Modeling primary user emulation attacks and defenses in cognitive radio networks

Primary user emulation attacks are a potential security threat to cognitive radio networks. In this work, we attempt to characterize an advanced primary user emulation attack and an advanced countermeasure against such an attack. Specifically, we show that both the attacker and the defender can apply estimation techniques and learning methods to obtain the key information of the environment and thus design better strategies. We further demonstrate that the advanced attack strategy can defeat the naive defense technique that focuses only on the received signal power, whereas the advanced defense strategy that exploits the invariant of communication channels can counteract the advanced attack effectively.