etc.

Smart Healthcare & Wellbeing


• Home equipment/appliances Control, • Safety Detectors (e.g. smoke, gas, motion), • Security, Surveillance, • Environment (e.g. heat, air, light), • Entertainment,etc.

Smart Home


• Energy & Lighting, • Security, Surveillance, • Emergency (e.g. fire, gas), • Metering, • Offices, • Hotels,etc.

Smart Building


• Monitoring, Fault Detection, • Metering, • Electricity, • Waste Management, • Water, • Gas, • Tolls, • Bridges, etc.

Smart Utilities

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• E-Government, • Security, Surveillance, • Emergency (e.g. fire, flood, tsunami), • Energy Management (e.g. lighting), • Air & Water Quality Monitoring, • Traffic Control, Parking, • Transportation (e.g. cars, buses, metro, trams,…), • Tourism, • Culture, Arts, • Education, etc.

Smart City/ Community


• Farming, Agriculture, Livestock, • Water, • Pollution, • Weather, • Nature, etc.

Smart Environment

36

• Smart Factory, • Manufacturing, Robotics, • Transportation (e.g. Airlines, Rails, Shipping), • Logistics, Supply Chain Management, • Financial Services, • Banking, • Insurance, etc.

Smart Industry & Services

37

Sensors & Actuators.

Wireless: RFID, WiFi, Bluetooth, Cellular, Satellite.

Sensor Networks (HW & SW).

Addressing.

Cloud Computing (Storage, Processing, Analytics, Security, etc.)

TECHNOLOGY


Connected device software platforms: TinyOS Contiki OS MantisOS Nano-RK Android

Connectivity software platforms: Arrayent, Californium, Java CoAP framework , Erbium, CoAP framework for Contiki, XMesh networking stack.


• WiFi (IEEE 802.11 a/b/g/n) • Bluetooth (IEEE 802.15.1) • UWB (IEEE 802.15.3) • Zigbee (IEEE 802.15.4) • WBAN (IEEE 802.15.6 • IEEE P1451.5

Wireless interface


IERC (Internet of Things European Research Cluster) projects: CASAGRAS2, IoT-A, IoT Lab,…

IoT-I (IoT Initiative)

Smart Santander project

Auto-ID lab

IPSO (Internet Protocol for Smart Objects) etc.

IoT Experiments


Devices (Sensors, Actuators, etc.), Networking & Communications, Data Management, Decision Making, Security & Privacy, Social & Legal issues, Economics, Human Behavior & Usability, Marketing, etc.

Research Challenges


• Deadline: July 1, 2014.

• Three winners of the IoT Innovation Grand Challenge will share US $250,000 in award money.

• Submissions must be entered into one of five categories: Applications and Application Enablement, Analytics, Management, Networking, or Things.

• Each submission must map to one of a variety of industries: Education, Energy, Healthcare, Manufacturing, Oil and Gas, Retail, Smart Cities, Sports and Entertainment or Transportation

• https://iotchallenge.cisco.spigit.com/Page/AboutTheContest

Cisco IoT Innovation Grand Challenge


https://iotchallenge.cisco.spigit.com/Page/AboutTheContest


• Deadline: June 17, 2014. • Cisco will be awarding $300,000 for breakthrough

approaches in malware defense, security credential management, and privacy protection to secure the IoT.

• Cisco will select up together six winners with awards from $50,000 to $75,000.

• https://www.ninesights.com/community/cisco

Cisco Security Grand Challenge


https://www.ninesights.com/community/cisco


+ Wireless Sensor Network (WSN) (co-author: Dr. E. Karapistoli)

A wireless network consists of a large number of autonomous sensors that are spatially distributed in area of interest in order to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, pollutants, etc.

Sensor:

Sensors

ADC

Processor

Memory Transceiver

Location finding system (optional)

Mobilizer (optional)

Sensing Unit Processing Unit

Power unit

Communication Unit

48

+ WSN Architecture

Internet, Satellite

Sink

Sink

Task Manager

User

Sensor Field

Sensor Node

Figure – The big picture CCIT 2014 Keynote speaker Prof. A.A. Economides 49

+ Design factors

Power Consumption

Fault Tolerance (Reliability)

Scalability

Production Costs

Operating

Hardware Constraints


+ WSNs are vulnerable to various types of attacks

Internet, Satellite

Sink

Sink

Task Manager

User

Sensor Field

Sensor Node

Spoofed Routing

information

Wormhole Attack


+ Security Goals

Availability: ensuring the survivability of network services despite denial-of-service (DoS) attacks

Confidentiality: ensuring that information is accessible only to those authorized to have access.

