Collaborative Spectrum Sensing Research Articles

Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things.

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds.

5G-RCOLAB: A system level simulator for 5G and beyond in rural areas

This paper describes 5G-RCOLAB, an open source simulation tool for performance evaluation of 5G/6G wireless networks in rural areas. The tool is designed with a layered architecture, based on three modules: (i) a link-level simulator of a communication channel for remote areas, with a mapping Link to System (L2S) interface; (ii) a Long-Term Evolution (LTE)-based physical layer adapted for remote areas, together with a Medium Access Control (MAC) layer that includes a novel Collaborative Spectrum Sensing (CSS) technique with security features for Byzantine attacks and spectrum usage efficiency; and (iii) an application oriented network layer that emulates the behavior of typical users in rural areas. The tool has the goal to reduce simulation complexity of large scale 5G rural networks and at the same time, to represent coherently the features of the physical and MAC layers, that include new channel models that cover long distances with the use of TV White Spaces (TVWS) in the Ultra High Frequency (UHF) and Very High Frequency (VHF) bands, as well as novel CSS techniques to improve spectrum efficiency. The 5G-RCOLAB was validated both by field test measurements from a Proof of Concept (PoC) prototype and by the simulation and evaluation of three 5G common core use cases in rural areas. The results show that the 5G-RCOLAB features allow a better evaluation of novel technologies implemented in the physical and MAC layers and provide paths for improvements and deployments of cost effective solutions of 5G in rural or remote areas.

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

A Collaborative Spectrum Sensing Algorithm Based on Reputation Update against Malicious User Attacks

In cognitive radio networks, collaborative spectrum sensing (CSS) algorithms could improve spectrum detection performance; however, most explorations are based on reliable network environments. In the real network environment, there may be malicious users that bring wrong spectrum sensing results and attacks designed by them remarkably reduce the spectrum efficiency. In order to resist the attacks of malicious users, this paper proposes a CSS method based on the reputation update. By setting an appropriate reputation threshold, the user fusion center selects the sensing user with a higher reputation to participate in the CSS. Each user’s reputation value is then updated according to whether its local sensing result matches the final judgment result. This article chiefly discusses scenarios for application of three information fusion rules. The simulation results reveal that the proposed approach with reputation update outperforms the conventional CSS algorithm for a variety of judgment rules. The proposed algorithm is capable of preventing lower reputation users from participating in the CSS, filtering out malicious users, and eliminating the impact of malicious users’ attacks.

Hierarchical Cat and Mouse based ensemble extreme learning machine for spectrum sensing data falsification attack detection in cognitive radio network

While cognitive radio networks (CRNs) provide a promising solution to mitigate the scarcity of the radio spectrum, they are still susceptible to several security threats. Until now, only a few researchers considered the usage of intrusion detection systems (IDSs) to combat the threads against CRNs. In CRN, spectrum sensing is considered as a significant function and collaborative spectrum sensing (CSS) has known to result in better sensing accuracy. However, CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack wherein a node maliciously falsifies the sensing report prior to sending it to the Fusion Centre (FC), with the aim of disrupting the spectrum sensing process. Thus, a new novel machine learning model is significant for detecting of attacks in a CRN. In this paper, a Hierarchical Cat and Mouse Based Ensemble Extreme Learning Machine (HCM-EELM) model has proposed to analyse the security threats in CRN for enhancing the network performance by minimizing the attacks. Also, it is attempted at investigating the viability of machine learning classification for detecting SSDF attack in a binary reporting CRN. History of the sensing reports accumulated at the FC are applied to acquire the temporal characteristics of SU, and thereby establishing the training and testing data-sets. Here, three attacks namely Random Yes (RY) attack, Random No (RN) attack, and Random False (RF) attack have considered. Moreover, the proposed model is implemented in MATLAB software and the stimulation results are compared with existing models such as Extreme Learning Machine (ELM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Support Vector Machine (SVM). The result has determined in terms of performance metrics such as accuracy, precision, recall, F1-score and FAR. As a result, better accuracy for 99.7% has achieved than the existing models by detecting the attacks efficiently.

Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework

Multiple secondary users (SUs) perform collaborative spectrum sensing (CSS) in cognitive radio networks to improve the sensing performance. However, this system severely degrades with spectrum sensing data falsification (SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing, each SU compares the decisions of CSS and independent spectrum sensing (IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time slot. Simulation results verify effectiveness of the proposed algorithm.

A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs.

Detecting Byzantine attacks in Cognitive Radio Networks : A two-layered approach using Hidden Markov Model and machine learning

Cognitive Radio Networks (CRN) are gaining popularity due to their ability to harness the TV whitespace. CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU). Such a collaborative sensing mechanism enables an increased detection accuracy of the incumbent’s presence (or absence) as all the SUs send their local sensing reports to a Fusion Centre (FC), where these sensing reports are aggregated to arrive at a final sensing decision. However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.k.a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. The first layer, Processing layer, uses Hidden Markov Model (HMM) to get a probabilistic relationship between the PU’s states and the SUs’ sensing reports. This generates the required dataset for the next layer. The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters. Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users.

Throughput Maximization of Collaborative Spectrum Sensing Under SSDF Attacks

In cognitive radio networks (CRNs), secondary users (SUs) can opportunistically access the licensed bands through spectrum sensing while primary users (PUs) are inactive. While collaborative spectrum sensing (CSS) exploits the benefits of cooperation, the spectrum sensing data falsification (SSDF) attackers are subsequently introduced, which aim at undermining the performance of CRNs. We thus are motivated to maximize the throughput of the CRNs under SSDF attacks. First, to better discern SSDF attackers, we designate a double reputation based algorithm to differentiate the SSDF attackers and honest secondary users (HSUs). Then the trade off problem is formulated to find the optimal network parameters including, sensing duration, global decision threshold, energy detection threshold, reputation thresholds for maximizing the throughput of the secondary users network (SUN) subject to limited interference with the PUs. To address the non-convexity of the optimization problem, dummy variables are introduced and the alternative optimization method is adopted. Numerical results show that the proposed algorithm is effective in increasing throughput of the SUN with sufficient protection to the PUs.

Gini Index Test Metric Based Collaborative Sensing of Spectrum for Cognitive Networks

The key confront for a cognitive radio network is to identify the presence of primary users consistently so as to curtail the intrusion to license approved communications. Consequently, sensing of spectrum is a main significant requisite of a cognitive network. Nevertheless, because of the ambiguity in the channel, local interpretations don’t provide a trustworthy solution and therefore collaboration is needed between the users. Collaborative spectrum sensing is a competent scheme to enhance the spectrum utility in wireless networks, which utilizes cooperation between multiple nodes to prevail over the inadequacy of single-node to improve the performance of detection. In this paper, a detailed investigation, simulation and comparative analysis of collaborative scheme for sensing the spectrum of cognitive networks based on Gini Index test metrics is done and the results are compared with various other detecting schemes. The simulation is accomplished by means of Matlab R2014 software.

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

The Cognitive radio network (CR) is a widespread technology in which the Secondary users are assumed to be of the winning users to acquire the spectrum by reducing the false alarm possibilities and the false detection of the user assumed to be original user in nature is restricted with the usage of Spectrum monitoring agents. The collaborative spectrum sensing (CSS) is an approach that will identify the false intruder in the CR networks, here it is proposed with the Enhanced Q-Learning model with Coalition Game approach (EQLCG) to outline the energy enhancement. Besides an approach on Greedy Bidding is used to allocate the spectrum to the winning secondary user (SU) based on the idle primary user to strengthen the spectrum sensing. The winning secondary user forms a communication establishment with the neighbouring SU to eradicate the miss detection probability based on group level cooperation. The simulation experiment analyses the cluster level security with energy monitoring that has been performed using the analysis of interference by applying the coalition game theory modelling and the information obscured by the attacker is reduced with the usage of enhanced Q-learning, and the results prove that overhead is substantially monitored. The proposed paper enhances the security in physical layer with energy conservation and maintains the spectrum usage for application purpose. The proposed simulation approach reduces the miss detection and false alarm probabilistic approach while compared with Stackelberg and Bayesian game models.

Design of Optimal Non-Uniform Quantizer in Imperfect Noisy Reporting Channels for Collaborative Spectrum Sensing

We design an optimal non-uniform quantizer for collaborative spectrum sensing (CSS) in cognitive radio networks (CRNs). We assume non-ideal reporting channels are and consist of additive white Gaussian noise (AWGN). To design the optimum quantization at the transmitter, we minimize the mean square error (MMSE) at the receiver. The local data at each secondary user (SU) is quantized and transmitted via an AWGN reporting channel. Then, quantized symbols are received and decoded at the fusion center (FC) by a maximum a-posteriori probability (MAP) rule and, the final collaborative decision is made. By this method, a prediction is done for representation points of the quantizer and moreover, quantization and reporting channel noises are minimized, simultaneously. In addition, by taking into account the reporting channel noise in quantizer design, detection performance will be adaptive to change of signal to noise ratio (SNR) in the reporting channel. The simulation results demonstrate the superiority of the proposed quantizer compared to different quantizers in practical noisy reporting channels.

Detecting Byzantine attack in cognitive radio networks using machine learning

One primary function in a cognitive radio network (CRN) is spectrum sensing. In an infrastructure-based CRN, instead of individual nodes independently sensing the presence of the incumbent signal and taking decisions thereon, a fusion center (FC) aggregates the sensing reports from the individual nodes and makes the final decision. Such collaborative spectrum sensing (CSS) is known to result in better sensing accuracy. On the other hand, CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack (a.k.a. Byzantine attack) wherein a node maliciously falsifies the sensing report prior to sending it to the FC, with the intention of disrupting the spectrum sensing process. This paper investigates the use of machine learning techniques, viz., SVM, Neural Network, Naive Bayes and Ensemble classifiers for detection of SSDF attacks in a CRN where the sensing reports are binary (i.e., either 1 or 0). The learning techniques are studied under two experimental scenarios: (a) when the training and test data are drawn from the same data-set, and (b) when separate data-sets are used for training and testing. Under the first scenario, of all the techniques, NN and Ensemble are the most robust showing consistently very good performance across varying presence of attackers in the system. Moreover performance comparison with an existing non-machine learning technique shows that the learning techniques are generally more robust than the existing algorithm under high presence of attackers. Under the second scenario, in a limited environment, Ensemble is the most robust method showing good overall performance.

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Due to the potential applicability of spectrum sensing, cognitive wireless sensor networks have attracted plenty of interest in the research community to improve the bandwidth utilization for practical applications. To alleviate the effect of multi-path fading and resolve the problem of hidden terminal, collaborative spectrum sensing (CSS) is regarded as effective technology to obtain better sensing accuracy. However, CSS is usually vulnerable to the attack behaviors originated from malicious sensor nodes. In this paper, an enhanced cooperative spectrum sensing scheme against SSDF attack based on Dempster-Shafer evidence theory for cognitive wireless sensor networks is introduced. First, the holistic credibility of sensor nodes can be evaluated according to the real-time difference between them and the statistical sensing behaviors. Furthermore, the basic probability assignment function can be defined based on evidence theory, and the credibility of sensor nodes can be estimated. Finally, by using the weighted probability assignment for each cognitive sensor node, the fusion center can reduce the influence of malicious sensor nodes on the final decision and ensure the reliability of reports from cooperative sensor nodes. Simulation results demonstrate that the proposed method can resist SSDF attacks significantly and outperform the traditional secure schemes in aspect of sensing accuracy.

Entropy-Based Weighted Decision Combining for Collaborative Spectrum Sensing Over Byzantine Attack

Collaborative spectrum sensing (CSS) as a promising solution is widely adopted for identifying available spectrum. However, such cooperation is vulnerable to be threatened by Byzantine attack. To defend against such attack, a low-complexity entropy-based credibility weighted CSS scheme is proposed, where each sensing node’s weight is evaluated based on the inconsistency property of received data at the fusion center within two consecutive sensing slots during a sliding window . Unlike the existing approaches, the proposed scheme is suitable for homogeneous and heterogeneous scenarios and neither requires prior knowledge of attack behaviors nor reference sensing information. Simulation results show the validity of the proposed strategy to resist attack scenarios where a large fraction of attackers with varying probabilities range between 0.2 and 0.8.

Defending Against Massive SSDF Attacks From a Novel Perspective of Honest Secondary Users

In cognitive radio networks (CNRs), spectrum sensing data falsification (SSDF) attacks can severely deteriorate the performance of the collaborative spectrum sensing (CSS), which may be worse compared with the performance of the independent spectrum sensing (IndSS) under massive SSDF attacks. In this letter, a novel algorithm for defending against massive SSDF attacks is investigated from the perspective of honest secondary users (HSUs). In the proposed algorithm, each HSU evaluates the reliability of the CSS results. The CSS results with high reliability value are employed by secondary users (SUs) as final sensing results. However, its own sensing results are used if reliability value of CSS results is lower than the certain threshold. Theoretical derivation for the threshold of the reliability value is explained. Simulation results verify the effectiveness of the proposed scheme and indicate that our proposed algorithm can be considered as the extension of existing anti SSDF attacks algorithms.

