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Transcript
Scalable Robust and Secure Heterogeneous Wireless Networks Guevara Noubir College of Computer Science Northeastern University, Boston, MA [email protected] 1 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 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: 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 2 Outline Characteristics of heterogeneous wireless networks Some security aspects heterogeneous wireless networks Some novel approaches to scalability and robustness Physical, layer/link, and multi-layer attacks Multicasting Cross-layer design Accumulative Relaying Universal Network Structures Conclusion 3 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 4 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 5 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 6 Multilayer DoS in Wireless Networks Physical layer MAC layer Jamming of control traffic and mechanisms Network layer Smart multilayer aware jammers Malicious injection/disruption of routing information Transport layer Exploiting weaknesses in congestion control mechanisms 7 Physical Layer Jamming Leads to: Network partition Forcing packets to be routed over chosen paths Low-Power: cyber-mines 8 Low-Power Physical Layer Jamming Jamming effort: IP packet: Jamming duration/packet duration 1500 bytes = 12000 bits Uncoded packet: Jamming effort in the order of 10-4 9 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 10 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 11 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: 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: 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: 40dB! 12 Mitigating Physical Layer DoS Physical Layer: Link Layer: Spread-Spectrum Directional Antennas Cryptographic Interleaver + Efficient Coding Routing: Jamming-free paths Use of Mobility 13 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): C 1 H ( p) C 1 p log 2 ( p ) (1 p) log 2 (1 p) 14 Practical Codes Low Density Parity Codes: Very Close to Shannon’s Bound Best for long packets: E.g., 16000 bits Jamming Effort 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 Use a Reed-Solomon code with symbols of 8 bits Maximum length: 256 bytes Data: k 256bytes Tolerates: (256-k)/2 errors 15 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 16 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: Backoff: 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 17 DoS on Routing Malicious nodes can attack control traffic: Attack goals: disruption or resource consumption Techniques: 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 18 DoS on Transport Layer Transport layer should be able to differentiate between: Congestion Wireless link packets loss Due to traffic pattern change: new sessions Requires source rate reduction 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 19 Protection against DoS in wireless networks requires a careful cross-layer design 20 Secure Multicasting [with Kaya, Lin, Qian – Funded by Draper] Goal: Secure multicast applications: Communication over a multihop wireless ad hoc network Limited computation power, and energy Services: Secure remote tracking of mobiles Sharing sensed data Military: Data/Video streaming from UAV, multicasting of command decisions Specificity: Securely and efficiently acquire and disseminate time varying information Example: location information Authentication, integrity, confidentiality, revocation, group key management Approach: Overlay network of mobile nodes build secure multicast tree 21 Prototype Application iPAQ PDA Pharos Compact Flash GPS IEEE 802.11 PCMCIA card 22 Ad Hoc vs. Wired Multicast Wireless: Mobility: Higher packet loss Necessity of frequent discovery of paths Multihop: 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: Limited computation: nodes cannot manage large groups Active nodes 23 Group Management 1 2 5 3 4 9 6 7 10 8 11 12 x 13 Source y Group member 24 Issues and Results Efficient tree construction and maintenance Under mobility greedy algorithms can be very good Public key encryption is costly: 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 25 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 26 Accumulative Power Relaying [with Chen, Jia, Liu, Sundaram] B G A C Reliable reception Partial reception Problem: Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that minimizes total energy consumption 27 Accumulative Power Relaying [with Chen, Jia, Liu, Sundaram] B G A C Reliable reception Partial reception Problem: Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that minimizes total energy consumption 28 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 29 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) CostT ( S ) } i.e., build T that minimizes the stretch Max{ S V OPT ( S ) Applications: Environment: sensor network where routing is difficult Dissemination: efficient multicasting to dynamic groups Aggregation from changing groups Distributed queries 30 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) 31 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 32 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 33 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: Liv = {u: 2i-1 < d(u, v) 2i} L4r Algorithm: L3r Divide V –{r} into L1r, L2r, …, LlogDr, Run A(Lir, r) in parallel 34 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; 35 Universal Tree Algorithm H H’ 36 Universal Tree Algorithm H H’ 37 Universal Tree Algorithm H H’ 38 Universal Tree Algorithm H H’ 39