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Scalable Robust and SecureHeterogeneous Wireless
Networks
Guevara Noubir College of Computer Science
Northeastern University, Boston, [email protected]
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Mitigating Physical Layer DoS
Physical Layer: Spread-Spectrum Directional Antennas
Link Layer: Cryptographic Interleaver + Efficient
Coding Routing:
Jamming-free paths Use of Mobility
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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
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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
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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
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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
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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
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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
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Protection against DoS in wireless networks requires a careful cross-layer design
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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
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Prototype Application
Pharos Compact Flash GPS
IEEE 802.11 PCMCIA card
iPAQ PDA
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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
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Group Management
2 3
7
96
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5
10
12
8
4
y Group member
x Source
1
13
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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;
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Universal Tree Algorithm
H’H
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Universal Tree Algorithm
H’H
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Universal Tree Algorithm
H’H
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Universal Tree Algorithm
H’H