Spectrum Sensing Data Falsification Research Articles

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive IoT networks

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive <scp>IoT</scp> networks

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

AbstractTo improve spectrum utilization, cognitive radio (CR) enables unauthorized internet of thing (IoT) devices to opportunistically access the channel underutilized by the primary user (PU) in a cognitive IoT (CIoT). To this end, cooperative spectrum sensing (CSS) plays a crucial role in CR technology, which leverages the spatial diversity of corroborative IoT devices to accurately detect the PU signal. However, this open cooperative paradigm may suffer from spectrum sensing data falsification (SSDF) attack in which malicious IoT devices intentionally mislead the fusion center (FC) by providing false sensing results to make an incorrect global decision regarding the PU status. To effectively characterize the attack behavior of malicious IoT devices, we propose a massive SSDF attack model described by the attack cycle and malicious ratio within a sensing period to characterize the malicious behaviors. Additionally, we introduce a delivery evaluation mechanism and propose a dynamic sliding window‐CSS (DSW‐CSS) to mitigate the impact of massive SSDF attack. Moreover, we introduce a sequential reporting mechanism to further reduce the number of samples required by the global decision‐making of the FC. Finally, simulation results show the flexibility and aggressiveness of the proposed attack model and demonstrate the correctness and effectiveness of DSW‐CSS.

Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train models and the privacy concerns of such scenarios limit the applicability of centralized ML/DL-based approaches. Federated learning (FL) addresses these limitations by creating federated and privacy-preserving models. However, FL is vulnerable to malicious participants, and the impact of adversarial attacks on federated models detecting spectrum sensing data falsification (SSDF) attacks on spectrum sensors has not been studied. To address this challenge, the first contribution of this work is the creation of a novel dataset suitable for FL and modeling the behavior (usage of CPU, memory, or file system, among others) of resource-constrained spectrum sensors affected by different SSDF attacks. The second contribution is a pool of experiments analyzing and comparing the robustness of federated models according to i) three families of spectrum sensors, ii) eight SSDF attacks, iii) four scenarios dealing with unsupervised (anomaly detection) and supervised (binary classification) federated models, iv) up to 33% of malicious participants implementing data and model poisoning attacks, and v) four aggregation functions acting as anti-adversarial mechanisms to increase the models robustness.

CyberSpec: Behavioral Fingerprinting for Intelligent Attacks Detection on Crowdsensing Spectrum Sensors

Integrated sensing and communication (ISAC) is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabilities of resource-constrained spectrum sensors and the possibility of being manipulated by users with physical access complicate their protection against spectrum sensing data falsification (SSDF) attacks. Most recent literature suggests using behavioral fingerprinting and Machine/Deep Learning (ML/DL) for improving similar cybersecurity issues. Nevertheless, the applicability of these techniques in resource-constrained devices, the impact of attacks affecting spectrum data integrity, and the performance and scalability of models suitable for heterogeneous sensors types are still open challenges. To improve limitations, this work presents seven SSDF attacks affecting spectrum sensors and introduces CyberSpec, an ML/DL-oriented framework using device behavioral fingerprinting to detect anomalies produced by SSDF attacks affecting resource-constrained spectrum sensors. CyberSpec has been implemented and validated in ElectroSense, a real crowdsensing RF monitoring platform where several configurations of the proposed SSDF attacks have been executed in different sensors. A pool of experiments with different unsupervised ML/DL-based models has demonstrated the suitability of CyberSpec detecting the previous attacks within an acceptable timeframe.

Massive SSDF Attackers Identification in Cognitive Radio Networks by Using Consistent Property

To resist probabilistic spectrum sensing data falsification (SSDF) attacks in cognitive radio networks (CRNs), we in this paper propose a simple and effective attacker identification scheme from the perspective of each secondary user's (SU) historical sensing data without any prior knowledge about the strategy of attackers. In the proposed algorithm, the inconsistency property of the historical data within two consecutive sensing slots during a sensing time window is extracted to characterize different attack behaviors. Further, an optimal identification threshold is obtained for each SU and the analytical expressions of identification performance are also derived. Finally, simulation results along with theoretical analysis show the validity of the proposed scheme to defend against massive probabilistic SSDF attacks with low computation cost.

Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks

Crowdsensing platforms collect, process, transmit, and analyze spectrum data worldwide to optimize radio frequency spectrum usage. However, Internet-of-Things (IoT) spectrum sensors, performing some of the previous tasks, are exposed to software manipulation aiming to execute spectrum sensing data falsification (SSDF) attacks to compromise data integrity and spectrum optimization. Novel intrusion detection systems (IDSs) combining device fingerprinting with Machine and Deep Learning (ML/DL) improve the limitation of traditional solutions and remove the necessity of redundant sensors and reputation mechanisms. However, they fail when detecting SSDF attacks accurately while protecting sensors privacy. This work proposes a novel host-based and federated learning-oriented IDS for IoT spectrum sensors that considers unsupervised ML/DL and fingerprints based on system calls. The framework detection performance and consumption of resources are analyzed in local and federated scenarios with six spectrum sensors deployed on Raspberry Pis. The obtained results significantly improve related work when detecting SSDF attacks while protecting sensors privacy, and consuming CPU, memory, and storage of sensors in a reduced manner.

Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing

In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.

SpecForce: A Framework to Secure IoT Spectrum Sensors in the Internet of Battlefield Things

The battlefield has evolved into a mobile and dynamic scenario where soldiers and heterogeneous military equipment exchange information in real-time and wirelessly. This fact brings to reality the Internet of Battlefield Things (IoBT). Wireless communications are key enablers for the IoBT, and their management is critical due to the spectrum scarcity and the increasing number of IoBT devices. In this sense, IoBT spectrum sensors are deployed on the battlefield to monitor the frequency spectrum, transmit over unoccupied bands, intercept enemy transmissions, or decode valuable information. However, IoBT spectrum sensors are vulnerable to heterogeneous cyber-attacks, and their accurate detection is an open challenge in the literature. Thus, this paper presents SpecForce, a security framework for IoBT spectrum sensors based on device behavioral fingerprinting and ML/DL techniques. SpecForce considers heterogeneous data sources to detect the most dangerous and recent cyber-attacks affecting IoBT spectrum sensors, such as impersonation, malware, and spectrum sensing data falsification attacks. To evaluate the SpecForce detection performance, it has been deployed on 25 real spectrum sensors, and results show almost perfect detection for the three cyber-attack families previously mentioned.

Cognitive radio: SSDF attack and security

Cooperative spectrum sensing can improve the performance of system detection, but when there are some malicious users in sensors, they will launch spectrum sensing data falsification attack, this is to say they send false sensing result, which will have a great influence on the final decision of fusion center and the primary user. Given that, this paper proposes a basic cooperative spectrum sensing algorithm based on reputation to defend malicious users and then improve that algorithm, advance a new algorithm-reputation weighted cooperative spectrum sensing algorithm.Verified by simulation, our algorithm has achieved the expected effect. The first algorithm can effectively resist attacks especially when the attack probability of malicious users is high. When malicious users are more intelligent, their attack probabilities are different from each other and are uncertain. At this time, the second algorithm can better improve the performance of the final decision of fusion center.

Security and Energy Management in Cooperative Spectrum Sensing

The revolutionary concept of cognitive radio (CR) has been developed to address the issue of spectrum scarcity in wireless networks. Cooperative spectrum sensing is seen as a promising strategy to dramatically increase spectrum sensing performance in cognitive radio networks, but it is vulnerable to Spectrum sensing data falsification (SSDF) attacks. Existing security references have concentrated on ways to lessen the impact of a SSDF attacking, but these approaches have relied heavily on assumptions such as the attackers being a numerical minority and there being a trustworthy node for data fusion. This realisation inspires us to conduct a thorough examination of the SSDF attack and fusion centre (FC) techniques outside of these constraints. In particular, analysing complex harmful actions allows us to think about a generic SSDF attack model that is more general than the existing methods. According to this overview of attacks, the resultant condition renders the FC clear to attack. Based on these considerations, the ideal attack approach to maximise Bayes risk is examined for both the unknown and known fusion rule scenarios. Second, by casting it as a combinatorial optimisation issue, energy efficiency of cooperative sensing is discussed. In order to enhance the overall energy efficiency of system, a deep learning framework is built based on this formulation by combining graph neural network with reinforcement learning. The efficacy of this method has been demonstrated through simulation tests at varying network scales.

