Deception in Drone Surveillance Missions: Strategic vs. Learning Approaches
| dc.contributor.author | Wan, Zelin | en |
| dc.contributor.author | Cho, Jin-Hee | en |
| dc.contributor.author | Zhu, Mu | en |
| dc.contributor.author | Anwar, Ahmed H. | en |
| dc.contributor.author | Kamhoua, Charles | en |
| dc.contributor.author | Singh, Munindar | en |
| dc.date.accessioned | 2023-11-02T13:03:04Z | en |
| dc.date.available | 2023-11-02T13:03:04Z | en |
| dc.date.issued | 2023-10-23 | en |
| dc.date.updated | 2023-11-01T08:00:37Z | en |
| dc.description.abstract | Unmanned Aerial Vehicles (UAVs) have been used for surveillance operations, search and rescue missions, and delivery services. Given their importance and versatility, they naturally become targets for cyberattacks. Denial-of-Service (DoS) attacks are commonly considered to exhaust their resources or crash UAVs (or drones). This work proposes a unique proactive defense using honey drones (HD) for UAVs during surveillance operations. These HDs use lightweight virtual machines to lure and redirect potential DoS attacks. Both the choice of target by the attacker and the HD’s deceptive tactics are influenced by the strength of the radio signal. However, a critical trade-off exists in that stronger signals can deplete battery life, while weaker signals can negatively affect the connectivity of a drone fleet network. To address this, we formulate an optimization problem to select the best strategies for an attacker or defender in selecting their signal strength level. We propose a novel HD-based defense to identify the optimal setting using deep reinforcement learning (DRL) or game theory and compare their performance with that of non-HD-based methods, such as Intrusion Detection Systems and ContainerDrone. Our experiments demonstrate the unique benefits and superior efficacy of each HD-based defense across various attack scenarios. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1145/3565287.3616525 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/116589 | en |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | The author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Deception in Drone Surveillance Missions: Strategic vs. Learning Approaches | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |