Intrusion Detection in Wireless Sensor Networks

Group MeetingSpring 2005

Presented by Edith Ngai

Outline

• Wireless sensor networks (WSN)• Security in WSN• Background on intrusion detection• Intrusion detection in WSN

• Types of attacks• Intrusion detection components• Required technologies• Future directions

• Conclusion

Technology trend

• Small integrated devices• Smaller, cheaper, more powerful

• PDAs, mobile phones

• Many opportunities, and research areas• Power management

• Distributed algorithms

Wireless sensor networks

• Wireless sensor node• power supply

• sensors

• embedded processor

• wireless link

• Many, cheap sensors• wireless easy to install

• intelligent collaboration

• low-power long lifetime

Possible applications

• Military• battlefield surveillance, biological attack detection,

targeting

• Ecological• fire detection, flood detection, agricultural uses

• Health related• human physiological data monitoring

• Miscellaneous• car theft detection, inventory control, home

applications

Required technologies

• Efficient data routing• ad-hoc network• one or more ‘datasinks’

• In-network data processing• large amounts of raw data• limited power and bandwidth

• Node localization

Security in WSN

• Main security threats in WSN are:• Radio links are insecure – eavesdropping /

injecting faulty information is possible

• Sensor nodes are not temper resistant – if it is compromised the attacker obtains all security information

• Protecting confidentiality, integrity, and availability of the communications and computations

Why security is different?

•Sensor Node Constraint

•Battery

•CPU power

•Memory

•Networking Constraints and Features

•Wireless

•Ad hoc

•Unattended

Network defense

Protect - Encryption - Firewalls - Authentication - Biometrics

Detect - Intrusions - Attacks - Misuse of Resources - Data Correlation - Data Visualization - Malicious Behaviors - Network Status/

Topology

React - Response - Terminate Connections - Block IP Addresses - Containment - Recovery - Reconstitute

What is intrusion detection?

• Intrusion detection is the process of discovering, analyzing, and reporting unauthorized or damaging network or computer activities

• Intrusion detection discovers violations of confidentiality, integrity, and availability of information and resources

• Intrusion detection demands:• As much information as the computing

resources can possibly collect and store

• Experienced personnel who can interpret network traffic and computer processes

• Constant improvement of technologies and processes to match pace of Internet innovation

What is intrusion detection?

How useful is intrusion detection?

• Provide digital forensic data to support post-compromise law enforcement actions

• Identify host and network misconfigurations• Improve management and customer

understanding of the Internet's inherent hostility

• Learn how hosts and networks operate at the operating system and protocol levels

Intrusion detection models

• All computer activity and network traffic falls in one of three categories:

• Normal

• Abnormal but not malicious

• Malicious

• Properly classifying these events are the single most difficult problem -- even more difficult than evidence collection

Intrusion detection models

• Two primary intrusion detection models• Network-based intrusion detection monitors

network traffic for signs of misuse

• Host-based intrusion detection monitors computer processes for signs of misuse

• So-called "hybrid" systems may do both• A hybrid IDS on a host may examine network

traffic to or from the host, as well as processes on that host

IDS paradigms

• Anomaly Detection - the AI approach

• Misuse Detection - simple and easy

• Burglar Alarms - policy based detection

• Honey Pots - lure the hackers in

• Hybrids - a bit of this and that

Anomaly detection

• Goals:• Analyze the network or system and infer what

is normal

• Apply statistical or heuristic measures to subsequent events and determine if they match the model/statistic of “normal”

• If events are outside of a probability window of “normal” then generate an alert

Misuse detection

• Goals:• Know what constitutes an attack

• Detect it

• A database of known attack signatures should be maintained

Intrusion Detection in WSN

Network model

•BSj: base station at location (Xj, Yj)

•Si: sensor node at location (xi, yi)

•R: transmission range of the base station

•r: transmission range of the sensor node

•k-coverage: a node covers by k BSs

Definitions

• Coverage of a base station

• Number of coverage from base stations

• p sends data to q successfully (in 1-hop)

• p sends data to q successfully via k hops

• p fails in sending data from p to q

}:{ RBSppC ii

}1|...{ 2 NiBSiBSiBSipS jkik

Gqprqpqp s ,

):|},...,1{,(

|,..., 112

1111

qpppppjikji

qpppppGppqp

iiji

ski

si

ki

skk

s

qtopfromontransmissionfailureqp f ______

Types of intrusions

• Sinkhole SH(q), HelloFlood HF(q)• A region of nodes will forward packets

destined for a BS through an adversary

• Wormhole WH(q)• An adversary tunnels messages received in

one part of the network over a low latency link and replays them in a different part

mppBSpBSqp mrilis

ks |

mppBSpBSqqp mriliss

ks |21

Types of intrusions

• Missing Data MD(p)• Missing data from p to BSi

• Wrong Data WD(p)• Inconsistent data

• Interference • Sensor p cannot send packet to its

neighboring nodes

iif CpBSp |

mis

iw dBSpNdBSpd ))(()(

)(|: ii BSpdCpi

Architecture

History

Route Tracing

Data Fusion (local,global)

TopologyNeighboringMonitoring Data Collection

RoutingMissing Data?

Inconsistent Data?

Intrusion Type Identification

Yes

Yes

Intrusion Location

Intrusion Reaction

Suspicious Behavior?

Yes

Suspicious Routes?

Yes

Intrusion detection components

• Neighbor monitoring • Watchdog

• Data fusion• Local – neighboring nodes

• Global – overlapping areas

• Topology discovery

• Route tracing

• History

Intrusion classification

Components\Attack Types I II III IV V

Neighbor Monitoring

BS Dominating intermediate node

Dominating intermediate node

Selective forwarding

--- ---

Sensor --- --- Selective forwarding

--- Interference (jamming with neighbors)

Data Comparison

Global (may have missing or inconsistent data)

(may have missing or inconsistent data)

Missing data Inconsistent data (IVa – malicious sensor or intermediate nodes)

Missing data

Local (may have missing or inconsistent data)

(may have missing or inconsistent data)

Missing data Inconsistent data (IVb – sensor failure or being compromised)

Missing data

Routing (with topology info.)

BS a region of nodes forward packet through the same adversary

An adversary tunnels messages and replays them in a different part

--- --- ---

Attack Types: I - Sinkhole, Hello Flood II – Wormhole III – Missing DataIV – Wrong Data V - Interference

Required technologies

• Collection of the audit data• Localization• Data fusion• Routing

• Analysis on the audited data• Identify the intrusion characteristics• Detect the intrusions• Locate the intrusions

• Intrusion reaction

Future direction

• Study how to collect the audit data effectively

• Complete the intrusion detection architecture

• Investigate the methods to analyze the audit data for intrusion detection

• Explore how to locate and react to the intrusions

• Formulate and evaluate our intrusion detection solution

Conclusion

• We discussed the characteristics of WSN and its security issues

• We studied traditional intrusion detection technologies

• We introduced the problem of intrusion detection in WSN

• We proposed an intrusion detection architecture and analyzed various kinds of intrusions in WSN

• We showed our future direction