Ghost Injection Attack on Automatic Dependent Surveillance–Broadcast Equipped Drones Impact on Human Behaviour

Haddad, Yazeed; Orye, Erwin; Maennel, Olaf

doi:10.1109/cogsima51574.2021.9475928

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Y. Haddad et al [18] went on to test how humans would react to misleading and spoofed data; they chose 50 participants and tested their reactions while completing different tasks using a drone simulator. During their tasks, the attacker would send spoofed data of fake aircraft nearby to cause alteration in the drone's path and see how the pilot would react to such information.…”

Section: Ads-b and Ads-b Vulnerabilitiesmentioning

confidence: 99%

Secure Aviation Control through a Streamlined ADS-B Perception System

Abu Al-Haija,

Al-Tamimi

2024

ASI

View full text Add to dashboard Cite

Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join the initiative of securing this protocol and propose an efficient detection method to help detect any exploitation attempts by injecting these messages with the wrong information. This paper focused mainly on three attacks: path modification, ghost aircraft injection, and velocity drift attacks. This paper aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-score of 99.14%, and a Matthews correlation coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-score of 99.37%, and a Matthews correlation coefficient (MCC) of 0.988. Eventually, our best outcomes outdo existing models, but we believe the model would benefit from more testing of other types of attacks and a bigger dataset.

show abstract

Section: Ads-b and Ads-b Vulnerabilitiesmentioning

confidence: 99%

Secure Aviation Control through a Streamlined ADS-B Perception System

Abu Al-Haija,

Al-Tamimi

2024

ASI

View full text Add to dashboard Cite

show abstract

“…The outcome was that any critical air traffic control measure should not be solely based on ADS-B data without being fully secure and prone to attacks. Y. Haddad et al [19] researched 50 participants in a drone simulator who had to perform a task promptly. During their task, the attacker would send spoofed data of fake aircraft nearby to cause alteration in the drone's path and see how the pilot would react to such information.…”

Section: Related Workmentioning

confidence: 99%

Cost-effective Detection Model for Injected Automatic Dependent Surveillance-Broadcast Messages (ADS-B) for Secure Aviation Control

Al-Tamimi

Al‐Haija

2023

Preprint

View full text Add to dashboard Cite

Automatic Dependent Surveillance-Broadcast (ADS-B) is considered the future of aviation surveillance and traffic control as it allows different types of aircraft to transmit and gain information about their and other nearby aircraft's positions, velocity, and various other variables periodically. But, as this protocol still show that it lacks in terms of security and that researchers are still developing more methods and frameworks in order to secure this technology, we decided to join the initiative and propose an efficient detection method to help aid with detecting any attempts at injecting these messages which would cause multiple risks to aircrafts such as causing collision avoidance system failure, reporting wrong status of an aircraft, or even stealing it. This paper focused mainly on three different attacks; path modification, ghost aircraft injection, and velocity drift attacks. The dataset we utilized consisted of authentic messages captured from the OpenSky Network and generated injected messages using PyCharm. This study aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-Score of 99.14%, and a Matthews' Correlation Coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-Score of 99.37%, and a Matthews' Correlation Coefficient (MCC) of 0.988. The dataset is thought to offer good outcomes, but the model still requires more testing and a bigger dataset, bearing in mind that the model still needs to be tested against other types of attacks.

show abstract