Integrity: guaranteeing that a message being transferred is never corrupted.

Data Freshness: ensuring that the data is recent, and that no old messages have been replayed.

Authentication: enabling a node to ensure the identity of the peer node with which it communicates.

Non-repudiation: ensuring that the origin of a message cannot deny having sent the message.


+ Threats and Attacks • An attack can be either internal or external (origin-based classification):

– External attacks include attacks launched by a node that does not belong to the logical network.

– Internal attacks include attacks launched by a compromised node or an authorized participant of the network that has gone bad by running malicious code.

• Moreover, attacks can be either passive or active (nature-based classification):

– Passive attacks are able to retrieve data from the network that might be used later when launching an active attack.

• These attacks do not influence over the behavior of the network.

– Active attacks, on the other hand, directly hinder the provisioning of services.

• Most of these attacks result in a denial of service (DoS), which is a degradation or a complete halt in communication between nodes. CCIT 2014 Keynote speaker Prof. A.A. Economides 53

Presenter

Presentation Notes

Attacks include any action that intentionally aims to cause any damage to the network. They can be divided according to their origin or their nature. An origin-based classification splits attacks into two categories, external and internal (the more severe type of threat), whereas a nature-based classification Attacks can be performed in a variety of ways, most notably as Denial-of-Service (DoS) attack, and node compromise, but also through impersonation (Sybil attack), spoofing or alteration of the routing information, and so on.

+ Attack Models

Eavesdropping: an attacker intercepts packets transmitted over the air for further cryptanalysis or traffic analysis.

Traffic analysis: allows an attacker to determine that there is activity in the network, the location of the BSs, and the type of protocols being used.

Message injection: an adversary injects bogus control information into the data stream.

Message modification: a previously captured message is modified before being retransmitted

Node capture: An embedded device is considered being compromised when an attacker, through various means, gains control to the node itself.

Denial-of-Service (DoS) attacks: can be grouped into two categories

– Service degradation (e.g., collision attack), and

– Service disablement through power exhaustion (e.g. jamming)

Pass

ive

atta

cks

Act

ive

atta

cks

54

Presenter

Presentation Notes

A succinct list of attacks examining the source of a packet, its destination, size, number, and time of transmission

+ Layer-based attack categorization

Application Layer

Transport Layer

Network Layer

Data Link Layer

Physical Layer

Flooding Attack | Desynchronization attacks

Replay Attack | Sybil Attack | Spoofed, altered, or replayed routing information | Sinkhole, Wormhole Attack | Hello Flood Attack

Collision Attack | Sybil Attack | Node Replication | Acknowledgement Spoofing Attack

Eavesdropping | Jamming | Battery Exhaustion

Power M

anagement P

lane

Mobility M

anagement P

lane

Task Managem

ent Plane

Data Aggregation Distortion | Message Injection or Modification

Figure – Sensor Network Protocol Stack CCIT 2014 Keynote speaker Prof. A.A. Economides 55

+ Overview of Countermeasures Confidentiality is provided through the use of encryption technologies.

Cryptographic algorithms such as the DES, RC5, RSA are used to protect the secrecy of a message.

MAC (Message Authentication Code) or Digital Signature Algorithms (DSA) can be used to assure the recipient’s integrity of the data and authenticity of the message

Digital Signatures can be used to ensure non-repudiation.

Availability can be achieved by adding redundant nodes. Multi path and probabilistic routing can also be used to minimize the impact of unavailability.

Data freshness is ensured by adding a counter value in each message.