Byzantine Attacker Identification in Collaborative Spectrum Sensing: A Robust Defense Framework

The problem of Byzantine attack in collaborative spectrum sensing (CSS) is considered in this paper. To defend against Byzantine attack, a robust defense framework is proposed to efficiently identify the Byzantine attackers. Specifically, we first propose a robust defense framework, where a reference is built based on the extended sensing, and the transmit results and sensors are continuously evaluated via the reference and identified at intervals. In the framework, except of data falsification, multiple practical factors are considered, including the variation characteristic of sensors’ attributes, reporting channel imperfection, and inference errors based on the transmit results. Further, we derive the closed-form expressions of the reference and the identification performance and make optimization of the identification threshold in two cases: with and without the prior knowledge of attack behaviors, where the probability of correctly detecting Byzantine attackers is maximized under the constraint of the probability of falsely identifying honest sensors as attackers. In particular, when the prior knowledge is unavailable, maximized likelihood estimation is made based on the reference to achieve the optimization. Furthermore, we present in-depth simulations to demonstrate the high robustness of the proposed defence framework to multiple practical factors under a homogeneous scenario and a heterogeneous scenario.

On MMD-Based Secure Fusion Strategy for Robust Cooperative Spectrum Sensing

Collaborative spectrum sensing (CSS) in cognitive radio-based networks (CRNs) is vulnerable to spectrum sensing data falsification (SSDF) attack. Existing defense mechanisms are commonly subject to major limitations with unrealistic assumptions which can be easily violated in future wireless networks. Such assumptions include the number of honest users is in majority, the attackers’ flip rates are identical and fixed, the adoption of hard decision approach in the fusion strategy and only TV sets are considered as the primary users to be protected. Essentially, all existing representative schemes utilize certain low-dimensional human-observed metric to distinguish malicious users and honest users based on domain knowledge. Therefore, these defense mechanisms cannot perform properly under certain condition, such as sensing reports with different distributions but have equal mean and variance. In this paper, we propose a secure fusion strategy which adopts “soft decision” method and can distinguish malicious users and honest users under any distribution of sensing reports using maximum mean discrepancy (MMD). Our proposed CSS scheme is suitable for general CRN application scenarios. The simulation results reflect the effects of different kernel functions and window size on the system performance, and show our proposed defense mechanism outperforms the existing works.

Collaborative spectrum sensing mechanism based on user incentive in cognitive radio networks

In cognitive radio networks (CRNs), it is important for secondary users (SUs) to efficiently reuse spectrum without interfering communication of primary users (PUs). To acquire the communication opportunities, SUs first need become winning, i.e., suppressing its own miss detection probability under the upper limit imposed by PUs. Collaborative spectrum sensing (CSS) is a promising approach to improve the detection performance of SUs, where multiple SUs form a group and share their sensing results. In addition, the probability that winning SUs correctly detect idle state of PUs’ spectrum will affect their communication opportunities. We first formulate a global optimization problem as integer linear programming (ILP), which maximizes both the number of winning SUs and total communication opportunities among them. In CSS, we also have to consider the selfishness of SUs because winning SUs will compete with group members to acquire their own communication opportunities. To cope with this competitive problem in addition to scalability problem of the global optimization, we further formulate an individual optimization problem, which can be solved by a user-incentive based CSS mechanism composed of PU selection and group (re)formation among SUs, where communication opportunities are allocated to SUs according to their detection performance. Through simulation experiments, we show the proposed mechanism considering selfishness of SUs is competitive with the existing scheme based on group-level cooperation, in terms of both the ratio of winning SUs and total communication opportunities among them. Comparing with the global optimization, we also show that the proposed mechanism can support larger-scale systems with performance improvement. In addition, we show that the proposed mechanism can achieve stable group formation even under SUs’ selfish behavior. Finally, we discuss how the number of PUs affects the system performance.

A similarity algorithm based on hamming distance used to detect malicious users in cooperative spectrum sensing

The collaborative spectrum sensing (CSS) methods have been proposed to improve the sensing performance. However, studies rarely take security into account. CSS methods are vulnerable to the potential attacks from malicious users (MUs). Most existing MU detection methods are reputation-based, it is incapable as the attack model is intelligent. In this paper, a Hamming distance check (HDC) is proposed to detect MUs. The Hamming distance between all the sensing nodes is calculated. Because the reports from MUs are different from honest users (HUs), we can find the MUs and exclude them from the fusion process. A new trust factor (TF) is proposed to increase the effects of trustworthy nodes in the final decision. The proposed algorithm can effectively detect the MUs without prior knowledge. In addition, our proposed method can perform better than the existing approaches.