1

Scalable Robust and SecureHeterogeneous Wireless

Networks

Guevara Noubir College of Computer Science

Northeastern University, Boston, MAnoubir@ccs.neu.edu

2

The Heterogeneous Future of Wireless Networks

Ambient intelligence aware of people’s presence, needs, and context Ubiquitous computing: maintain seamless access to data and

services Nature and man-made disaster: require adequate operational modes

Fast recovery through reconfiguration and prioritization of services Resiliency to denial of service attack

Safety services: better quality of life for elderly and disabled people

The need for the enabling technology Limitations of current wireless technology:

No integration, QoS, seamless adaptivity, single-hop, limited data rates, battery life

Major issues: scalability, robustness, security We need novel approaches!

As these applications become more ubiquitous new threats will appear: Amplified by: untracability, limited resources (energy and computation power)

Talk focus on networking aspects

3

Outline Characteristics of heterogeneous wireless networks

Some security aspects heterogeneous wireless networks

Physical, layer/link, and multi-layer attacks Multicasting

Some novel approaches to scalability and robustness Cross-layer design Accumulative Relaying Universal Network Structures

Conclusion

4

Characteristics

Limited radio spectrum Shared Medium (collisions) Limited energy available at the nodes Limited computation power Limited storage memory Unreliable network connectivity Dynamic topology Need to enforce fairness

5

Flexibility Use of various coding/modulation schemes Use of various transmission power level Use of multiple RF interfaces Use of multi-hop relaying Clustering and backbone formation Planning of the fixed nodes location Packets scheduling schemes Application adaptivity

6

Universal Network Design: Universal Sensors Steiner Tree

Robust Distributed Compression: Generalized Slepian-Wolf

Sensor Nodes

Access Points

Multihop Heterogeneous Paths

Resource Efficient Paths: Multirate, Power-Controlled, Contention and Mobility Aware

Cooperating paths: Distributed MIMO, Accumulative Relaying

Mobile Nodes

Internet

Cross-layer power controlled MAC

7

Multilayer DoS in Wireless Networks

Physical layer Smart multilayer aware jammers

MAC layer Jamming of control traffic and mechanisms

Network layer Malicious injection/disruption of routing

information Transport layer

Exploiting weaknesses in congestion control mechanisms

8

Physical Layer Jamming

Leads to: Network partition Forcing packets to be routed over chosen

paths Low-Power: cyber-mines

user node

adversary node

dead area

9

Low-Power Physical Layer Jamming

Jamming effort: Jamming duration/packet duration

IP packet: 1500 bytes = 12000 bits

Uncoded packet: Jamming effort in the order of 10-4

10

Jamming IEEE802.11 and 802.11b

Modulation/codingRate

Packet lengthIP packet

Number of bitsneeded to jam

JammingEfficiency

BPSK 1500*8 1 12000

QPSK 1500*8 2 6000

CCK (5.5Mbps) 1500*8 4 3000

CCK (11Mbps) 1500*8 8 1500

11

Jamming Encoded Data Packets

UDP: Uncoded Data Packet

JP

Jamming Unreliable Communication

UDP JP: Jamming Packet

EDP: Encoded Data Packet in l codewords

IDP: Interleaved Data Packet

DDP: De-Interleaved Packet

UDP

>dmin-1/2

EDP UDP EDP

IDP

>dmin-1/2 errors within a single codeword

RP: Received Packet

RP

DDPP

Jamming ECC Protected Communication

Jamming Interleaved ECC Protected Communication

JP JP

dmin: code minimum Hamming distace

…

Link Architecture

12

Traditional Anti-Jamming Techniques

Spread-Spectrum in military provides: 20-30dB processing gain

Low-power jamming requires: 40dB!

jjjrrttrt

rrtrrjjrj

BLRGGP

BLRGGP

S

J2

2

Pj: jammer power

Gjr: antenna gain from jammer to receiver

Grj: antenna gain from receiver to jammer

Rtr: distance from transmitter to receiver

Lr: communication signal loss

Br: communications receiver bandwidth

Pt: transmitter power

Gtr: antenna gain from transmitter to receiver

Grt: antenna gain from receiver to transmitter

Rjr: distance from jammer to receiver

Lj: jammer signal loss

Bj: jamming transmitter bandwidth

Focus on bit-level

13

Mitigating Physical Layer DoS

Physical Layer: Spread-Spectrum Directional Antennas

Link Layer: Cryptographic Interleaver + Efficient

Coding Routing:

Jamming-free paths Use of Mobility

14

Proposed Solution for Link Layer

Cryptographic Interleaving +

Efficient Adaptive Error Correction

For Binary Modulation: Cryptographic interleaving transforms

the channel into a Binary Symmetric Channel

Capacity of BSC (Shannon):)1(log)1()(log1

)(1

22 ppppC

pHC

15

Practical Codes Low Density Parity Codes:

Very Close to Shannon’s Bound Best for long packets:

E.g., 16000 bits

Non-binary modulation e.g., IEEE802.11b (CCK): transmits 8 bits Use a Reed-Solomon code with symbols of 8 bits Maximum length: 256 bytes Data: k 256bytes Tolerates: (256-k)/2 errors

Jamming Effort Code Rate Shannon Limit Code Throughput

8% 0.5 0.598 0.5

17.4% 0.25 0.333 0.25

16

Conclusion on Physical Layer DoS

Existing Wireless Data Networks are easy targets of physical layer jamming

High transmission power, and spread-spectrum are not enough

Jammer effort in the order of 10-4 for an IP packet

Traditional anti-jamming focuses on bit protection

Cryptographic interleaving and Error Control Codes provide much better resiliency to Jamming

Additional technique that derive from the J/S ratio: directional antennas

Need adaptivity and careful integration within the network stack

17

Link/MAC Layer DoS

Attack Control Traffic RACH/Grant CH/BCCH channels in cellular Authentication (e.g., sending deauth message)

MAC Mechanisms of IEEE802.11: Reservation:

RTS/CTS are short packets: require less energy to be jammed

NAV: malicious nodes can force nodes to wait for long durations

EIFS: a single pulse every EIFS at high power Backoff:

Backoff allows an attacker to spend less energy when Jamming

Selecting attacks on MAC/IP addresses

18

DoS on Routing Malicious nodes can attack control traffic:

Jamming Inject wrong information

Attack goals: disruption or resource consumption Techniques:

Black hole: force all packets to go through an adversary node Rooting loop: force packets to loop and consume bandwidth and

energy Gray hole: drop some packets (e.g., data but not control) Detours: force sub-optimal paths Wormhole: use a tunnel between two attacking nodes Rushing attack: drop subsequent legitimate RREQ Inject extra traffic: consume energy and bandwidth Blackmailing: ruining the routing reputation of a node

Proposed secure routing protocols are still not practical

19

DoS on Transport Layer Transport layer should be able to

differentiate between: Congestion

Due to traffic pattern change: new sessions Requires source rate reduction

Wireless link packets loss Due to mobility and interference Requires modulation/coding/power/path change

Malicious nodes Selective jamming and disruptions Requires isolation of malicious nodes and dead areas

20

Protection against DoS in wireless networks requires a careful cross-layer design

21

Secure Multicasting[with Kaya, Lin, Qian – Funded by Draper]

Goal: Securely and efficiently acquire and disseminate time varying information Example: location information

Secure multicast applications: Secure remote tracking of mobiles Sharing sensed data Military: Data/Video streaming from UAV, multicasting of command

decisions

Specificity: Communication over a multihop wireless ad hoc network Limited computation power, and energy

Services: Authentication, integrity, confidentiality, revocation, group key management

Approach: Overlay network of mobile nodes build secure multicast tree

22

Prototype Application

Pharos Compact Flash GPS

IEEE 802.11 PCMCIA card

iPAQ PDA

23

Ad Hoc vs. Wired Multicast Wireless:

Unreliable links Loss of a packet results in node exclusion and necessity for

new join request Mobility:

Higher packet loss Necessity of frequent discovery of paths

Multihop: Cost of multicast depends on number of hops Major factor because of radio resources scarcity

Ad hoc: Limited computation: nodes cannot manage large groups Active nodes

24

Group Management

2 3

7

96

11

5

10

12

8

4

y Group member

x Source

1

13

25

Issues and Results Efficient tree construction and maintenance

Under mobility greedy algorithms can be very good Close to optimal trees O(log n) in theory but in practice

1.5 approximation Minimize broadcast cost and tree maintenance

Public key encryption is costly: Memory can be traded with computation

Revocation in an infrastructure-less environment

26

Novel Approaches to Scalability and Robustness

Scalability to large networks with limited resources requires novel techniques Make use of specificity of the environment Use techniques from a combination of fields:

Graph theory, linear programming, network flow Information theory, coding theory Accurate simulation and modeling tools

Accumulative relaying

Universal network design

27

Accumulative Power Relaying[with Chen, Jia, Liu, Sundaram]

Problem: Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that

minimizes total energy consumption

Reliable receptionPartial reception

A

B

C

G

28

Accumulative Power Relaying[with Chen, Jia, Liu, Sundaram]

Problem: Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that

minimizes total energy consumption

Reliable receptionPartial reception

A

B

C

G

29

Accumulative Relaying Very similar to the relay problem in information

theory and still open in it’s general form Simpler than the general relay problem:

Every energy optimal sequence can be transformed into a canonical form called wavepath

In a wavepath each node in the sequence activates its next hop neighbor and only its next hop neighbor

Finding a minimum energy wavepath is still NP-hard for arbitrary networks

Heuristic for building a wavepath can achieve more than 40% energy saving on a Euclidian plane

30

Universal Multicast Tree [with Jia, Lin, Rajaraman, Sundaram]

Problem: Given a graph G (V, E), n nodes, and a root/sink Build a tree T such that for all subgroups T leads to a low

weight tree for all subgroups (through pruning) i.e., build T that minimizes the stretch

Applications: Environment: sensor network where routing is difficult Dissemination: efficient multicasting to dynamic groups Aggregation from changing groups Distributed queries

})(

)({

SOPT

SCostMax T

VS

31

Universal Tree for the Euclidian Space

Results: Polynomial time algorithm to build a universal

tree with stretch O(log k) [where k is the size of the selected subgroup]

Hardness result: no algorithm can build a tree with stretch lower O(log n/loglog n)

32

Universal Structures Other results:

Algorithm for a universal tree for non-Euclidian metrics with poly-logarithmic stretch

Poly-logarithmic stretch for the universal Traveler Salesman Problem

Extensions: Universal tree for energy cost Universal tree for planar, range limited

wireless communication Fault-tolerant network structures

33

Conclusion We live in an exciting era:

Wireless physical layer is capable of providing high data rates

Software flexibility Computation power

This provides the building blocks to enable ubiquitous networking Creates new threats Need smart adaptive control of the physical

layer Need to deal with security and robustness in a

scalable way

34

Universal Tree for the Euclidian Space

Results: Polynomial time algorithm to build a universal tree with

stretch O(log k) [where k is the size of selected subgroup]

Hardness result: no algorithm can build a tree with stretch lower O(log n/loglog n)

Definition: Level i of v: Li

v = {u: 2i-1 < d(u, v) 2i}

Algorithm: Divide V –{r} into L1

r, L2r, …, Llog

r, Run A(Li

r, r) in parallel

L3r

L4r

35

Algorithm A(U, r) L = {r} Repeat

For every uU, let Iu denote the level of u to its nearest neighbor in L;

Let I = max {Iu : u U} Let H = {u U : Iu = I} Let H’ H s.t.

u, v H’ d(u,v) 2I-1, u H\H’ v H’ s.t. d(u,v) < 2I-1

u H’ output edge (u, nearest-neighbor(u)) L = L H’; U = U\H’;

Until no edge output;

36

Universal Tree Algorithm

H’H

37

Universal Tree Algorithm

H’H

38

Universal Tree Algorithm

H’H

39

Universal Tree Algorithm

H’H