+ IEEE 802.15.4 (ZigBee): Security Suites

Name Description

Null No security

AES-CTR Encryption only, CTR mode

AES-CBC-MAC-128 128-bit MAC



AES-CCM-128 Encryption and 128-bit MAC




+ Standalone Security Protocols for WSNs

• Secure Network Encryption Protocol (SNEP) – SNEP provides with confidentiality, two-party data

authentication, and data freshness • μTESLA

– extension of the TESLA protocol (by considering resource limitations)

– focuses on the need for authenticated broadcast in WSNs • TinySec

– A lightweight and generic link-layer security package – Supports two different security options: 1. authenticated encryption (TinySec-AE)

Data payload is encrypted MAC is used to authenticate packet

2. authentication only (TinySec-Auth) CCIT 2014 Keynote speaker Prof. A.A. Economides 58

+ Network Security Visualization

• Various security mechanisms have been proposed to address the security concerns of WSNs.

• Despite the fast development of computer security mechanisms, the scale and complexity of the generated wireless data put major challenges to the representation and understanding of security-relevant network information.

• To address this issue, efficient visualization techniques have been adopted by the researchers to bridge the gap.

A new security discipline emerges! CCIT 2014 Keynote speaker Prof. A.A. Economides 59

+ Until now…Visualization only for network traffic monitoring

• Network traffic visualization is one of the first directions to take when it comes to understanding, and analyzing information in vast amounts of network data.

• Many visualization tools graphically monitor real-world or simulated WSNs

(e.g. Surge, MoteView, Octopus, SNA, TOSSIM, OPNET, NS-2).

• While these tools offer some form of visualization, they are designed for

applications other than wireless security. Accordingly, these tools:

– lack the specialized techniques in visualizing security-related data.

– tend to miss abnormalities and security attacks that occur unpredictably.


+ In the near future… Visualization for network security

• Visualization should go beyond the simple ”illustration” of network behavior in order to help the analysts discriminate between normal and abnormal network activities.

• Network security visualization provides insight into areas that other system fail to enlighten by integrating visualization and machine learning techniques.

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Presenter

Presentation Notes

A visualization-based system devised for the process of detecting attack patterns inside a network is most likely a complementary tool to the already comprehensive line of network security products (i.e. firewalls, IDSs, etc.) What we need to do is to take advantage of its ability at providing insight into the attack detection process through the use of efficient visual representations & user interactions.

+ Security Visualization Techniques

Node Links

Glyphs

Parallel Coordinates

Bundle Diagrams

Radial Panels


+ Research Challenges

Security is somewhat difficult to achieve in WSNs:

Public-key cryptographic systems are inefficient on low-end devices. Moreover, cryptography by itself is not enough for insiders.

Link layer security with key management can prevent the majority of outsider attacks. However, it provides little protection against insiders, HELLO floods, and wormholes.

Wormholes and DoS attacks are difficult to defend against and can be mounted effectively by both laptop-class insiders and outsiders.

Nodes that are near to base stations are attractive to compromise requiring protocols to reduce their significance.

The development of secure routing protocols is challenging because sensor nodes are prone to failures and the topology of a sensor network changes frequently due to node failures and possible mobility.


Presenter

Presentation Notes

Most of the current network security visualization techniques focus on one of these areas, either displaying data for only one node on a network, or displaying overall network data without going into detail on particular nodes.

+ Open Issues

Improving the efficiency of symmetric key operations on sensor nodes is still an open research issue.

Although most secure schemes are able to limit the effects of attacks, intelligent attack detecting mechanisms are still of need for security.

Currently, there are some protocols that let routing paths bypass the detected compromised nodes or attacks. However, current secure routing algorithms have no effect to conquer undetected attacks. Hence, new secure routing protocols that can defend against undetected attacks or even node compromise are highly desirable.

Most approaches assume the base station is secure and robust enough. However, in some special application environments, such as battlefield surveillance, base stations may be easy to be destroyed or attacked. Under such conditions, base station protection must be carefully investigated.

Most current security studies focus on individual topics of security issues. However, security overhead will degrade other performances of the WSN. Hence, the tradeoff between security and Quality of Service (QoS) needs to be evaluated.


IoT enables dramatic society transformation! WSN is a main ingredient of IoT.

WSN Security is important!

Visualization for IoT security.

Conclusions


Thank you for your attention

Prof. Anastasios A. Economides

66 [email protected]

http://conta.uom.gr




internet of things_by_economides_keynote_speech_at_ccit2014_final

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