BACKGROUND:
In a cognitive radio network, the cognitive transmitter senses the medium to detect spectrum
opportunities and transmits its own data if the channel is sensed to be idle. A jammer can also sense the medium and identify the slots
of successful transmission. The jammer’s main objective is to reduce the throughput of the cognitive transmitter.
METHODS:
Towards this objective, the jammer builds a deep learning classifier in which the most recent sensing results of
acknowledgments (ACKs) sent by the receiver are used to predict the slots of successful transmissions of the cognitive transmitter.
This allows the attacker to reliably predict the successful transmissions and can effectively jam these transmissions. The deep learning
classification soft decision probabilities are used by the jammer for power control subject to a certain power budget. A receiver-based
defense mechanism is developed against the jamming attacks. The receiver purposely takes some wrong actions, i.e., sends ACK
when transmission is not successful and vice versa, to poison the training process of the attacker.
Results:
We show that our receiver’s defense mechanism effectively enhances the throughput of the cognitive transmitter when
compared to the transmitter’s defense mechanism, where the transmitter takes some wrong decisions when it accesses the
medium.
CONCLUSION:
A novel defense mechanism against jamming attacks in cognitive radio networks is introduced.
Lightweight Deep Learning Model for Automatic Modulation Classification in Cognitive Radio Networks
