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

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm

Cognitive Radio Networks: Realistic or Not?

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important interactions of the research with commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. This is necessary to detect the existence of spectrum holes without which no other procedure can be fulfilled. However, simply sensing (cooperatively or not) the energy received from a primary transmitter cannot enable correct dynamic spectrum access. For example, the low strength of a primary transmitter's signal does not assure that there will be no interference to a nearby primary receiver. In addition, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing elegant and clever solutions to the DSA problem no robust, implementable scheme has emerged. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments.Comment: Work in progres

Reinforcement learning-based trust and reputation model for spectrum leasing in cognitive radio networks

Cognitive Radio (CR), which is the next generation wireless communication system, enables unlicensed users or Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users(PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed using Trust and Reputation Management (TRM). Generally speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field

Threats Advancement in Primary User Emulation Attack and Spectrum Sensing Data Falsification (SSDF) Attack in Cognitive Radio Network (CRN) for 5G Wireless Network Environment

Primary User Emulation (PUE) attack and Spectrum Sensing Data Falsification (SSDF) attack on Data Fusion Centre and attack on Common Control Channel (CCC) is a serious security problems and need to be addressed in cognitive radio network environment. We are reviewing the recent advances of threats for the future 5th Generation (5G) wireless radio network from these attacks. Several existing security schemes have been proposed and discussed to overcome these attacks. We propose new security scheme that able to mitigate the attacks and provide security solutions. This scheme intended to mitigate the threats from the attacks in CRN and improve the future 5G network security

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

When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies usually assume network or attackers as passive entities, e.g., assuming the prior knowledge of attacks is known or fixed. In practice, attackers can actively adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by defense strategies. In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. We propose a generic algorithm to create malicious sensing data using this surrogate model. Our real-world experiments show that the LEB attack is effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the proposed LEB attack and existing defenses, we introduce a non-invasive method named as influence-limiting defense, which can coexist with existing defenses to defend against LEB attack or other similar attacks. We show that this defense is highly effective and reduces the overall disruption ratio of LEB attack by up to 80%

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature