Data Falsification Attack Research Articles

Detecting Byzantine Attack in Cognitive Radio Networks by Exploiting Frequency and Ordering Properties

Collaborative spectrum sensing in cognitive radio network (CRN) helps in increasing the accuracy of detection of the incumbent signal. Each node in an infrastructure-based CRN sends its local sensing report to the fusion center (FC), which aggregates the reports to arrive at a final sensing decision. However, collaborative sensing is vulnerable to the Byzantine attack (also known as spectrum sensing data falsification attack) in which malicious nodes falsify sensing reports in order to adversely affect the final sensing decision. Temporal data collected at the FC in the form of the sensing reports form a rich audit data set, which can be analyzed to identify malicious nodes, if any. In this paper, we present two anomaly detection techniques based on such an analysis. First, we exploit the frequency property of such statistical data and develop a lightweight intrusion detection scheme. Second, we use the Markov chain model which is based on the ordering property of the data to develop another intrusion detection scheme. Simulation results along with theoretical analysis show the validity of the two schemes.

Mitigating spectrum sensing data falsification attack in ad hoc cognitive radio networks

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.

Social learning for resilient data fusion against data falsification attacks

BackgroundInternet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers.MethodsTo tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network.ResultsWe analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary.ConclusionsOur results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.

Securing Cooperative Spectrum Sensing Against Collusive False Feedback Attack in Cognitive Radio Networks

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, CSS assumes that all secondary users (SU) always are honest, thus offering opportunities for attackers to launch spectrum sensing data falsification (SSDF) attack. To combat such threat, recent efforts have been made to trust mechanism. In this paper, we argue that powering CSS with trust mechanism is not enough. A vulnerability for trust mechanism is found, that is, the feedback data from initiator SUs are generally unchecked. Such feedback data corresponding to the spectrum status of primary users can be exploited by attackers to disturb trust mechanism in a collusive manner. Especially, collusive false feedback (CFF) attackers would form a clique to help each other. One of CFF attackers can disguise as an initiator SU who sends the feedback in accordance with the sensing data of their conspirators who play the role of cooperating SUs, resulting in promoting their conspirators' sensing trust quickly. With high sensing trust, attackers can avoid the detection of trust mechanism and, hence, launch SSDF attack (fake sensing data) more easily. We conduct an in-depth investigation on CFF attack and propose a two-level defense scheme called FeedGuard from the design ideas of feedback trust and I-C frequency correlation analysis. Simulation results show that the FeedGuard scheme can enhance the accuracy of sensing trust evaluation, thus successfully reducing the power of CFF attack against CSS.

Attack-Aware Defense Strategy: A Robust Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks

Cognitive radio (CR) is a key enabling technology of dynamic spectrum access to achieve better utilization of frequency spectrum. Cooperative spectrum sensing (CSS) exploits the spatial diversity between CR users to improve the accuracy of spectrum sensing. But, in practice, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local sensing results to a fusion center and disrupt the global decision-making process. In this study, we present a smart and robust defense scheme called attack-aware CSS (ACSS). The idea is based on the estimation of attack strength where the attack strength corresponds to the attack population. The attack strength is defined as the ratio of the number of malicious sensors to the total number of sensors which is equal to the probability that a specific sensor is malicious. Assuming the malicious sensor behavior or attack strategy, the proposed approach estimates attack strength and uses the Bayesian hypothesis test to improve the collaborative sensing performance. Computer simulation results illustrate the effectiveness of the proposed ACSS approach against SSDF attacks, especially for CR networks located in hostile environments.

Trust-based multi-hop cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing is one of the solutions for cognitive radio networks, which can resolve the uncertainty of stand-alone spectrum sensing. It means that each secondary user senses defined spectrum bands for the unused spectrum detection and shares its sensing results with the others to improve the accuracy of spectrum sensing. Due to the presence of malicious secondary users and their fake sensing reports, various forms of attacks will be encountered that reduce the performance of cooperative spectrum sensing. Spectrum sensing data falsification attack (SSDF) is one of the attacks that some research works have been presented to defend against it, based on trust and reputation management (TRM). Previous works assume that all the secondary users are in the transmission range of each other and one-hop sensing reports are provided. However, multi-hop dissemination of sensing reports is a necessary scenario for the secondary users with limited energy sources and its security attacks are very challenging to be encountered. In this paper, a trust-based multi-hop cooperative spectrum sensing method is proposed to deal with SSDF attack. Simulation results show that this scheme improves spectrum sensing accuracy and reduces.

Lightweight cloned‐node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other's network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.

