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Summary of Trustworthiness Research at IU

Summary of Trustworthiness Research at IU. Professors Kapadia , Myers, Wang. Research on Side-channel Detection & Mitigation. Side-channel detection: Sidebuster (CCS 2010) Mitigation infrastructure for wireless channel: Demultiplexing (joint work with UNL, MSR and McGill)

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Summary of Trustworthiness Research at IU

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  1. Summary of TrustworthinessResearch at IU Professors Kapadia, Myers, Wang

  2. Research on Side-channel Detection & Mitigation • Side-channel detection: Sidebuster (CCS 2010) • Mitigation infrastructure for wireless channel: Demultiplexing (joint work with UNL, MSR and McGill) • Automatically decompose wi-fi traffic into multiple subflows using virtual wireless interfaces

  3. Research on Sensory Malware • Speech based malware: Soundcomber (NDSS 2011) • Trojan app uses limited permissions • Captures both speech and tone based audio • Analyzes audio for credit card numbers • Uses stealthy covert channels to communicate extracted sensitive data to the “malware master” • Basic defensive architecture to prevent attack

  4. Future Sensory Malware Projects • Potential projects for next 6 months • Video mining for sensitive video • Enemy looking through your eyes? • Activity mining with accelerometers to detect group activity patterns • Infer military activity patterns based on accelerometer?

  5. Sensor to Sensor Infection Dynamics • Vulnerability Analysis: Determine the plausibility of malware to transmit from sensor to sensor via wireless signals and create an epidemic assuming human dynamics in dense metropolitan settings. • Understand Epidemic Dynamics • Effects of infection time, initial infected nodes, metropolitan density, circadian rhythms, etc…. • Builds on work using smartphones to geolocate phones not in the sensor network.

  6. Sensor Theft & Loss Prevention • Aggregate Risk Engine Structure: • SVM or other non-linear classifier • Empirically evaluate benefits of multiple sensors in risk analysis • Determine which sensor information is most useful to aggregator. • Other Sensors • Phone call & Application use patterns • (stays within Reality Mining Data Set)

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