r seeks to disturb the SUs to prevent them from reaching consensus by randomly reporting the true or false results about the existence of PUs. The majority of existing defense methods can be divided into two categories: one mak- ing direct judgments based on the current spectrum sensing data while the other uses the historical spectrum sensing data to update sensors' reputation. 11.1.3 Jamming Attacks The open nature of wireless communication leaves it vulnerable to various attacks. One of the most common attacks in wireless communication as well as SS networks is the jamming attack. Attackers transmit signals to interfere with the victims' communications in order to cause a DoS and compromise availability of communication links [104]. Traditional anti-jamming methods used in wireless communications include sequence-based fre- quency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS). However, the fixed transmission patterns of these methods leave them helpless against dynamic jamming attacks and cause low spectrum efficiency. SS techniques enable flexible access to different channels, allowing users to avoid attackers by exploiting that flexibility. The ML techniques provide more adaptive channel 11.1 Security Concerns in SS selection ability to systems in order to avoid jamming attacks. They also give the system the ability to learn and predict the behavior of jammers to increase anti-jamming channel selection efficiency. The attackers may also use different ML-based methods to improve their attack strategies [206] rendering the study of advanced jamming attacks and corresponding countermeasures of vital importance to the SS system. 11.1.4 Intercept/Eavesdrop Eavesdropping is another common attack in wireless communications. Due to the broad- cast nature of radio propagation, any active transmissions operated over the shared spectrum by different wireless networks are extremely vulnerable to eavesdropping. It is therefore important to investigate the confidentiality protection of SS commu