This dissertation aims to contribute to cognitive radio network research by investigating the impacts of Primary User Emulation Attacks (PUEA) on cognitive radio networks, the problem of trust between users in networks, and also the mitigation of PUEA activities in the network. This technique helps to reduce the errors of the secondary users in detecting the frequency of the signals of the primary users.
Background
To ensure that a secondary user is granted equal rights to the primary user, the secondary user impersonates the attributes of a primary user, causing the secondary user to behave maliciously. In this way, the malicious user gains unparalleled access to the primary user's spectral band.
Overview of Dissertation
- Objectives and Motivation
- Research Contribution
- Dissertation Review
- Resulting Peer Reviewed Publications
Chapter 3 presents the concept of a primary user emulation attack and investigates and analyzes its impact on cognitive radio networks. Mneney, βImpact of Primary User Emulation Attacks on Cognitive Radio Networksβ International Journal on Communications Antenna and Propagation, Vol.
Objective
Introduction to Cognitive Radio
7 Primary user emulation attacks, falsification of spectrum sensing data, objective function attacks, and Sybil attacks are examples of attacks on a cognitive radio network. A Sybil attack is a ubiquitous security threat in cognitive radio networks where a single malicious node disguises multiple identities and behaves as multiple geographically distinct nodes [59].
Cognitive Radio and Cognitive Radio Networks
What distinguishes cognitive radio from other traditional communication paradigms are its key functions as shown in figure 2-1. Adapt -: A cognitive radio/device can adapt their operating parameters, such as frequency, transmission power, modulation type, etc., to the variations of the surrounding radio environment.
Cognitive Radio Network Architecture
- Infrastructure Architecture
- Ad-hoc Infrastructure - In
- Mesh Architecture
Cognitive radio users can directly access base stations or use ad-hoc architectures. Cognitive radio users can directly access base stations or use other cognitive radio users as multi-hop relay nodes.
Spectrum Sensing in Cognitive Radio Networks
- Energy Detection
- Matched Filter Detection
- Cyclostationary Feature Detection
- Cooperative Spectrum Sensing
In centralized sensing, the fusion center (FC) collects full-spectrum sensing information from various secondary users and identifies available spectrum holes and broadcasts this information to secondary users. In the case of distributed sensing, secondary users exchange spectrum sensing information with each other and jointly decide which part of the spectrum is available. In a logical OR rule, even if one of the secondary users reports that the channel is busy, the channel status decision will be valid.
Security Threats in Cognitive Radio Networks
In the logical AND rule, a channel is set to be "busy" if all secondary users report that it is busy, and it will be "idle" if all secondary users do not sense any activity on the channel. Data in the wireless network can be intercepted without prior notice or the channel can be blocked or overused by adversaries [26], but cognitive radio technology opens more chances to threats and attacks due to its intrinsic nature. 57], the main user emulation attacks are considered to be one of the serious threats to cognitive radio systems due to the dangers it poses to spectrum sensing.
Chapter Summary
Cognitive radio networks are very similar to other wireless networks, as the operational nature of wireless media is the open air, but they are more vulnerable to attacks compared to wired networks. This means that these new threats and attacks are created by CRNs due to their unique cognitive properties. Like any other wireless communication technology, a comprehensive analysis of reliability and security challenges in CRNs is a very important step towards realizing lasting practical solutions.
Objective
Introduction
In the literature, this type of attack on cognitive radio networks is considered a Primary User Emulation Attack (PUEA) [27]. Therefore, we can define a primary user emulation attack as an attack in cognitive radio networks where the malicious user pretends to be the primary user and blocks inactive channels by broadcasting a similar signal to the primary user [28]. There are two types of primary user emulation attacks related to the primary user, depending on the target and purpose of the attack.
Impacts of Primary User Emulation Attacks in Cognitive Radio Networks
- System Model of Primary User Emulation Attacks
- Primary Exclusive Region
- An Analytical Model of Primary User Emulation Attacks
- Probability Density Function of Received Signal
- Using Neyman-Pearson Composite Hypothesis Test to Investigate the Impact of PUEA
- Simulations Setup and Results
- Observation and Discussion
That is, the received signal energy of the secondary user from the primary user ππ(π), is proportional to ππβ2, while the received signal energy of the secondary user from the malicious users ππ(π), is proportional to ππβ4. The received power at the secondary user from each of the malicious users is independently and identically distributed (i.i.d). The PDF of the received signal at the secondary user due to the primary sender and the PDF of the received signal at the secondary user due to the malicious user are calculated.
Chapter Summary
The results in Figure 3.5 show that the chance of false alarms increases as the number of malicious users in the network increases. Thus, for large π , the total received power from the malicious users may not be enough to successfully launch a PUEA in the network. The higher the number of malicious users in the network, the more power it generates, resulting in good secondary users making incorrect decisions, making the chances of PUEA in the network high.
