Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks

Cognitive radio (CR) technology allows secondary users (SUs) to share primary user's (PU's) spectrum band by adapting its transmission power in a sensing-based spectrum sharing manner. Spectrum sensing is an important activity in the CR network. Spectrum sensing is used to detect availability of a channel in the PU's spectrum band for the SUs. Presence of attackers such as primary user emulation (PUE) attackers in CR networks imposes some security threats to the SUs which affects the spectrum sensing decisions. In PUE attack, SUs are mislead from using the PU's spectrum band which results in degradation of SU's throughput. To enhance SU's throughput, we have considered hybrid spectrum access scheme with spectrum prediction in this paper. In hybrid spectrum access scheme, the SU switches between overlay or underlay modes based on spectrum sensing decision available at SU. In predictive scheme, the SUs will sense only those channels which are predicted to be idle. Simulation results show that degradation in SU's throughput due to presence of PUE attackers can be compensated by using spectrum prediction in conjunction with hybrid spectrum access.

Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSN), where malicious secondary users (SU) try to emulate primary users (PU) to maximize their own spectrum usage or obstruct other SU from accessing the spectrum. In this paper, we have designed an application to monitor the SU’s behavior with respect to the CWSN normal behavior profile towards it’s one hop neighbor. Abnormal behavior towards PUE attack of any SU helps us to identify PUE attackers in the network. Our application does not require extensive computational capabilities and memory and therefore suitable for resource constraint cognitive sensor nodes.

The primary user emulation attack (PUEA) is one of the most common attacks affecting the physical layer of the cognitive radio network (CRN). In this attack, a malicious user or a selfish user mimics the signal characteristics of the primary user (PU) to deceive the legitimate secondary user (SU) causing it to leave the available channel while the real PU is absent hence, detecting this attacker is vital in building a real CRN. In this paper, the PUEA is detected based on the Time difference of Arrival (TDOA) localization technique using the particle swarm optimization (PSO), novel bat algorithm (NBA), and the modified particle swarm optimization (MPSO) to minimize the localization error from the TDOA measurement and comparison is made among the three algorithms in term of the localization accuracy, convergence rate, computation time via simulation using the MATLAB simulation tool by running the monte Carlo 1000 times. The performance of the techniques was evaluated using the mean square error (MSE) and cumulative distribution function (CDF) and the MPSO algorithm out-performed the PSO and the NBA

SummarySpectrum sensing in cognitive radio networks (CRNs) is subjected to some security threats such as primary user emulation (PUE) attack and spectrum sensing data falsification (SSDF) attack. In PUE attack, a malicious user (MUPUE) transmits an emulated primary signal throughout the spectrum sensing interval to secondary users (SUs) to forestall them from accessing the primary user (PU) spectrum bands. In SSDF attack, malicious users (MUSSDF) intentionally report false sensing decisions to the fusion center (FC) to influence the overall decision. While most of the existing literatures have studied the effects of these 2 types of attacks separately, the present paper evaluates the secondary network performance in terms of throughput under both the PUE and SSDF attacks with improved energy detectors (IEDs) where SU's spectrum access is hybrid, ie, either in overlay or in underlay mode. An analytical expression on throughput of SU under the simultaneous influence of both of these attacks is developed. Impact of several parameters such as IED parameter, attacker probabilities, and attacker strength on the throughput of SU is investigated. Performance of the present scheme is also compared with only PUE and only SSDF attacks. A simulation test bed is developed in MATLAB to validate our analytical results.

The primary user emulation attack (puea) is considered to be one of the common threats in cognitive radio networks (crns). in this problem, an attacker emulates the primary user (pu) signal to deceive other secondary users (sus) and forcing them to leave the white spaces (free spaces) in the spectrum assigned before to the PU. The PUEA is detected and localized using the time-difference-of-arrival (TDOA) localization technique based on stochastic optimization algorithms. Particle swarm optimization (PSO) algorithms are proposed to minimize the cost function provided by the TDOA measurements and to increase the accuracy of the detection. The PSO variants are evolved by changing the parameters of the standard PSO such as inertia weight and acceleration constants. These approaches are presented and compared with the standard PSO in terms of convergence speed and processing time. This paper presents the first study of designing a PSO algorithm suitable for the localization problem and it will be considered as a good guidance for applying the optimization algorithms in wireless positioning techniques. Mean square error (MSE) and cumulative distribution function (CDF) are used as the evaluation metrics to measure the accuracy of the proposed algorithms. Simulation results show that the proposed PSO approaches provide higher accuracy and faster convergence than the standard PSO, social spider optimization (SSO), cuckoo search (CS), firefly optimization (FA) and Taylor series estimation (TSE) methods.

As the proliferation of wireless handheld devices such as smart phones and tablets grow exponentially, new technologies have to be adopted to efficiently use the radio frequency spectrum. Cognitive Radio(CR) network offers a promising solution to improve spectrum utilization and efficiency by harnessing the available spectrum resources in real-time. However, participants in the CR network can comprise of malicious adversaries who adopt dishonest and non-cooperative strategies to attack the network. In our research, we focus on a critical attack on the CR network: Primary User Emulation (PUE). In a PUE attack, the malicious adversary violates spectrum sharing etiquette by impersonating the primary user at the expense of other unselfish secondary users. To thwart such an attack, we present a strategy to authenticate the primary user, which involves collaborative learning among CR users.

