Shameek Bhattacharjee

Review: Vulnerabilities in cognitive radio networks: A survey

Cognitive radio networks are envisioned to drive the next generation wireless networks that can dynamically optimize spectrum use. However, the deployment of such networks is hindered by the vulnerabilities that these networks are exposed to. Securing communications while exploiting the flexibilities offered by cognitive radios still remains a daunting challenge. In this survey, we put forward the security concerns and the vulnerabilities that threaten to plague the deployment of cognitive radio networks. We classify various types of vulnerabilities and provide an overview of the research challenges. We also discuss the various techniques that have been devised and analyze the research developments accomplished in this area. Finally, we discuss the open research challenges that must be addressed if cognitive radio networks were to become a commercially viable technology.

Spectrum Map and Its Application in Resource Management in Cognitive Radio Networks

Measurements on radio spectrum usage have revealed an abundance of under-utilized bands of spectrum that belong to primary (licensed) networks. Prior knowledge about the occupancy of such bands and the expected achievable performance on those bands can help secondary (unlicensed) networks to devise effective strategies to improve utilization. Such prior spatio-temporal spectrum usage statistics can either be obtained from a database that is maintained by the primary networks or could be measured by customized sensors deployed by the secondary networks. In this paper, we use Shepards interpolation technique to estimate a spatial distribution of spectrum usage over a region of interest, which we call the spectrum map. The interpolation is achieved by intelligently fusing the data shared by the the secondary nodes considering their mutual distances and spatial orientation with each other. The obtained map is a two-dimensional (2-D) interpolation function that is continuously differentiable and passes through all the spectrum usage values recorded at arbitrary locations; thus providing a reference for primary occupancy in that region. For determining the optimal locations for sensing primary activity, we use an iterative clustering technique that uses tree structured vector quantization. We use the spectrum map to estimate different radio and network performance metrics like channel capacity, network throughput, and spectral efficiency. As a comprehensive case study, we demonstrate how the spectrum map can be used for efficient resource allocation in TV white space. In particular, we consider an IEEE 802.22-based WRAN and show how the rendezvous probability can be improved for better radio resource allocation, thereby increasing the secondary spectrum utilization.

Utilizing misleading information for cooperative spectrum sensing in cognitive radio networks

In cognitive radio networks, the radios continuously scan the radio spectrum and create a spectrum usage report. Due to channel uncertainty, there are inaccuracies in these reports. Oftentimes, the radios share and fuse the observed data in order to increase the accuracy of the spectrum usage. However, malicious nodes tend to send false information (i.e., attack) in order to mislead the construction of the spectrum usage report. In this paper, we use a trust model to evaluate the trustworthiness of every node and use the trust values to effectively fuse the information from all nodes. A node compares the information sent by a neighboring node with the predicted information. Based on the ratio of matches (or mismatches), the neighboring node is assigned a trust value. Then, we propose a log-weighted metric utilizing trust values to distinguish malicious nodes from others. Subsequently, we propose threshold based Selective Inversion (SI) fusion and Complete Inversion (CI) fusion to effectively combine not only the information sent by honest nodes but also utilize misleading information sent by malicious nodes. We also propose a combination of the two inversion schemes. We compare the performance of the inversion based fusion schemes with blind and trust-based fusions. Results reveal better performance for inversion based fusion schemes for various intensities of attack. We also conduct simulations to evaluate the optimal thresholds that are used for invoking the inversion based fusion schemes.

A cloud service for trust management in cognitive radio networks

Transferring computations for cognitive radio network (CRN) management to a computer cloud opens the possibility to implement new, possibly more accurate and powerful resource management strategies. Algorithms to discover communication channels currently in use by a primary transmitter and identify malicious nodes with high probability could be based on past history; when the trust is computed by the mobile devices this approach is not feasible because such algorithms require massive amounts of data and intensive computations. In this paper, we introduce a cloud service based on a novel trust management algorithm; this solution, applicable to infrastructure-based and to ad hoc CRNs, ensures secure and robust operation in the presence of malicious nodes. We discuss the economic benefits, scalability and robustness of the proposed service for different network configurations and parameters.

Statistical Security Incident Forensics against Data Falsification in Smart Grid Advanced Metering Infrastructure

Compromised smart meters reporting false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grids operations. Most existing works only deal with electricity theft from customers. However, several other types of data falsification attacks are possible, when meters are compromised by organized rivals. In this paper, we first propose a taxonomy of possible data falsification strategies such as additive, deductive, camouflage and conflict, in AMI micro-grids. Then, we devise a statistical anomaly detection technique to identify the incidence of proposed attack types, by studying their impact on the observed data. Subsequently, a trust model based on Kullback-Leibler divergence is proposed to identify compromised smart meters for additive and deductive attacks. The resultant detection rates and false alarms are minimized through a robust aggregate measure that is calculated based on the detected attack type and successfully discriminating legitimate changes from malicious ones. For conflict and camouflage attacks, a generalized linear model and Weibull function based kernel trick is used over the trust score to facilitate more accurate classification. Using real data sets collected from AMI, we investigate several trade-offs that occur between attackers revenue and costs, as well as the margin of false data and fraction of compromised nodes. Experimental results show that our model has a high true positive detection rate, while the average false alarm rate is just 8%, for most practical attack strategies, without depending on the expensive hardware based monitoring.

Multinomial trust in presence of uncertainty and adversaries in DSA networks

Dynamic spectrum access (DSA) networks allow opportunistic spectrum access to license exempt secondary nodes. Usually secondary nodes employ a cooperative sensing mechanism to correctly infer spectrum occupancy. However, the possibility of falsification of locally sensed occupancy report, also known as secondary spectrum data falsification (SSDF) can cripple the operation of secondary networks. In this paper, we propose a multivariate Bayesian trust model for secondary nodes in a distributed DSA network. The proposed model accurately incorporates anomalous behavior as well as monitoring uncertainty that might arise from an anomaly detection scheme. We also propose possible extensions to the SSDF attack techniques. Subsequently, we use a machine learning approach to learn the thresholds for classifying nodes as honest or malicious based on their trust values. The threshold based classification is shown to perform well under different path loss environments and with varying degrees of attacks by the malicious nodes. We also show the trust based fusion model can be used by nodes to disregard a nodes information while fusing the individual reports. Using the fusion scheme, we report the improvements of cooperative spectrum decisions for various multi-channel SSDF techniques.

Bayesian inference based decision reliability under imperfect monitoring

Reliability of a cooperative decision mechanism is critical for the proper and accurate functioning of a networked decision system. However, adversaries may choose to compromise the inputs from different sets of components that comprise the system. Often times, the monitoring mechanisms fail to accurately detect compromised inputs; hence cannot categorize all inputs into polarized decisions: compromised or not compromised. In this paper, we propose a Bayesian inference model based on multinomial evidence to quantify reliability for a cooperative decision process as a function of beliefs associated with observations from the imperfect monitoring mechanism. We propose two reliability models: an optimistic one for a normal system and a conservative one for a mission critical system. We also provide an entropy measure that reflects the certainty or uncertainty on the calculated reliability of the decision process. Through simulation, we show how the reliability and its corresponding entropy changes as the accuracy of the underlying monitoring mechanism improves1.

Trust based channel preference in cognitive radio networks under collaborative selfish attacks

Secondary spectrum data falsification (SSDF) is a common attack in cognitive radio networks, where dishonest nodes share spurious local sensing data. This behavior misleads the collective inference on spectrum occupancy. The situation is more aggravated when a collaborative SSDF attack is launched by a coalition of selfish nodes. Defense against such collaborative attacks is difficult with popularly used voting based inference models. This paper proposes a method based on Bayesian inference that indicates how much the collective decision on a channels occupancy can be trusted. Using an anomaly monitoring technique, we check if the reports sent by a node match with the expected occupancy and classify the outcomes into three categories: i) if there is a match, ii) if there is a mismatch, and iii) if it cannot be decided. Based on the measured observations over time, we estimate the parameters of the hypothesis of match and mismatch events using a multinomial Bayesian based inference. We quantitatively define the trust as the difference between the posterior beliefs associated with matches and that of mismatches. The posterior beliefs are updated based on a weighted average of the prior information on the belief itself and the recently observed data. We conduct simulation experiments that show that the proposed trust model is able to distinguish the attacked channels from the non-attacked ones. Also, a node is able to rank the channels based on how trustworthy the inference on a channel is. We are also able to show that attacked channels have significantly lower trust values than channels that are not.

Towards Fast and Semi-supervised Identification of Smart Meters Launching Data Falsification Attacks

Compromised smart meters sending false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on the smart grid»s operation. Most existing defense models only deal with electricity theft from individual customers (isolated attacks) using supervised classification techniques that do not offer scalable or real time solutions. Furthermore, the cyber and interconnected nature of AMIs can also be exploited by organized adversaries who have the ability to orchestrate simultaneous data falsification attacks after compromising several meters, and also have more complex goals than just electricity theft. In this paper, we first propose a real time semi-supervised anomaly based consensus correction technique that detects the presence and type of smart meter data falsification, and then performs a consensus correction accordingly. Subsequently, we propose a semi-supervised consensus based trust scoring model, that is able to identify the smart meters injecting false data. The main contribution of the proposed approach is to provide a practical framework for compromised smart meter identification that (i) is not supervised (ii) enables quick identification (iii) scales classification error rates better for larger sized AMIs; (iv) counters threats from both isolated and orchestrated attacks; and (v) simultaneously works for a variety of data falsification types. Extensive experimental validation using two real datasets from USA and Ireland, demonstrates the ability of our proposed method to identify compromised meters in near real time across different datasets.

W2Q: A dual weighted QoI scoring mechanism in social sensing using community confidence

A significant vulnerability in social sensing based services is false notifications from sensing agents, thereby resulting in inaccurate published information that induces loss of revenue and business goodwill. Existing popular schemes utilize rating feedbacks (over the published information) to quantify the perceived usefulness (quality) of the information. However, these schemes do not reward the confidence of the feedback community and lacks provision to regulate the impact of uncertain feedbacks (ratings), and hence can be easily manipulated. In this paper, we propose a model, called W2Q, to mathematically evaluate the Quality of Information (QoI) as a function of the proportion of positive ratings, total number of ratings, and amortized proportion of uncertain ratings. The proposed model exploits Bayesian inference, and a dual weighted regression model to compute the QoI of any published information. We evaluate the proposed model through an experimental study assuming a crowd sourced-urban application as a proof of concept. Experimental results show that compared with the state-of-the-art Jøsangs belief model, the resultant QoI score is less susceptible to rogue ratings and captures subtle differences between true and false information.