Countering physical eavesdropper evasion with adversarial training
Signal classification is a universal problem in adversarial wireless scenarios, especially
when an eavesdropping radio receiver attempts to glean information about a target …
when an eavesdropping radio receiver attempts to glean information about a target …
On the limitations of targeted adversarial evasion attacks against deep learning enabled modulation recognition
Wireless communications has greatly benefited in recent years from advances in machine
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …
A deep ensemble-based wireless receiver architecture for mitigating adversarial attacks in automatic modulation classification
Deep learning-based automatic modulation classification (AMC) models are susceptible to
adversarial attacks. Such attacks inject specifically crafted wireless interference into …
adversarial attacks. Such attacks inject specifically crafted wireless interference into …
Adversarial filters for secure modulation classification
Classification (MC) is the problem of classifying the modulation format of a wireless signal. In
the wireless communications pipeline, MC is the first operation performed on the received …
the wireless communications pipeline, MC is the first operation performed on the received …
A hybrid training-time and run-time defense against adversarial attacks in modulation classification
Motivated by the superior performance of deep learning in many applications including
computer vision and natural language processing, several recent studies have focused on …
computer vision and natural language processing, several recent studies have focused on …
Adversarial attacks on deep-learning based radio signal classification
M Sadeghi, EG Larsson - IEEE Wireless Communications …, 2018 - ieeexplore.ieee.org
Deep learning (DL), despite its enormous success in many computer vision and language
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …
Investigating a spectral deception loss metric for training machine learning-based evasion attacks
Adversarial evasion attacks have been very successful in causing poor performance in a
wide variety of machine learning applications. One such application is radio frequency …
wide variety of machine learning applications. One such application is radio frequency …
The best defense is a good offense: Adversarial attacks to avoid modulation detection
We consider a communication scenario, in which an intruder tries to determine the
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …
Robust automatic modulation classification in the presence of adversarial attacks
Automatic modulation classification (AMC) is used in intelligent receivers operating in
shared spectrum environments to classify the modulation constellation of radio frequency …
shared spectrum environments to classify the modulation constellation of radio frequency …
Evaluating adversarial evasion attacks in the context of wireless communications
B Flowers, RM Buehrer… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Recent advancements in radio frequency machine learning (RFML) have demonstrated the
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …
use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet …