Identification and Punishment Policies for Spectrum Sensing Data Falsification Attackers Using Delivery-Based Assessment

Cooperative spectrum sensing (CSS) has been considered as a powerful approach to improve the utilization of scarce radio spectrum resources. However, spectrum sensing data falsification (SSDF) can hugely degrade the achievable detection accuracy of the primary user (PU) channel status in cognitive radio (CR) networks and thus requires learning from past CR network environments. Using the supervised learning (SL) method, a large number of labels about the real state of the channel availability need to be acquired. In a practical setting, however, obtaining off-the-shelf label data is a challenging undertaking because it necessitates cooperation among PUs and secondary users (SUs) and increases communication overhead in CR networks. Motivated by this, we solve the issue of label data scarcity in CR networks in this paper, and propose a semi-supervised learning-enabled two-stage framework for CSS against SSDF attack, in which combines the superior performance of semi-supervised support vector machine (S3VM) and the fast convergence of K-means. At last, simulation results demonstrate that S3VM is equipped with a greater detection performance than support vector data description (SVDD), and the same detection performance as support vector machine (SVM), especially it also the robustness and excellent performance using a small amount of labeled data.

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.

In cognitive radio networks, cooperative spectrum sensing (CSS) is generally adopted for improving spectrum sensing accuracy to increase spectrum utilization and avoid interference with the primary users. However, some malicious secondary users (SUs) may affect the CSS performance by inducing false observation bits for fusion. The message authentication code (MAC) is a promising technique to avoid the damage from the spectrum sensing data falsification (SSDF) attacks. In this paper, as both the more spectrum sensing nodes and the MAC reporting bits result in extra energy consumption, we propose an energy efficiency model to capture the effects of the length of MAC and the number of cooperative SUs under independent and collaborative SSDF attacks, respectively, and analyze the existence of the optimal length of MAC and the optimal number of cooperative SUs that can achieve the maximum value of energy efficiency, respectively. Simulation results are provided to show that the CSS scheme based on MAC can resist SSDF attacks and the accuracy of the theoretical analysis is also validated.

In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

In cooperative spectrum sensing, malicious user (MU) sends tampered sensing result in spectrum sensing to pose threats to cognitive radio networks, which is called spectrum sensing data falsification (SSDF) attack. Many existing MU detection schemes are designed for the narrow-band environment. However, Research on MU detection schemes for compressive sensing (CS), which has some huge advantages in sensing wide-band spectrum, is just beginning. Because of the high complex of reconstruction algorithms, it's necessary to identify potential MU and then exclude the MU before reconstructing the original signal in CS. In this paper, we consider attack and defense behavior in CS and propose a density-based MU detection (DBMUD) with a trusted user to distinguish MU precisely. DBMUD maintains reliability even if MUs are a majority. Simulation results demonstrate that DBMUD performs well in fighting against SSDF attack.

Cooperative spectrum sensing is envisaged to increase reliability in cognitive radio networks. Several users cooperate to detect the availability of a wireless channel. In this framework, some malicious users may affect the decisions taken by the cognitive users by providing false information, introducing spectrum sensing data falsification (SSDF). In this paper, the effect of multiple malicious users on the energy efficiency of a cognitive radio network is given. A low-overhead security protocol is proposed to address SSDF attacks under a trade-off between energy efficiency and security. An analytical study is provided to set the optimal number of security bits required to maximize energy efficiency. Simulation results show a considerable improvement on the achievable energy efficiency, and the optimal number of bits explicitly depends on the selected fusion rule, the number of malicious users and the number of legitimate users.

By enpowering the capability of CR, sensor nodes in CRSNs can sense the availability of licensed channels and access the idle channels for data transmission. However, due to channel fading and shadowing, spectrum sensing by individual sensor node has inevitable sensing errors, which adversely impact the performance of both the CRSN and the primary users (PUs) of the licensed channels. To overcome the limitation of individual spectrum sensing, collaborative spectrum sensing is generally employed in CRSNs to improve the spectrum sensing accuracy [1, 2]. However, since sensor nodes may be compromised by adversaries, these nodes can send false sensing results, making CRSNs vulnerable to spectrum sensing data falsification (SSDF) attack [3–5]. In SSDF attacks, compromised nodes may operate independently or collaboratively to mislead the channel availability decision, which can significantly reduce spectrum utilization and degrade overall network performance [6]. To resist SSDF attacks, a number of countermeasures, including trust/reputation based approaches [7–10], abnormal statistical-behavior detection based approaches [11–15] and clustering based approaches [16, 17], have been proposed in cognitive radio networks (CRNs), which could be applied into CRSNs. But few of the related works consider energy efficiency in SSDF attack countermeasure design [18, 19].

Malicious users can exploit vulnerabilities in cognitive radio networks (CRNs) and cause heavy performance degradation by denial of service (DoS) attacks. During operation, cognitive radios (CRs) spend a considerable amount of time to identify idle (free) channels for transmission. In addition, CRs also need additional security mechanisms to prevent malicious attacks. Proactive model predictive control (MPC)-based medium access control (MAC) protocols for CRs can quicken the idle channel identification by predicting future states of channels in advance. This provides enough time for CRs to carry out other calculations like DoS attack detection. However, such external detection techniques use additional power that makes them inappropriate for energy constrained applications. As a solution, this paper proposes a proactive learning-based MAC protocol (PROLEMus) that shows immunity to two prominent CR-based DoS attacks, namely, primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) attack, without any external detection mechanism. PROLEMus shows an average of 6.2%, 8.9%, and 12.4% improvement in channel utilization, backoff rate, and sensing delay, respectively, with low prediction errors (≤1.8%) saving 19.65% energy, when compared to recently proposed MAC protocols like ProMAC aided with additional DoS attack detection mechanism.

The Cognitive Radio (CR) is a fully-reconfigurable wireless device that can intelligently sense, manage, and exploit temporarily-vacant licensed spectrum bands during the absence of incumbent users. Broadly, the IEEE 802.22 is the first complete Wireless Regional Area Network (WRAN) standard that utilizes CR technology in the opportunistic access of white spaces in the television (TV) bands. Intrinsically, the increased CR networks' vulnerabilities alongside the experienced growth of attackers' capabilities, creates a strain on CR network designers to act accordingly. However, most of the existing efforts solely examined the issues of Denial-of-Service (DoS) attacks of IEEE 802.22 networks, such as Primary User Emulation (PUE) attack and Spectrum Sensing Data Falsification (SSDF) attack, each treated in isolation. One main challenge in CR network security domain is to efficiently represent possible simultaneous multiple security threats, and assess their effects. Unlike the previous works, this paper addresses the aforementioned challenge through using the holistic approach of assessing the combined effect of simultaneous multiple DoS attacks. The Bayesian Attack Graph (BAG) model is utilized in this paper to capture the probabilistic dependencies among IEEE 802.22 DoS threat-environment and known vulnerabilities. The simulation results indicate up to 51.3% increase in the probability of DoS in IEEE 802.22 networks considering simultaneous multiple attacks in comparison to the most severe sole attack. Finally, the paper introduces the BAG model as a feasible CR vulnerability metric that can facilitate the creation of a security tightening plan.

We study security issues that may pose a serious threat to the spectrum sensing for wireless cognitive radio networks. In particular, we focus on two of the main security threats: Incumbent Emulation (IE) attacks and Spectrum Sensing Data Falsification (SSDF) attacks. We develop a Distributed Spectrum Sensing (DSS) algorithm to defend against each of the two attacks in a two-tier cognitive radio network. For the IE attacks, we develop a user verification scheme, called localization-based defense. For the SSDF attacks, we propose an optimal nonlinear cooperation scheme, which minimizes the interference to the primary emulation attack. To take the more realistic scenarios into account, we characterize the DSS algorithm as an M-ary Hypotheses problem, and formulate it as an optimization problem by introducing a deflection coefficient. The simulation results evaluate our proposed optimal nonlinear cooperation scheme and validate the significant cooperative gain achieved.

Abstract5G and 6G promise to be catalysts for the Internet of Things (IoT), enabling ultra‐massive and mission‐critical IoT. The demands of new IoT applications and the large number of connected objects being announced will exacerbate the thorny issue of spectrum scarcity in wireless communications. Cognitive radio (CR) is a revolutionary technology that promises to mitigate the problem of spectrum scarcity through more efficient use of spectrum. Integrating CR into the IoT allows objects to opportunistically access spectrum resources already allocated to a Primary User (PU) without causing interference. Spectrum sensing (SS) allows objects to be aware of the PU's spectrum occupancy status. In radio environments where the PU signal is subject to multipath and shadowing effects that degrade the channel between the PU and objects, SS performed by a single object could be inaccurate and lead to incorrect decisions about the PU's status. Cooperative spectrum sensing (CSS) has been proposed to improve detection performance. However, this cooperation between objects has opened the way to a new form of attack known as the Spectrum Sensing Data Falsification (SSDF) or Byzantine attack. In a SSDF attack, attackers falsify their local sensing results before sharing them in the CSS. This attack is very harmful to the CSS and can lead to a loss of spectrum opportunities or interference with the PU. In this paper, from the attack point of view, a sophisticated Byzantine attack model that generalizes well the simple attack strategies has been proposed and allows an attacker to implement other attack strategies. From a defense point of view, a new and innovative Weighted Sequential Hypothesis Testing (WSPRT) scheme has been suggested. This ensures the security of the CSS while significantly reducing the average number of samples required for overall decision‐making in a very hostile IoT network. The results obtained from simulations carried out in various attacks scenarios show that the proposed secure CSS scheme requires at most six samples to detect the state of the PU without error when the proportion of attackers reaches 80%. This performance far exceeds that of other defense mechanisms such as classic WSPRT, SPRT, and majority rule, with which we have compared it under the same conditions. In general, for the classic WSPRT, SPRT, and majority rule mechanisms, the error rate starts to deteriorate at ratio 40% of attackers and the number of samples is greater than six and reaches 50.

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.

SummaryIn a cognitive radio ad hoc network, there is no central authority. Hence, distributed collaborative spectrum sensing (CSS) plays a major role in achieving an accurate spectrum sensing result. However, CSS is sensitive to spectrum sensing data falsification (SSDF) attack, in which a malicious user falsifies its local sensing report before disseminating it into the network. To capture such abnormal behavior of a node, we present an approach for detecting SSDF attack based on dissimilarity score. A secondary user (SU) computes the dissimilarity score of its neighbors from the messages received from its h‐hop neighbors. Further, we also present how the proposed scheme can be used on the sequence of sensing reports to detect and isolate the malicious SUs on the fly.

Cognitive radios are essential concept to transfer from legacy spectrum system to dynamic one. Cognitive radios are brilliant technology, capable to sense the radio environment and reconfigure the system parameters in dynamic network. Cognitive radio network includes multiple features of ad hoc network. These features includes distributed system with multihop framework, opportunistic spectrum system, alternative network topology. Cognitive radio features required to find accurate spectrum portion to successfully deploy cognitive radio ad hoc network. Several users of cognitive network in spectrum sensing cooperative environment detects the active or passive status of primary users. This technique helps to enhance the detection performance of users. Ad hoc network have no central system for data fusion it is crucial to distribute the spectrum sensing network fully cooperative in nature. This study is focus to create cooperative environment based spectrum sensing network for cognitive radio ad hoc networks. The analysis is depends on viewing of reliable sensing and less energy consumption. The contribution of this research is to design context aware framework for distributed system and mobile cognitive radio ad hoc network. This study contribute to avoid Insistent Spectrum Sensing Data Falsification (ISSDF) attacks.

Cooperative spectrum sensing for vacant bands is one of the key techniques in cognitive radio networks. However, the existence of malicious secondary users could severely degrade the performance of cooperative spectrum sensing. Currently, sequential probability ratio test scheme (SPRT) is considered as a powerful approach to improve the sensing decision for cooperative spectrum sensing. Considering the binary sensing behaviors of secondary users (SUs), a novel trusted sequential probability ratio test scheme based on beta function (BSPRT) is proposed in this paper to avoid the intermittent spectrum sensing data falsification (ISSDF) attack. By evaluating the trust value of SUs dynamically, the additive punishment and multiplicative decay factors are introduced to mitigate the harmful effect of malicious SUs in the process of the soft-decision data fusion, and thus improving the accuracy of the final sensing decision. Simulation results show that the novel scheme outperforms the conventional SPRT schemes. The BSPRT scheme can work effectively in the scenario where a half of SUs are malicious.