Transcript Scalable Robust and Secure Heterogeneous Wireless Networks Guevara Noubir College of Computer Science Northeastern University, Boston, MA [email protected].

Scalable Robust and Secure
Heterogeneous Wireless Networks
Guevara Noubir
College of Computer Science
Northeastern University, Boston, MA
[email protected]
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The Heterogeneous Future of Wireless
Networks
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Ambient intelligence aware of people’s presence, needs, and context
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Ubiquitous computing: maintain seamless access to data and services
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Nature and man-made disaster: require adequate operational modes
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Safety services: better quality of life for elderly and disabled people
The need for the enabling technology
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Limitations of current wireless technology:
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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:
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Fast recovery through reconfiguration and prioritization of services
Resiliency to denial of service attack
Amplified by: untracability, limited resources (energy and computation power)
Talk focus on networking aspects
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Outline
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Characteristics of heterogeneous wireless networks
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Some security aspects heterogeneous wireless networks
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Some novel approaches to scalability and robustness
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Physical, layer/link, and multi-layer attacks
Multicasting
Cross-layer design
Accumulative Relaying
Universal Network Structures
Conclusion
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Characteristics
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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
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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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Multihop Heterogeneous Paths
Resource Efficient Paths:
Multirate, Power-Controlled, Contention and Mobility Aware
Cooperating paths:
Distributed MIMO, Accumulative Relaying
Internet
Access Points
Mobile Nodes
Sensor Nodes
Universal Network Design:
Universal Sensors Steiner Tree
Robust Distributed Compression:
Generalized Slepian-Wolf
Cross-layer power controlled MAC
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Multilayer DoS in Wireless Networks
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Physical layer
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MAC layer
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Jamming of control traffic and mechanisms
Network layer
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Smart multilayer aware jammers
Malicious injection/disruption of routing information
Transport layer
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Exploiting weaknesses in congestion control
mechanisms
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Physical Layer Jamming
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Leads to:
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Network partition
Forcing packets to be routed over chosen paths
Low-Power: cyber-mines
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Low-Power Physical Layer Jamming
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Jamming effort:
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IP packet:
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Jamming duration/packet duration
1500 bytes = 12000 bits
Uncoded packet:
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Jamming effort in the order of 10-4
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Jamming IEEE802.11 and 802.11b
Modulation/coding
Rate
Packet length
IP packet
Number of bits
needed to jam
Jamming
Efficiency
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
Link Architecture
Jamming Unreliable
Communication
Jamming ECC Protected
Communication
UDP
UDP
EDP
…
Jamming Interleaved ECC
Protected Communication
UDP
EDP
IDP
JP
JP
JP
>dmin-1/2
UDP: Uncoded Data Packet
JP: Jamming Packet
EDP: Encoded Data Packet
in l codewords
RP: Received Packet
IDP: Interleaved Data Packet
DDP: De-Interleaved Packet
RP
DDP
P
dmin: code minimum
Hamming distace
>dmin-1/2 errors within
a single codeword
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Traditional Anti-Jamming Techniques
Focus on bit-level
2
P
G
G
R
J
j
jr rj tr Lr B r

S Pt Gtr Grt R 2jr L j B j
Pj:
Gjr:
Grj:
Rtr:
Lr:
Br:
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Pt:
Gtr:
Grt:
Rjr:
Lj:
Bj:
transmitter power
antenna gain from transmitter to receiver
antenna gain from receiver to transmitter
distance from jammer to receiver
jammer signal loss
jamming transmitter bandwidth
Spread-Spectrum in military provides:
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jammer power
antenna gain from jammer to receiver
antenna gain from receiver to jammer
distance from transmitter to receiver
communication signal loss
communications receiver bandwidth
20-30dB processing gain
Low-power jamming requires:
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40dB!
