Security in Cognitive Radio Networks: Sensing and Primary User Emulation

Cognitive Radio Networks, or CRNs, introduce a new paradigm toward solving the spectrum scarcity problem by enabling unlicensed users to opportunistically access unused licensed spectrum bands. These networks make use of cognitive radio technology that is capable of sensing the spectral environment, identifying vacant channels, and modifying transmission parameters accordingly. In any shared resource-based system, security concerns have always occupied the first place. Correct sensing and preventing such kind of attacks, PUE attacks, is highly important in terms of the whole effectiveness and reliability of CRNs. The contribution below provides a discussion about the security challenges related to sensing in CRNs, focusing on the PUE attack and its countermeasures toward secure operation.

Overview of Cognitive Radio Networks and Sensing

Cognitive radios function by dynamically sensing the unutilized portions of the spectrum and use them for transmitting data. The cognitive radios in CRNs do this by effectively sensing the spectrum so as to identify those bands that are interference-free and those which are occupied. 

It is performed by:

1. Spectrum Sensing: Cognitive radios will periodically sense the spectrum bands so as to identify channels not used by the licensed or primary users.

2. Spectrum Decision: Once the spectrum detector detects an unused spectrum band, cognitive radios then make their decisions on optimum frequency bands considering interference, throughput, and conditions of the channel.

3. Spectrum Sharing: Once a band is identified and selected, a secondary user shares the channel with other cognitive radios or primary users. 

4. Spectrum Mobility: Once a secondary user detects a primary user, the channel must be vacated to avoid interference.

Therefore, sensing is the basis of operation of CRN. However, in nature it is very vulnerable to attack because the criteria of operation are essentially based on environmental data which malicious users can manipulate. Probably one of the most frequent threats in CRNs is PUE attack that seriously impairs the accuracy of spectrum sensing and may cause serious disruptions.

Primary User Emulation-PUE Attacks

The PUE attacks are carried out by the emulation of primary users through the faking of their signal characteristics and, as such, misguiding cognitive radio to believe that it is occupied. In the case of a PUE attack, malicious secondary users (attackers) create similar signals to those the primary user would transmit, thereby reducing the possibility of other secondary users making use of the vacant channels. This effectively prevents SUs from using the available spectrum, and reduces spectral efficiency. It could also create an artificial spectrum scarcity in favor of an attacker. 

There are primarily two types of PUE attacks:

1. Selfish PUE Attacks: In this type of attack, the malicious user attempts to exclusively access the spectrum with the intention of barring other secondary users from sensing and accessing the available channels. This will directly translate into increased spectrum efficiency for the attacker at the cost of legitimate secondary users.

2. Malicious PUE Attacks: In such an attack, the only motive of the attacker is to create a denial-of-service for the legitimate secondary users. This might even further lead to overall network disruption, reduced throughput, and interference with primary users. 

Success of a PUE attack highly relies on the capability of an attacker to exactly emulate the signal characteristics of a primary user. This will be made easier by the shared nature of CRNs whereby secondary users share a frequency band hence it will be easy to emulate the primary users' signals undetected.

Security Measures Against PUE Attacks in CRNs

For such an attack, robust security protocols and advanced detection mechanisms must be met.

The main methods of securing CRNs against PUE attacks involve the following:

1. Signal characteristic authentication perhaps may provide some insights into the authentication of signal characteristics of a primary user to counter the PUE attacks.

This technique exploits specific features of the signal, which can be in location  or time-based information to differentiate the genuine primary user signals from the emulated ones. For example, with GPS-based localization, CRNs can verify the source of the signal and hence reject all transmissions emanating from unauthorized locations.

2. The machine learning algorithms may classify the signal patterns and thereby identify the genuine primary users and the potential attackers. Cognitive radio networks will improve the capability of detecting anomalies by training on primary user signal behavior. The Machine learning models, such as support vector machines, deep neural networks, and clustering algorithms can be trained on big datasets for improving the accuracy of detection.

3. Cooperative Spectrum Sensing: In cooperative spectrum sensing, various secondary users share their spectrum information with the ultimate goal of collaboratively detecting the unoccupied channels. CRNs can pool the sensing data together with the aim of optimizing the detection performance to reduce PUE attacks locally. Clearly, this makes the system more robust but at the same time requires that communications from secondary users be secure against attacks due to collusion.

4. Location Verification and Trust Management: The introduction of trust management mechanisms will help improve the dependability of the sensing data received from secondary users. CRNs can develop scores related to the trustworthiness of secondary users based on their history and reputation. The location verification technique allows for ensuring the signals that are received as from primary users emanate from the right geographical location, hence providing an added line of defense against PUE attacks.

5. Cryptographic Techniques: Some of the cryptographic techniques, such as PKI, can secure the communication channels of CRNs. This can be achieved through the encryption of control signals and thereby coming up with secure identifications for the primary users in order to avoid unauthorized access to the spectrum and hence protection against spoofed signals.

Conclusion

Security in Cognitive Radio Networks is fundamental to viable and dependable operations. Due to the dynamic and opportunistic nature of CRNs, their security protocols are always strict, especially on the issues of spectrum sensing and authentication of a primary user. This brings us to the PUE attack, which indeed is serious, as such attacks take advantage of openness and shared nature of cognitive networks to deny access to the spectrum for legitimate secondary users. In fact, if authentication of signals can be integrated with machine learning-based techniques and cryptography along with collaborative sensing, the impact can be substantially mitigated. Thus, future research in CRN should be directed toward the development of algorithms for improving sensing accuracy and robustness against sophisticated PUE attacks.

It is only then that the true potential of the CRNs can be exploited for maximum spectrum efficiency required to meet the ever-increasing demands of wireless communications. Security in CRNs would make the situation of spectrum scarcity proactive and adaptive enough to provide a solution toward a more efficient and resilient wireless ecosystem.