Objective
Introduction
Creating a Trustworthy Cognitive Radio Network
36 To ensure a trustworthy cognitive radio network, a robust transmitter verification system [10] that can distinguish between trusted secondary users and malicious secondary users is also necessary. In hostile environments, such a mechanism can be integrated into the spectrum detection process of a cognitive radio network to increase its credibility.
System Model of a Cognitive Radio Network
Proposed Techniques
- Distance Estimated Based on Location Coordinates
- Distance Measured Based on Received Power Level
- Verification of Spectrum Occupancy
4.4) Based on the received power level, the distance between the secondary user and the primary user is determined. Therefore, the distance between users can be estimated based on the received power level. That means if the primary user is not using the spectrum, βπ+1 is not sent, so the malicious user cannot emulate the primary user and therefore the spectrum occupancy is verified.
Relative Trustworthiness of a User
Simulations and Discussion
We can see from Figure 4.3 that the estimated location of the primary user closely matches its actual location, which is 5 km away from the secondary users, i.e., the distance of the primary user from each secondary user is the same. If the reliability increases to 1, then we can conclude that we are communicating with the main user and not the malicious or untrusted user. If the reliability is close to 1, we can still conclude that he is a top user because of some uncertainties that may tend to reduce the reliability.
Chapter Summary
Objective
Introduction
The results obtained by our proposed cooperative spectrum sensing technique for energy detection are compared with the conventional cooperative spectrum sensing approach for energy detection discussed in [48] to determine its performance.
A System Model of a Cognitive Radio Network with PUEA Present
48 further consider a scenario where the PUEA continuously sends spurious signals in empty and busy lanes to selfishly acquire a lane, thereby forcing a secondary user to vacate an existing lane. πππ₯ππ as the signals emitted by the primary user and PUEA, respectively, with power βππ and βππ at the πth instant. We define π¦ππ as the signal received at π secondary user at π time.
Proposed Cooperative Spectrum Sensing Technique against PUEA
From the cooperative spectrum detection algorithm in [68], the probability of detection (ππ) and the probability of false alarm (ππ) for OR/AND merger rules can be derived. For the OR fusion rule, the (ππππ ) and (ππππ ) of the final decision made by the fusion center using the local spectrum decisions can be written as. πππ΄ππ· = βππ=1πππ , (5.5) πππ΄ππ· = βππ=1πππ , (5.6) where ππ π and πππ are the detection and false alarm probabilities in the local spectrum detection process of all secondary users in the cognitive radio network, respectively.
Proposed Energy Detection Based Cooperative Spectrum Sensing with PUEA
In determining the performance of the analyzed spectrum sensing method of the previous section, we use Neyman-Pearson criterion [51] to determine the probability of detection using energy detection-based cooperative spectrum sensing. -Pearson technique provides a threshold for detection subject to a constant probability of false alarm πππ. are Gamma function and upper incomplete Gamma function [53], respectively. 5.20). In evaluating the system performance, a parameter related to spectrum sensing, called probability of error, is used.
Proposed Technique for the Case of an Always Present Attacker in the Network
54 Now that PUEA is included in our proposed method, we can evaluate the method by comparing it with the conventional spectrum sensing method for energy detection, which does not consider an attacker in the system, as proposed in [48]. In the presence of a constant attacker sending spoof signals over the licensed frequency band, the PUEA signal is received by the secondary users under both π»0 and π»1. The probability of detection (πππ) is now expressed as. 5.26) Similarly to the previous section, when formulating the cooperative spectrum sensing technique based on energy sensing, ππ will also satisfy the central Chi-square distribution (π2) with 2π degrees of freedom and parameter π4,π2 and is given by.
Simulations and Discussion
59 Figure 5.6 also illustrates the error probability versus the false alarm probability in the AND fusion rule with the number of secondary users π set to 12. Also as π½ increases, there is an increase in the error probability for the conventional spectrum sensor method. 61 From all the results, we can conclude that the conventional spectrum sensing method using the AND fusion rule often leads to a low error probability in the network.
Chapter Summary
So if the conventional spectrum detection method is to be used, it should be used under the EN fusion rule. But again, our proposed spectrum detection method outperforms the conventional spectrum detection method in both the OR and EN fusion rules, with a much greater improvement in the OR fusion rule. In conclusion, we can say that the best possible mitigation of PUEA in a cognitive radio network is achieved using the proposed spectrum sensing method in the OR fusion rule.
Conclusion
The resulting PDF is used in a Neyman-Pearsons composite hypothesis test to investigate the effects of PUEA in the network. This means that the high quality and reliability of the received spectrum sensing occupancy information is very essential for the fusion center that decides the presence and absence of a primary user in the network. For the case of an ever-present attacker in a network, a cooperative spectrum sensing technique based on energy sensing was proposed.
Future work
Yao, βCooperative spectrum sensing in cognitive radio networks in the presence of primary user emulation attack,β IEEE Transactions on Wireless Communications Vol. Balakrishnan, "Cooperative spectrum sensing in cognitive radio networks: a survey" Elsevier Journal on Physical Communications, Vol. Li, "Defense against primary user emulation attacks in cognitive radio networks using advanced encryption standard."
