Congressos amb més articles Campus del Baix Llobregat (CBL) - 2011

Es presenta la llista de congressos en les que més han participat els autors del CBL ordenat per centres. Si el centre no apareix vol dir que els congressos en el que han participat els seus autors no están buidats a l’SCOPUS.Preprin

Exploiting Spectrum Sensing Data for Key Management

In cognitive radio networks, secondary users (SUs) communicate on unused spectrum slots in the frequency bands assigned to primary users (PUs). Like any other wireless communication system, cognitive radio networks are ex- posed to physical layer attacks. In particular, we focus on two common at- tacks, namely, spectrum sensing data falsification and eavesdropping. Such attacks can be counteracted by using symmetric key algorithms, which how- ever require a complex key management scheme. In this paper we propose a novel algorithm that significantly reduces the complexity of the management of symmetric link keys by leveraging spectrum sensing data that is available to all nodes. In our algorithm, it is assumed that a primary secret key is pre-distributed to the legitimate SUs, which is needed every number of de- tection cycles. With the aid of the information provided in the primary key, our algorithm manipulates the collected samples so that a segment of the estimated sensing statistic at the two legitimate SUs can be used as a seed to generate a common symmetric link key. The link key is then employed to encrypt the transmitted data. Our algorithm exhibits very good performance in terms of bit mismatch rate (BMR) between two link keys generated at the two legitimate SUs. In addition, our solution is robust against the difference in the received signal to noise ratio between two legitimate SUs thus making it suitable for practical scenarios. Furthermore, our algorithm exploits the decision statistic that SUs use for spectrum sensing, hence, it does require neither extra processing nor extra time, allowing the SUs to quickly and securely tab into empty spectrum slots

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Spectrum sensing, spectrum monitoring, and security in cognitive radios

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. In this dissertation, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user’s signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. In-band spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). We investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). Next we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving radios. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. We also consider the problem of centralized binary hypothesis testing in a cognitive radio network (CRN) consisting of multiple classes of cognitive radios, where the cognitive radios are classified according to the probability density function (PDF) of their received data (at the FC) under each hypotheses