Resilience of Satellite Transmitter Fingerprinting Against Jamming Attacks

מושגי ליבה

Satellite transmitter fingerprinting techniques are resilient against jamming attacks, requiring similar or greater attacker power to disrupt the fingerprint compared to disrupting the message contents.

תקציר

The paper evaluates the effectiveness of radio fingerprinting techniques for satellite communication systems under interference and jamming attacks. It focuses on the Iridium satellite constellation and collects a dataset of 540,066 Iridium messages with varying levels of Gaussian noise added to the incoming signal. The authors also generate additional datasets by adding synthetic jamming to clean signals in software.

The results show that in order to disrupt the transmitter fingerprint through jamming, a similar or greater transmit power is required than to disrupt the message contents via traditional jamming techniques. The difference in required attacker power is within 2.5 dB. The authors conclude that the use of fingerprinting to authenticate satellite communication does not significantly weaken the system against jamming attacks.

The authors discuss potential reasons for this, including the fact that fingerprinting in the satellite context is more robust to noise due to the high levels of atmospheric distortion, and the fingerprinter operating on a different part of the signal than the decoder. They also note that an attacker using more targeted adversarial machine learning techniques may be able to disrupt the fingerprinter more effectively, but this is left for future work.

סטטיסטיקה

The bit error probability for QPSK is given by P(bit error) = 0.5 * erfc(sqrt(Eb/N0)).
The probability of a message decode error is given by P(message error) = 1 - (1 - P(block error))^3, where P(block error) = 1 - (1 - p)^31 - 31p(1 - p)^30 - 31/2 * p^2 * (1 - p)^29.
A 50% message error rate is achieved at a bit error rate of p ≈ 0.08.

ציטוטים

The attacker's objective is to cause victim messages to be rejected as illegitimate, by inducing fingerprinter error on a certain proportion of packets.
It takes a greater amount of attacker power to disrupt the fingerprint than to disrupt the message through jamming.

תובנות מפתח מזוקקות מ:

Sticky Fingers

by Josh... ב- arxiv.org 04-05-2024

https://arxiv.org/pdf/2402.05042.pdf

שאלות מעמיקות

How would the results differ if the fingerprinting model was trained on terrestrial data instead of satellite data?

Training the fingerprinting model on terrestrial data instead of satellite data could lead to different results in terms of resilience against jamming attacks. Terrestrial data may not have the same level of atmospheric noise and distortion that satellite data experiences, which could impact the model's ability to generalize to the unique challenges of satellite communication. The model trained on terrestrial data may not be as robust in the presence of high levels of noise and interference characteristic of satellite systems. Additionally, the features and patterns present in terrestrial signals may differ from those in satellite signals, affecting the model's performance in identifying and authenticating transmitters. Therefore, the results could show a lower resistance to jamming attacks and a higher vulnerability to disruptions in the fingerprinting system when trained on terrestrial data.

What other types of adversarial attacks, beyond jamming, could an attacker use to disrupt the fingerprinting system?

In addition to jamming attacks, an attacker could employ various other adversarial attacks to disrupt the fingerprinting system. Some potential attacks include:

Spoofing Attacks: An attacker could attempt to spoof the transmitter's fingerprint by generating signals that mimic the characteristics of legitimate transmitters. This could lead to false authentication and compromise the security of the system.

Replay Attacks: By replaying previously captured signals, an attacker could deceive the fingerprinting system into accepting unauthorized transmissions as legitimate. This could result in unauthorized access to the communication channel.

Adversarial Machine Learning Attacks: Attackers could use adversarial machine learning techniques to craft input signals that exploit vulnerabilities in the fingerprinting model, leading to misclassification of transmitters and bypassing the authentication process.

Signal Manipulation Attacks: Attackers could manipulate the signal properties, such as altering the modulation scheme or introducing subtle distortions, to evade detection by the fingerprinting system and disrupt the authentication process.

How could the resilience of satellite fingerprinting systems be further improved to withstand a wider range of attacks?

To enhance the resilience of satellite fingerprinting systems against a wider range of attacks, several strategies can be implemented:

Adversarial Training: Incorporate adversarial training techniques during the model training phase to expose the fingerprinting system to potential attacks and improve its robustness against adversarial manipulations.

Feature Diversity: Include a diverse set of features in the fingerprinting model to capture a broader range of transmitter characteristics, making it more difficult for attackers to spoof or disrupt the system.

Anomaly Detection: Integrate anomaly detection mechanisms to identify unusual patterns or behaviors in the signals, enabling the system to detect and mitigate adversarial attacks in real-time.

Encryption: Implement encryption techniques to secure the communication channel in addition to fingerprinting, adding an extra layer of protection against unauthorized access and tampering.

Continuous Monitoring: Regularly monitor the performance of the fingerprinting system and update the model with new data to adapt to evolving attack strategies and maintain effectiveness in detecting and authenticating transmitters.