A Data Intensive Reputation Management Scheme for Vehicular Ad Hoc Networks Anand Patwardhan, Anupam Joshi, Tim Finin, and Yelena Yesha Anand Patwardhan Doctoral Candidate Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Anand Patwardhan Doctoral Candidate Department of Computer Science and Electrical Engineering University of Maryland Baltimore County V2VCOM 2006

Outline Data management in VANETs Security perspective Trust-based security Distributed data-intensive reputation management Algorithm for screening data Simulation results

GPS satellite Onboard Computer with various sensors: GPS location Cameras Engine Condition Tire pressure etc. Localized and distributed Wireless Access points Various forms of connectivity GPS Localized Info-Stream Services Situation Awareness allows Adaptation Location & directions GSM, GPRS, EDGE, E-VDO WiMax Hazard warnings, Detours, Inclement weather, Road conditions, Traveler info. VANET connectivity Update propagation

Objectives Situation awareness for smart-vehicles adapt to current conditions optimal utilization of surface transport infrastructure Provisioning context sensitive travel information locally and directly a growing need to provide context-sensitive information to mobile handheld devices and car-computers with travel related information) Distributed control and fault tolerance ensure continued functioning in face of infrastructure failures arising from natural calamities or terrorist attacks Prevalent Enabling Technologies Smart cars with arrays of sensors (GPS, cameras, etc.) Multimodal wireless communication (GSM, WiFi etc.) Distributed sensor networks embedded in the transport infrastructure

Background Highly dynamic conditions Lack of centralized trust authority Data and security guarantees Information processing and decision making Distributed collaborative processes Softer security guarantees Trust based security

Dynamic conditions Network Mobility of devices Arbitrary topologies Limited connectivity Mobility Time frames important (message transmission and surface velocity) Radio ranges, interference, and obstructions Environment Road conditions, congestion, inclement weather, hazards etc.

Trust and Risk Management Conventional PKI, variants, or Web-of-Trust (PGP) infeasible Limited connectivity I&A difficult No guarantees of intent Security properties Confidentiality, integrity – cryptographic methods Availability – multiple sources, epidemic updates Reliability of source? Malicious entities, selfish-interest, non-cooperative nodes?

VANET Security Perspective Data Authenticity, reliability (quality), and timeliness Network Reliable routes Cooperative and trustworthy peers Intrusion and fault resilience Identification and Authentication Unique persistent identifiers (e.g. SUCVs) Decentralized reputation management

Examples of collaborative processes Routing On demand route setup Maintenance Data dissemination Relay data packets for others Caching Intrusion detection Reputation management Service discovery

Stimulating collaboration Cost of collaboration Storage Communication Reputation management Self-interest What is the payoff? (incentives) Higher availability (cooperation) Improved response times Reliability Reciprocity (tit-for-tat) Avenues for recourse

Data dissemination model Anchored sources (trusted) carousel information updates Mobile devices propagate these further via epidemic updates (collaboration) Burden of collecting relevant information and verifying it is placed on the consumer devices Validation of data is achieved either Trusted source (trivial case) Agreement Post-validation by trusted source

Segment validation algorithm

Simulation setup Glomosim v Transmission range 100m Simulated area: Dupont Circle, Washington DC Geographic area of 700m by 900m Mobility speeds 15 to 25 m/s Pause times of 0 to 30 s 38 anchored resources (trusted) 50 to 200 mobile devices (vehicles) Simulation time: 30 mins

Simulated area

Autonomous and Assisted Trusted sources only Trusted sources and assisted

Validated segments

Effect of malicious nodes 0% malicious 30% malicious 60% malicious

Ongoing and Future work Distributed data-intensive reputation management Trust relationships built using persistent identities for further trustworthy collaboration: Basis for Distributed intrusion detection Service discovery Reciprocative/adaptive levels of cooperation Contention management Adaptive radio-ranges to increase throughput

Questions?