Promising to significantly improve spectrum utilization, cognitive radio networks (CRNs) have attracted a great attention in the literature. Nevertheless, a new security threat known as the primary user emulation (PUE) attack raises a great challenge to CRNs. The PUE attack is unique to CRNs and can cause severe denial of service (DoS) to CRNs. In this paper, we propose a novel PUE detection system, termed Signal activity Pattern Acquisition and Reconstruction System. Different from current solutions of PUE detection, the proposed system does not need any a priori knowledge of primary users (PUs), and has no limitation on the type of PUs that are applicable. It acquires the activity pattern of a signal through spectrum sensing, such as the ON and OFF periods of the signal. Then it reconstructs the observed signal activity pattern through a reconstruction model. By examining the reconstruction error, the proposed system can smartly distinguish a signal activity pattern of a PU from a signal activity pattern of an attacker. Numerical results show that the proposed system has excellent performance in detecting PUE attacks.

Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks.

The inherent nature of cognitive radio (CR) networks has brought new threats to wireless communications. Primary user emulation attack (PUEA) has been widely studied as a serious threat to cooperative spectrum sensing (CSS) in CR networks. In PUEA, a malicious user can obstruct CR users from accessing idle frequency bands by imitating licensed primary user (PU) signal characteristics. The present study introduces a new CSS scheme in the presence of a malicious PUEA based on multi-level hypothesis testing (MLHT). In the proposed method, generalizing from binary hypothesis testing to MLHT, we partition the decision space to four decision options and apply minimum Bayes cost criteria to determine the channel status. We also discuss practical limitation issues that need to be considered when applying the MLHT approach. Simulation results are provided to indicate the performance improvement of the proposed MLHT method against PUEA, compared with the conventional method.

This paper presents a method to alleviate the effects of primary user emulation attacks in cognitive radio (CR) networks. The proposed method uses a simplified belief propagation (BP) algorithm that is able to identify whether a transmitter is a legitimate primary user or an attacker. In a primary user emulation attack (PUEA) a transmitter impersonates a primary user (PU) in order to deceive secondary users (SU) into believing that a channel is occupied. As a result, secondary users must vacate the channel immediately. This paper presents a simplified belief propagation method as a defence strategy against primary user emulation attacks. In our method each secondary user examines an incoming signal from a transmitter and determines with a certain probability whether the transmitter is a legitimate user or not. This probability is known as the belief. The beliefs at each secondary user are reconciled and a final belief is compared to a predefined threshold. If the final belief is below the threshold, the user is identified as an attacker. If it is above the threshold, the user is deemed a primary user. This result is then propagated throughout the network so that all users on the network are aware of the attacker. In this paper, we present a method based on the belief propagation framework. The proposed method converges in a single iteration; this is the result of a redefined messaging protocol and a simplified belief equation. As a result, the computational complexity of the method is reduced significantly, while still maintaining a high level of accuracy.

One of the most challenging issues in Cognitive Radio Networks (CRN) is to detect Primary User Emulation Attack (PUEA). In the absence of Primary Users (PU), the attackers mimic PUs’ signal characteristics to fool legitimate Secondary Users (SU) that evacuate the channel for them, in order to use the channel selfishly. Many works have been done to detect PUEA; among them, localization and encryption and so on are the examples. Recently, game theory has been used to detect PUEA. In this paper, a method based on game theory is proposed, that without using any complex calculation and second methods (RSS, GPS and so on), PUEA can be detected. This method is especially proposed for MANET and can be used in any circumstance of CRNs (ad-hoc, centralized, distributed…). It is reliable, with minimum miss detection and no false alarm of PU. Simulation results show that the proposed method has good operations even in dense networks and ultra-dense networks.

With the ever-increasing demand for wireless spectrum for various applications, the need for intelligent and efficient utilization of the limited resource has been felt tremendously in the last few years. Cognitive radio network (CRN) with its dynamic spectrum sharing techniques coupled with flexibility, reliability, awareness and adaptability offers a fitting solution. Unfortunately, this brings along myriads of threats and attacks specific to CRN. In this paper, a specific kind of attack called Primary User Emulation Attack (PUEA) by Selfish Secondary Users has been considered and various algorithms to counter this have been explored. In PUEA an adversary’s CR transmits signals whose characteristics emulate those of the Primary User (PU). This paper focuses on location-based security algorithms to counter PUEA which include Distance Ratio Test (DRT) and Distance Difference Test (DDT) for transmitter verification. Certain modifications to DRT and DDT have been proposed to alleviate the inherent drawbacks of the techniques. This includes using a random configuration of the location verifiers (LVs) to counter an adversary trying positions equivalent to that of a PU. In light of certain shortcomings of this initial proposed approach, a superior DRT over DDT assortment algorithm is proposed. Also, an LV location optimization algorithm using genetic algorithm based on similarity index is designed to optimize the locations of the LVs.

Secure cooperative spectrum sensing under primary user emulation attack in cognitive radio networks: Attack-aware threshold selection approach

In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders’ payoff is determined to obtain optimal defense decisions.

The Cognitive Radio (CR) is the key technology to deal with spectrum scarcity by allowing unlicensed CR users to coexist with existing users in licensed spectrum bands without interfering with binding communications. Cognitive technology provides the spectrum ability to be shared with licensed networks. This radio system can adjust its transmitter and recipient parameters on the basis of interaction with the current conditions in the environment. Due to this flexibility cognitive radios are exploring different types of threats and security attacks. In this paper, we focus primarily on the Primary User Emulation Attack (PUEA) which is a major attack in the cognitive radio network. PUEA is similar to a Denial-of-service attack that can seriously disrupt the spectrum sensing process and deny other legitimate secondary users to access the available spectrum. We proposed a Neyman-Pearson composite hypothesis test (NPCHT) based analytical model to study the impact of PUEA in a fading wireless communication environment. Simulation results show that the proposed techniques can substantially reduce the impact of the malicious attack on the network