Secure Non-Consensus Based Spectrum Sensing in Non-Centralized Cognitive Radio Networks

In a non-centralized cognitive radio network, secondary users (SUs) cooperate in order to determine the presence of the primary user (PU) signal. This can be accomplished by each node sharing its spectrum sensing result with its neighboring SUs. In this paper, a non-consensus based distributed spectrum sensing (NCSS) scheme is presented, in which a node uses the raw energy level of the PU signal from its $h$ -hop neighbors for determining the status of the PU. A message passing scheme is used rather than the conventional consensus scheme. A comparison with an existing consensus algorithm is also shown. Further, two types of spectrum sensing data falsification attacks are considered in which the attackers inject very high or very low energy values. To catch such abnormal behavior of a node, an approach called secure NCSS algorithm is proposed, which isolates a node that generates extreme energy values so that it is removed from future computation. For this, an outlier detection technique is adopted. The approach is based on the idea that a malicious node’s energy level deviates significantly from those of genuine nodes, whereas all genuine nodes share nearby estimated energy levels.

Co-operative Detection for Malicious Nodes in Under-Attack WSN

The network security must be taken into consideration in wireless sensor networks. In our project, we take sensor node data falsification (SNDF) attack using malicious nodes and co-operative detection is used. Fusioncentre collects information from the nodes created in a cluster environment and makes a global decision. The protocol used here is Ad-hoc-on demand distance vector[5] (AODV) and the performance analysis is done using parameters such as throughput and End-to-end delay. The stimulation is done in NS2 using network animator and graphical results are taken.The throughput will be increased compared to the existing system whereas End-to-End delay will be decreased.

Energy-Efficient Defensive Strategy Against Hybrid SSDF/Eavesdropping Attacks Over Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math> </inline-formula> Channels

During cooperative spectrum sensing (CSS) in cognitive radio networks, malicious users (MUs) not only launch spectrum sensing data falsification (SSDF) attack, but also wiretap legitimate users (LUs) during the reporting phase. In this letter, an energy-efficient defensive strategy against hybrid SSDF/eavesdropping attacks is proposed, to achieve both energy-efficiency and physical layer security. Secure CSS reporting/eavesdropping channels are modeled as Nakagami- ${m}$ fading channels. The optimal MUs and LUs are determined via the achievement of maximum energy-efficiency that is subject to the optimal report bits, report distance as well as report error rate. Furthermore, intercept performance of the proposed strategy in the presence of multiple MUs is also investigated. Simulation results reveal that, the proposed strategy is immune to MU numbers, report distance as well as report error rate. Moreover, the intercept probability can be reduced in multiple MUs scenario when the legitimate-to-malicious ratio is high and the channel fading factor ratio is large.

Can Clustering be Used to Detect Intrusion During Spectrum Sensing in Cognitive Radio Networks?

Collaborative sensing helps in achieving a more accurate sensing decision than individual sensing in cognitive radio network (CRN). In an infrastructure-based CRN, each node sends its local sensing report to the fusion center (FC), which uses a fusion rule to aggregate the local sensing reports. However, collaborative sensing is vulnerable to the spectrum sensing data falsification attack, in which a node falsifies its local sensing report before sending it to the FC with the intention of disrupting the final sensing decision of the FC. In practice, the strategy of an attacker is not known. However, the collection of sensing reports at the FC can be useful for data mining with the objective of identifying the attackers. In this paper, we present a method that uses clustering techniques for detection and isolation of such attackers. We employ two clustering techniques, viz., K -medoids clustering and agglomerative hierarchical clustering. Unlike threshold detection that requires some predefined threshold value as input, the proposed approach detects the attackers using only the collection of sensing reports at the FC. We also present how we can use the proposed approach on streaming data (sensing reports), and thus, detect and isolate attackers on the fly. Comparative numerical simulation results support the validity of the approach.

An enhanced cooperative spectrum sensing scheme for anti-SSDF attack based on evidence theory

Cooperative spectrum sensing is widely accepted as an effective method to improve the reliability of spectrum sensing. However, spectrum sensing data falsification attacks (SSDF attacks) have posed serious threats to the credibility of cooperative spectrum sensing. This paper proposes an evidence theory-based anti-SSDF cooperative spectrum sensing scheme that eliminates the influence that malicious users have on the system performance in cooperative spectrum sensing. First, the data fusion center calculates the credibility according to the Jousselme distance between the evidence vectors. Then based on the credibility, the influence of malicious users on the fusion decision is reduced by using the weighted probability assignment for each cognitive user. Furthermore, to reduce the costs of collaboration and the amount of computation in applying evidence theory, the new scheme introduces a projection approximation algorithm at the local spectrum sensing process with weighted averaging in the data fusion phase. The results of the simulation show that the proposed scheme not only improves the system performance when malicious users initiate SSDF attacks, but also reduces the cooperative bandwidth and computational complexity.

Securing Cooperative Spectrum Sensing Against Collusive SSDF Attack using XOR Distance Analysis in Cognitive Radio Networks.

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, if CSS assumes that all secondary users (SU) are honest, it may offer opportunities for attackers to conduct a spectrum sensing data falsification (SSDF) attack. To suppress such a threat, recent efforts have been made to develop trust mechanisms. Currently, some attackers can collude with each other to form a collusive clique, and thus not only increase the power of SSDF attack but also avoid the detection of a trust mechanism. Noting the duality of sensing data, we propose a defense scheme called XDA from the perspective of XOR distance analysis to suppress a collusive SSDF attack. In the XDA scheme, the XOR distance calculation in line with the type of “0” and “1” historical sensing data is used to measure the similarity between any two SUs. Noting that collusive SSDF attackers hold high trust value and the minimum XOR distance, the algorithm to detect collusive SSDF attackers is designed. Meanwhile, the XDA scheme can perfect the trust mechanism to correct collusive SSDF attackers’ trust value. Simulation results show that the XDA scheme can enhance the accuracy of trust evaluation, and thus successfully reduce the power of collusive SSDF attack against CSS.

Throughput of cognitive radio networks with improved energy detector under security threats

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.

Cooperative Estimation of Primary Traffic Under Imperfect Spectrum Sensing and Byzantine Attacks

Cognitive radio (CR) systems exploit the accurate knowledge of primary traffic statistics to improve the CR performance and reduce the harmful interference on primary network. It is essential for cooperative CRs to operate in an efficient secured manner while estimating the primary statistics. In this paper, a new reporting mechanism for cooperative estimation of primary traffic is proposed to increase the spectrum and energy efficiency. This is achieved by the reduction of the reporting channel overhead from cooperative users to the fusion center. Simulation results show that the proposed scheme reduces significantly the signaling overhead, thus making the system more spectrum and energy efficient. Moreover, the openness of cooperative CR makes it susceptible to data falsification attacks, also known as Byzantine attacks. This attack poses a series of damages on the reliability of the estimation of primary traffic. In this paper, we define the types of malicious coordinated attacks on CRs and analyze the possibility of estimating the primary traffic statistics under these attacks. Moreover, we provide a simple yet effective countermeasure based on the proposed reporting for cooperative estimation. Simulation evaluation shows that the proposed algorithm provides an excellent countermeasure for spectrum sensing data falsification attacks.

Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in CWSN

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilisation compared to normal Wireless Sensor Networks (WSNs) by using opportunistic spectrum access to transfer data. They transmit data through the primary user's spectrum band when there is heavy traffic in its own network, thereby eliminating collisions and delays in data delivery. During this, CWSNs are subject to several security threats, attacks on secrecy and authentication, attacks on network availability (DOS attacks), stealth attacks on service integrity etc. The Spectrum Sensing Data Falsification (SSDF) attack is a type of DOS attack where the attackers modify the spectrum sensing report to compel the base station to take a wrong collaborative decision regarding the vacant spectrum band in other networks. In this paper, we have proposed the Maximum-Match Filtering algorithm (MMF) for collaborative spectrum sensing and spectrum decision making in CWSNs, which is executed at the base station to counter the SSDF attack.

MAC Based Energy Efficiency in Cooperative Cognitive Radio Network in the Presence of Malicious Users

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.

Mitigation strategy against SSDF attack for healthcare in cognitive radio networks

Wireless communications have a rapid growth that leads to huge demand on the deployment of new wireless services in both licensed and unlicensed frequency spectrum. The performance of Cooperative Spectrum Sensing (CSS) in cognitive radio is based on two factors: cooperative gain and cooperative overhead. There are many other dominating factors that affect the performance of CSS like the presence of attacks, energy efficiency, sensing time and delay, spectrum efficiency, etc. The major threat to CSS is Spectrum Sensing Data Falsification (SSDF) attack. Existing methods to mitigate the SSDF attack doesn't focus on energy efficiency which is performed here. To deal with these challenges, we propose an energy efficient mechanism against SSDF attack for healthcare application in Cognitive Radio Networks (CRNs). We propose a Malicious Node Identification Algorithm (MIA) to mitigate the problem of SSDF attack in the network and to eliminate the redundant data at the fusion centre, Balanced Cluster-based Redundancy Check Aggregation (BCRCA) mechanism is proposed. Finally, the decision taken by the fusion centre is based on Weighted Selection Combining (WSC) technique that improves spectrum decision accuracy.

Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in CWSN

Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in CWSN

Honest Auction Based Spectrum Assignment and Exploiting Spectrum Sensing Data Falsification Attack Using Stochastic Game Theory in Wireless Cognitive Radio Network

Cognitive radio networks being a widespread disruptive technology where idle spectrum are being analysed and made available to the unlicensed secondary users (SU) from licensed primary users (PU). During this battle of allocation of spectrum to the SU, the attackers can masquerade the network and disrupt the channels. The vulnerability attacks are more often happened based on the nature of usage of the spectrum in PU and they collect those information from secondary agent cluster, one among them is the Spectrum Sensing data falsification attack. In heterogeneous network, the vulnerabilities affect the utility of the channel and changes the information in fusion centre, where the sensing reports are might be corrupted during transmission. The paper focus on assigning the idle spectrum to be allocated based on binary stage honest based winner determination using greedy approach and the broadcasted information is secured using two phase cryptography. Besides, a rational player approach has been proposed to make the network to be susceptible to the idle environment using stochastic game modeling to prevent the attack, where a optimal solution is being estimated with nash equilibrium to identify the best response for the user to exploit the attack. The proposed mechanism is much more significantly improved duly compared with traditional mechanism and it highly improves the network performance in monitoring the induced attack and allocating the spectrum with error free transmission.