Block Outlier Methods for Malicious User Detection in Cooperative Spectrum Sensing

Block outlier detection methods, based on Tietjen-Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.Comment: Accepted in Proceedings of 79th IEEE Vehicular Technology Conference-Spring (VTC-Spring), May 2014, Seoul, South Kore

Improved likelihood ratio statistic-based cooperative spectrum sensing for cognitive radio

Cooperative spectrum sensing (CSS) is a technique where multiple cognitive radio users cooperate among themselves to make binary decisions about the presence of a primary user. The single cognitive user often faces the hidden terminal problem. However, CSS tackles this problem by sending local sensing-based decisions to the fusion centre. A major drawback of conventional energy detection is the poor performance at low SNR regime. In this work, likelihood ratio statistics is considered as a test-statistic due to its highest statistical power. An improved likelihood ratio statistic-based CSS scheme is proposed by considering several past sensing events. The proposed scheme mitigates the poor detection at low SNR regime and misdetections arising due to sudden drops in signal energy. Furthermore, the generalised Byzantine attack is taken into account considering a security aspect. The proposed scheme is also shown to outperform Anderson Darling-based malicious user detection in CSS at a low SNR regime. The proposed scheme is verified and validated over empirical spectrum data. The performance improvement is at the cost of computational time, which in practice is very low and is justified by the significant performance improvements of the proposed scheme at low SNR regime

A Context-aware Trust Framework for Resilient Distributed Cooperative Spectrum Sensing in Dynamic Settings

Cognitive radios enable dynamic spectrum access where secondary users (SUs) are allowed to operate on the licensed spectrum bands on an opportunistic noninterference basis. Cooperation among the SUs for spectrum sensing is essential for environments with deep shadows. In this paper, we study the adverse effect of insistent spectrum sensing data falsification (ISSDF) attack on iterative distributed cooperative spectrum sensing. We show that the existing trust management schemes are not adequate in mitigating ISSDF attacks in dynamic settings where the primary user (PU) of the band frequently transitions between active and inactive states. We propose a novel context-aware distributed trust framework for cooperative spectrum sensing in mobile cognitive radio ad hoc networks (CRAHN) that effectively alleviates different types of ISSDF attacks (Always-Yes, Always-No, and fabricating) in dynamic scenarios. In the proposed framework, the SU nodes evaluate the trustworthiness of one another based on the two possible contexts in which they make observations from each other: PU absent context and PU present context. We evaluate the proposed context-aware scheme and compare it against the existing context-oblivious trust schemes using theoretical analysis and extensive simulations of realistic scenarios of mobile CRAHNs operating in TV white space. We show that in the presence of a large set of attackers (as high as 60% of the network), the proposed context-aware trust scheme successfully mitigates the attacks and satisfy the false alarm and missed-detection rates of $10^{-2}$ and lower. Moreover, we show that the proposed scheme is scalable in terms of attack severity, SU network density, and the distance of the SU network to the PU transmitter

Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques

Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks

Contributions to the security of cognitive radio networks

The increasing emergence of wireless applications along with the static spectrum allocation followed by regulatory bodies has led to a high inefficiency in spectrum usage, and the lack of spectrum for new services. In this context, Cognitive Radio (CR) technology has been proposed as a possible solution to reuse the spectrum being underutilized by licensed services. CRs are intelligent devices capable of sensing the medium and identifying those portions of the spectrum being unused. Based on their current perception of the environment and on that learned from past experiences, they can optimally tune themselves with regard to parameters such as frequency, coding and modulation, among others. Due to such properties, Cognitive Radio Networks (CRNs) can act as secondary users of the spectrum left unused by their legal owners or primary users, under the requirement of not interfering primary communications. The successful deployment of these networks relies on the proper design of mechanisms in order to efficiently detect spectrum holes, adapt to changing environment conditions and manage the available spectrum. Furthermore, the need for addressing security issues is evidenced by two facts. First, as for any other type of wireless network, the air is used as communications medium and can easily be accessed by attackers. On the other hand, the particular attributes of CRNs offer new opportunities to malicious users, ranging from providing wrong information on the radio environment to disrupting the cognitive mechanisms, which could severely undermine the operation of these networks. In this Ph.D thesis we have approached the challenge of securing Cognitive Radio Networks. Because CR technology is still evolving, to achieve this goal involves not only providing countermeasures for existing attacks but also to identify new potential threats and evaluate their impact on CRNs performance. The main contributions of this thesis can be summarized as follows. First, a critical study on the State of the Art in this area is presented. A qualitative analysis of those threats to CRNs already identified in the literature is provided, and the efficacy of existing countermeasures is discussed. Based on this work, a set of guidelines are designed in order to design a detection system for the main threats to CRNs. Besides, a high level description of the components of this system is provided, being it the second contribution of this thesis. The third contribution is the proposal of a new cross-layer attack to the Transmission Control Protocol (TCP) in CRNs. An analytical model of the impact of this attack on the throughput of TCP connections is derived, and a set of countermeasures in order to detect and mitigate the effect of such attack are proposed. One of the main threats to CRNs is the Primary User Emulation (PUE) attack. This attack prevents CRNs from using available portions of the spectrum and can even lead to a Denial of Service (DoS). In the fourth contribution of this the method is proposed in order to deal with such attack. The method relies on a set of time measures provided by the members of the network and allows estimating the position of an emitter. This estimation is then used to determine the legitimacy of a given transmission and detect PUE attacks. Cooperative methods are prone to be disrupted by malicious nodes reporting false data. This problem is addressed, in the context of cooperative location, in the fifth and last contribution of this thesis. A method based on Least Median Squares (LMS) fitting is proposed in order to detect forged measures and make the location process robust to them. The efficiency and accuracy of the proposed methodologies are demonstrated by means of simulation

Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques

Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks

Wideband cyclostationary spectrum sensing and characterization for cognitive radios

Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques

Trustworthiness Management in the Internet of Things

The future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. Object cooperation allows us to develop complex applications in which each node contributes one or more services. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search for services and the management of objects’ trustworthiness. In that scenario, in which the information moves from a provider to a requester node in a peer-to-peer network, Trust Management Systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The cornerstone of any TMS is the ability to generate a coherent evaluation of the information received. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, strong assumptions still need to be considered. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. In this regard, this thesis proposes an exhaustive analysis of the trustworthiness management in the IoT and SIoT. To this, in the beginning, we generate a dataset and propose a query generation model, essential to develop a first trust management model to overcome all the attacks in the literature. So, we implement several trust mechanisms and identify the importance of the overlooked assumptions in different scenarios. Then, to solve the issues, we concentrate on the generation of feedback and propose different metrics to evaluate it based on the presence or not of errors. Finally, we focus on modelling the interaction between the two figures involved in interactions, i.e. the trustor and the trustee, and on proposing guidelines to efficiently design trust management models

A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.http://link.springer.com/journal/5002020-11-03hj2020Electrical, Electronic and Computer Engineerin