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Award Abstract #1646317

CPS: Breakthrough: Collaborative Research: Track and Fallback: Intrusion Detection to Counteract Carjack Hacks with Fail-Operational Feedback

NSF Org: CNS
Division Of Computer and Network Systems
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Initial Amendment Date: September 1, 2016
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Latest Amendment Date: April 10, 2017
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Award Number: 1646317
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Award Instrument: Standard Grant
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Program Manager: Ralph Wachter
CNS Division Of Computer and Network Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: October 1, 2016
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End Date: September 30, 2019 (Estimated)
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Awarded Amount to Date: $250,551.00
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Investigator(s): Gedare Bloom gedare.bloom@howard.edu (Principal Investigator)
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Sponsor: Howard University
2400 Sixth Street N W
Washington, DC 20059-9000 (202)806-4759
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NSF Program(s): Special Projects - CNS,
CPS-Cyber-Physical Systems
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Program Reference Code(s): 1714, 7918, 8234, 9251
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Program Element Code(s): 1714, 7918

ABSTRACT

The security of every vehicle on the road is necessary to ensure the safety of every person on or near roadways, whether a motorist, bicyclist, or pedestrian. Features such as infotainment, telematics, and driver assistance greatly increase the complexity of vehicles: top-of-the-line cars contain over 200 computers and 100 million lines of software code. With rising complexity comes rising costs to ensure safety and security. This project investigates novel methods to improve vehicular security by detecting malicious cyber attacks against a moving automobile and responding to those attacks in a manner that ensures the safety of humans in close proximity to the vehicle.

The objective of this project is to protect in-vehicle networks from remote cyber attacks. The method of protection is a distributed in-vehicle network intrusion detection system (IDS) using information flow tracking and sensor data provenance in the cyber domain with novel approaches to address the physical uncertainty and time constraints of an automotive control system. When an intrusion is detected, the IDS triggers a fail-operational mode change to provide graceful degradation of service and initiate recovery without compromising human safety. Specific research aims of this project are to explore the design space of fail-operational IDS for automotive in-vehicle networks, to evaluate security and resiliency of an automobile using a fail-operational IDS, and to generalize fundamentals of a fail-operational IDS to other cyber-physical systems.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Bloom, Gedare and Cena, Gianluca and Bertolotti, Ivan Cibrario and Hu, Tingting and Valenzano, Adriano. "Optimized event notification in CAN through in-frame replies and Bloom filters," 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), 2017.   

Bloom, Gedare and Cena, Gianluca and Bertolotti, Ivan Cibrario and Hu, Tingting and Valenzano, Adriano. "Supporting security protocols on CAN-based networks," 2017 IEEE International Conference on Industrial Technology, 2017.   

Bloom, Gedare and Alsulami, Bassma and Nwafor, Ebelechukwu and Bertolotti, Ivan Cibrario. "Design patterns for the industrial Internet of Things," 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS), 2018.   

Nwafor, Ebelechukwu and Campbell, Andre and Hill, David and Bloom, Gedare. "Towards a provenance collection framework for Internet of Things devices," 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017.   

Bloom, Gedare and Cena, Gianluca and Cibrario Bertolotti, Ivan and Hu, Tingting and Navet, Nicolas and Valenzano, Adriano. "Event Notification in CAN-based Sensor Networks," IEEE Transactions on Industrial Informatics, 2019.   

Young, Clinton and Zambreno, Joseph and Olufowobi, Habeeb and Bloom, Gedare. "Survey of Automotive Controller Area Network Intrusion Detection Systems," IEEE Design & Test, 2019.   

Olufowobi, Habeeb and Ezeobi, Uchenna and Muhati, Eric and Robinson, Gaylon and Young, Clinton and Zambreno, Joseph and Bloom, Gedare. "Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network," AutoSec AutoSec '19 Proceedings of the ACM Workshop on Automotive Cybersecurity, 2019.   

Young, Clinton and Olufowobi, Habeeb and Bloom, Gedare and Zambreno, Joseph. "Automotive Intrusion Detection Based on Constant CAN Message Frequencies Across Vehicle Driving Modes," Proceedings of the ACM Workshop on Automotive Cybersecurity, 2019.   

Bloom, Gedare and Sherrill, Joel and Gilliland, Gary. "Aligning Deos and RTEMS with the FACE safety base operating system profile," ACM SIGBED Review, v.15, 2018.   

Olufowobi, Habeeb and Bloom, Gedare and Young, Clinton and Zambreno, Joseph. "Work-in-Progress: Real-Time Modeling for Intrusion Detection in Automotive Controller Area Network," 2018 IEEE Real-Time Systems Symposium (RTSS), 2018.   

 

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