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Virginia Tech

Exploring the Vulnerabilities of Traffic Collision Avoidance Systems (TCAS) Through Software Defined Radio (SDR) Exploitation

Abstract

dc:description.abstract

Traffic Collision Avoidance Systems (TCAS) are safety-critical systems that are deployed on most commercial aircraft in service today. However, TCAS transactions were not designed to account for malicious actors. While in the past it may have been infeasible for an attacker to craft arbitrary radio signals, attackers today have access to open-source digital signal processing software like GNU Radio and inexpensive Software Define Radios (SDR). Therefore, this thesis presents motivation through analytical and experimental means for more investigation into TCAS from a security perspective. Methods for analyzing TCAS both qualitatively and quantitatively from an adversarial perspective are presented, and an experimental attack is developed in GNU Radio to perform an attack in a well-defined threat model.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Engineering
Department dc:contributor.department
Electrical and Computer Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Berges, Paul Martin
Chair dc:contributor.committeechair
  • Gerdes, Ryan M.
Committee members dc:contributor.committeemember
  • Reed, Jeffrey H.
  • Clancy, Thomas Charles III

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:20681
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/90165

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Berges, Paul Martin. Exploring the Vulnerabilities of Traffic Collision Avoidance Systems (TCAS) Through Software Defined Radio (SDR) Exploitation. masters thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90165