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Mitigating Physical Layer DoS
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Physical Layer:
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Link Layer:
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Spread-Spectrum
Directional Antennas
Cryptographic Interleaver + Efficient Coding
Routing:
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Jamming-free paths
Use of Mobility
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Proposed Solution for Link Layer
Cryptographic Interleaving
+
Efficient Adaptive Error Correction
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For Binary Modulation:
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Cryptographic interleaving transforms the
channel into a Binary Symmetric Channel
Capacity of BSC (Shannon):
C  1  H ( p)
C  1  p log2 ( p)  (1  p) log2 (1  p)
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Practical Codes
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Low Density Parity Codes:
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Very Close to Shannon’s Bound
Best for long packets:
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E.g., 16000 bits
Jamming Effort
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Code Rate
Shannon Limit
8%
0.5
0.598
Code
Throughput
0.5
17.4%
0.25
0.333
0.25
Non-binary modulation e.g., IEEE802.11b (CCK): transmits 8 bits
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Use a Reed-Solomon code with symbols of 8 bits
Maximum length: 256 bytes
Data: k  256bytes
Tolerates: (256-k)/2 errors
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Conclusion on Physical Layer DoS
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Existing Wireless Data Networks are easy targets of physical layer jamming
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High transmission power, and spread-spectrum are not enough
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Jammer effort in the order of 10-4 for an IP packet
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Traditional anti-jamming focuses on bit protection
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Cryptographic interleaving and Error Control Codes provide much better
resiliency to Jamming
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Additional technique that derive from the J/S ratio: directional antennas
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Need adaptivity and careful integration within the network stack
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Link/MAC Layer DoS
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Attack Control Traffic
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RACH/Grant CH/BCCH channels in cellular
Authentication (e.g., sending deauth message)
MAC Mechanisms of IEEE802.11:
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Reservation:
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Backoff:
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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 allows an attacker to spend less energy when Jamming
Selecting attacks on MAC/IP addresses
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DoS on Routing
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Malicious nodes can attack control traffic:
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Attack goals: disruption or resource consumption
Techniques:
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Jamming
Inject wrong information
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
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Transport layer should be able to differentiate
between:
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Congestion
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Wireless link packets loss
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Due to traffic pattern change: new sessions
Requires source rate reduction
Due to mobility and interference
Requires modulation/coding/power/path change
Malicious nodes
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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]
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Goal:
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Secure multicast applications:
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Communication over a multihop wireless ad hoc network
Limited computation power, and energy
Services:
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Secure remote tracking of mobiles
Sharing sensed data
Military: Data/Video streaming from UAV, multicasting of command decisions
Specificity:
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Securely and efficiently acquire and disseminate time varying information
Example: location information
Authentication, integrity, confidentiality, revocation, group key management
Approach:
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Overlay network of mobile nodes build secure multicast tree
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Prototype Application
iPAQ PDA
Pharos Compact Flash GPS
IEEE 802.11 PCMCIA card
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Ad Hoc vs. Wired Multicast
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Wireless:
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Mobility:
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Higher packet loss
Necessity of frequent discovery of paths
Multihop:
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Unreliable links
Loss of a packet results in node exclusion and necessity for new
join request
Cost of multicast depends on number of hops
Major factor because of radio resources scarcity
Ad hoc:
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Limited computation: nodes cannot manage large groups
Active nodes
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Group Management
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x
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Source
y Group member
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Issues and Results
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Efficient tree construction and maintenance
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Under mobility greedy algorithms can be very good
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Public key encryption is costly:
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Close to optimal trees O(log n) in theory but in practice 1.5
approximation
Minimize broadcast cost and tree maintenance
Memory can be traded with computation
Revocation in an infrastructure-less environment
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Novel Approaches to Scalability and
Robustness
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Scalability to large networks with limited
resources requires novel techniques
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Make use of specificity of the environment
Use techniques from a combination of fields:
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Graph theory, linear programming, network flow
Information theory, coding theory
Accurate simulation and modeling tools
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Accumulative relaying
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Universal network design
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Accumulative Power Relaying
[with Chen, Jia, Liu, Sundaram]
B
G
A
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C
Reliable reception
Partial reception
Problem:
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Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that
minimizes total energy consumption
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Accumulative Power Relaying
[with Chen, Jia, Liu, Sundaram]
B
G
A
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C
Reliable reception
Partial reception
Problem:
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Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that
minimizes total energy consumption
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Accumulative Relaying
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Very similar to the relay problem in information
theory and still open in it’s general form
Simpler than the general relay problem:
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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]
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Problem:
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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)
CostT ( S )
}
i.e., build T that minimizes the stretch Max{
S V
OPT ( S )
Applications:
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Environment: sensor network where routing is difficult
Dissemination: efficient multicasting to dynamic groups
Aggregation from changing groups
Distributed queries
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Universal Tree for the Euclidian Space
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Results:
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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
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Other results:
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Algorithm for a universal tree for non-Euclidian
metrics with poly-logarithmic stretch
Poly-logarithmic stretch for the universal Traveler
Salesman Problem
Extensions:
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Universal tree for energy cost
Universal tree for planar, range limited wireless
communication
Fault-tolerant network structures
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Conclusion
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We live in an exciting era:
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Wireless physical layer is capable of providing high
data rates
Software flexibility
Computation power
This provides the building blocks to enable
ubiquitous networking
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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
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Results:
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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:
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Level i of v: Liv = {u: 2i-1 < d(u, v) 2i}
L4r
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Algorithm:
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L3r
Divide V –{r} into L1r, L2r, …, LlogDr,
Run A(Lir, r) in parallel
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Algorithm A(U, r)
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L = {r}
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Repeat
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For every uU, 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.
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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’
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