Open Collections

Yao, Yingao

University of British Columbia

Swarm robotics, particularly drone swarms, are used in various safety-critical tasks (e.g., package delivery, warehouse management, entertainment, agriculture, etc). Drone swarms use swarm intelligence to carry out the mission in collaboration. While a lot of attention has been given to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. GPS attack is a kind of physical attack that feeds the drone with erroneous GPS signals via physical channels such as the GPS signal emitter. Specifically, we show that the attacker can target a swarm member (target drone) through GPS spoofing attacks, and indirectly cause other swarm members (victim drones) to veer from their course, resulting in a collision. We call these Swarm Propagation Vulnerabilities. In this thesis, we introduce a fuzzing framework consisting of two fuzzing tools, SwarmFuzzGraph and SwarmFuzzBinary , to capture the attacker’s ability, and efficiently find such vulnerabilities in swarm control algorithms. SwarmFuzzGraph uses a combination of graph theory and gradient-guided optimization to find the potential attack parameters. Our evaluation on a popular swarm control algorithm shows that SwarmFuzzGraph achieves an average success rate of 48.8% in finding SPVs. Compared to random fuzzing, SwarmFuzzGraph has a 10x higher success rate, and 3x lower runtime. However, SwarmFuzzGraph fails to find SPVs when applied in another swarm control algorithm due to its inability to handle different swarm topologies. We then propose SwarmFuzzBinary , which uses observation-based seed scheduling and binary search to find attack parameters in swarms with various topologies. The evaluation on the same two swarm control algorithms shows that SwarmFuzzBinary ’s success rate is comparable to SwarmFuzzGraph and works in all tested algorithms. Specifically, SwarmFuzzBinary achieves an average success rate of 60.2% and 86.5% in each swarm control algorithm. Compared to random fuzzing, SwarmFuzzBinary has a 5x higher success rate, and 3x lower runtime. We also find that swarms of a larger size are more vulnerable to this attack type, for a given spoofing distance.

Text

2023-12-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/86954

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/