## 4 Security Threats
### Sensing and Monitoring  
The traditional main goal of spectrum sensing in CR networks is for secondary users to detect the presence of primary user signals to avoid transmitting and interfering with them. However, more generally, sensing is needed to detect and possibly identify other users and the transmission technologies and protocols they use (for the sensing node, or group, to develop a communication strategy). Intense research is being carried out focusing on optimizing the spectrum sensing process both to reduce its performance overhead and to increase its accuracy (and even standardization activities of some aspects are ongoing). Also, a sensing process would be necessary to monitor conformance to agreed-upon access protocols and policies.  
Misbehavior in the sensing process may emerge with users executing some of the following attacks:  
* _Primary user masking_: A (malicious) node may transmit signals able to mask the primary user's ones towards misleading the spectrum sensing procedure. Thus, a legitimate node would not be able to detect the primary user's transmissions and falsely assume it found a spectrum hole [24].
* _Primary user emulation (PUE) attacks_: A node may transmit elaborately created signals which seem exactly the same as a primary user's ones. A sensing node may be unable to distinguish the real from the fake signals and falsely mark a spectrum portion as occupied by a primary user and defer its transmission, thus leaving more spectrum for the attacker. Mechanisms to counter PUE attacks are presented in [17,29,30].
* _Non-standards-compliant sensing techniques_: Standards as to how sensing should be implemented or requirements so that the desired sensing performance is achieved may be in place. Here we refer to cases when, either due to malice or selfishness, or even due to software or hardware failures, nodes do not follow the stipulated sensing behavior and we elaborate with some examples. First, nodes may use sensing intervals that are too long in order to allow for a timely reaction, e.g., to the appearance of a licensed user. Second, a node may not release a channel for sensing purposes in due time (given that sensing is to be performed at specified intervals, time during which secondary users need to remain \"silent\"). Other examples of misbehavior would be deliberately introducing de-synchronization between nodes, in order to blur the boundary between deliberate and accidental misuse or sensing with insufficient sensitivity. Finally, there is the case for tampering with spectrum sensor software and hardware to affect their normal and stable operation [31].  
It should be noted that, if we consider that nodes may be heterogeneous as to their sensing capabilities, an attacker with knowledge of their characteristics could target the \"weaker\" ones, as explained by [PERSON] and [PERSON] [24]. To elaborate, there are nodes which, by design, have a single front-end used both as their transceiver and their spectrum sensor. In this case, the effective time for transmissions is limited by sensing intervals. In contrast with nodes that have separate interfaces for sensing and exchanging data, such a node would suffer more if an attacker jammed his transmissions.  
The above discussion focuses on the sensing process. However, in the case of distributed spectrum sensing, equally important is the data fusion step, where information on spectrum usage conditions (e.g., primary user presence) from various sources (e.g., user devices, dedicated spectrum sensors, _etc._) are collectively evaluated. Misbehaving nodes with the proper incentives or malfunctioning ones may submit invalid measurements and mislead the entity responsible for data fusion. Methods to increase the robustness of the distributed sensing process against data falsification attacks are studied in [30,32].