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

An Online Learning Approach for Cooperator Selection in CSS Under SSDF Attack

Cooperative spectrum sensing (CSS) can greatly improve sensing accuracy by combining the individual sensing results. However, it also faces spectrum sensing data falsification (SSDF) attack which degrades the cooperative sensing performance in practice. To deal with SSDF attack and reduce total energy consumption, it is recommended to select reliable secondary users (SUs) and SUs which have the best detection performance for cooperation. In this letter, an online learning based user selection algorithm is proposed according to the approximate ground truth about the licensed channel state, which addresses the practical problem that different users have different sensing capability and may launch SSDF attacks. In addition, the cooperator’s trust degree is updated in the online learning process. Simulation results indicate that the proposed SUs selection algorithm can quickly converge to a stable state in terms of collision probability and spectrum waste probability under highly unreliable conditions.

Deep Reinforcement Learning based reliable spectrum sensing under SSDF attacks in Cognitive Radio networks

In Spectrum Sensing Data Falsification (SSDF) attacks, malicious Secondary Users (SUs) actively send erroneous local sensing results to the Fusion Centre (FC) that influence the actual outcomes of Cooperative Spectrum Sensing (CSS). Existing trust value-based algorithms are partially successful as SUs can quickly change their characteristics from honest to malicious and vice versa. The present work explores the Deep Reinforcement Learning algorithm (DRL) for adapting the behavioural changes of SUs during CSS to overcome the issue mentioned earlier. The agent in the DRL algorithm wisely avoids the malicious data from the received sensed-energy values at FC and reduces the sensing error effectively. Simulation results show that the proposed work outperforms the conventional and Support Vector Machine-based approaches in CSS reliabilities under SSDF attacks.

Comprehensive Performance Analysis of Soft Data Fusion Schemes under SSDF Attacks in Cognitive Radio Networks

Background: Trust and security are the biggest challenges facing the Cooperative Spectrum Sensing (CSS) process in Cognitive Radio Networks (CRNs). The Spectrum Sensing Data Falsification (SSDF) attack is considered the biggest threat menacing CSS. Methods: This paper investigates the performance of different soft data combining rules such as Maximal Ratio Combining (MRC), Square Law Selection (SLS), Square Law Combining (SLC), and Selection Combining (SC), in the presence of Always Yes and Always No Malicious User (AYMU and ANMU). Results: This comparative study aims to assess the impact of such malicious users on the reliability of various soft data fusion schemes in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are performed to show that the soft data fusion scheme using MRC is the best in the field of soft data computing. Conclusion: ANMU has a slight impact on CSS. Yet, AYMU affects the cooperative detection performance.

Research on spectrum sensing data falsification attack detection algorithm in cognitive Internet of Things

Research on spectrum sensing data falsification attack detection algorithm in cognitive Internet of Things

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.

Mitigation of spectrum sensing data falsification attack using multilayer perception in cognitive radio networks

&lt;p&gt;Cognitive radio network (CRN) is used to solve spectrum scarcity and low spectrum utilization problems in wireless communication systems. Spectrum sensing is a vital process in CRNs, which needs continuous measurement of energy. It enables the sensors to sense the primary signal. Cooperative Spectrum Sensing (CSS) has recommended to sense spectrum accurately and to enhance detection performance. However, Spectrum Sensing Data Falsification (SSDF) attack being launched by malicious users can lead to wrong global decision on the availability of spectrum. It is an extremely challenging task to alleviate impact of SSDF attack. Over the years, numerous strategies have been proposed to mitigate SSDF attack ranging from statistical to machine learning models. Energy measurement through statistical models is based on some predefined criteria. On the other hand, machine learning models have low sensing performance. Therefore, it is necessary to develop an efficient method to mitigate the negative impact of SSDF attack. This paper intends to propose a Multilayer Perceptron (MLP) classifier to identify falsified data in CSS to prevent SSDF attack. The statistical features of the received signals are measured and taken as feature vectors to be trained by MLP. In this manner, measurement of these statistical features using MLP becomes a key task in cognitive radio networks. Trained network is employed to differentiate malicious users signal from honest users’ signal. The network is trained with the Levenberg-Marquart algorithm and then employed for eliminating the effect of attacks due to the SSDF process. Once the simulated results are observed, it can be revealed that the proposed model could efficiently reduce the impact of malicious users in CRN.&lt;/p&gt;

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.

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

The exponential growth of Internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

Smart contract-based secure cooperative spectrum sensing algorithm

Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users’ transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU’s utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack.