Scalable Robust and Secure Heterogeneous Wireless Networks

1. Scalable Robust and SecureHeterogeneous Wireless Networks Guevara Noubir College of Computer Science Northeastern University, Boston, MA noubir@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

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

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. 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 Jamming IEEE802.11 and 802.11b

11. Jamming Encoded Data Packets Link Architecture

12. 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 Traditional Anti-Jamming Techniques • Spread-Spectrum in military provides: • 20-30dB processing gain • Low-power jamming requires: • 40dB! 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):

15. Jamming Effort Code Rate Shannon Limit Code Throughput 8% 0.5 0.598 0.5 17.4% 0.25 0.333 0.25 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

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 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 1 5 2 3 4 10 9 6 7 8 12 11 x Source 13 y Group member

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 B G C A Reliable reception Partial reception

28. Accumulative Power Relaying[with Chen, Jia, Liu, Sundaram] • Problem: • Determine a feasible schedule [(N1, P1), …, (Nk, Pk)] that minimizes total energy consumption B G C A Reliable reception Partial reception

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 subgroupsT leads to a low weighttree 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

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: Liv = {u: 2i-1 < d(u, v) 2i} • Algorithm: • Divide V –{r} into L1r, L2r, …, LlogDr, • Run A(Lir, r) in parallel L4r L3r

35. Algorithm A(U, r) • L = {r} • Repeat • For every uU, let Iudenote 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’