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In this paper, we consider a communication connectivity problem involving a primary user (transmitter, for example, a Ground Control Station (GCS)) servicing a group of secondary users (receivers, for example, drones) under hostile interference. We formulate this multi-link communication connectivity problem, where the channels are affected by Rayleigh fading, as a zero-sum power resource allocation game between a transmitter and an adversary (jammer). The transmitter's objective is to maximize the probability of communication connectivity with all the receivers. It is proven that the problem has unique equilibrium in power allocation strategies, and the equilibrium is derived in closed form. Moreover, we reduce the problem of designing the equilibrium in power resource allocation strategies to the problem of finding a fixed point of a real-valued function. An algorithm based on the bisection method to find the fixed point (and so equilibrium strategies) is developed, and its convergence is proven.

We consider the communication between a source (user) and a receiver in the presence of multiple jammers, and study resource assignment in a non-cooperative game theory framework using communication latency as the user's utility. Equilibrium strategies are found in closed form, and their uniqueness is proven. This allows one to identify jammers whose equilibrium strategies have an on/off structure, or they are sensitive to small variations in the network parameters.

The paper considers a multi-access channel scenario, where several users communicate with a base station, and investigates power allocation is a game-theoretic framework. The communication metric is the inverse signal-to-interference-plus-noise ratio (SINR) at the base station, which, for low SINR reflects communication delay. Each user faces a trade-off between the latency of the signal received by the base station, and the price that the user pays for using a specific amount of power that causes interference in the system. The equilibrium is derived in closed form and its uniqueness is proven. It is shown that the resulting strategy allows each user to maintain uninterrupted communication. For comparison purposes, we construct a specific three user network scenario, and study the SINR and throughput metrics. In that setting, we show that, unlike the latency metric, SINR and throughput may give rise to multiple equilibria, which may cause destabilization of communication.

We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework using communication latency as the user’s utility. The user switches between two different modes, i.e., the (a) regular transmission mode, according to which both players follow a Nash equilibrium; and the (b) smart transmission mode, according to which the user always implements the best response strategy. First, we consider the case in which the switching between transmission modes occurs with a given frequency. For this case we find the optimal transmission power of the user by formulating and solving a Bayesian game problem. We show that an increase in the frequency of smart transmissions leads to a decrease in communication latency and to an increase in the total transmission cost. We determine the switching frequency that optimizes the latency-cost trade off using α-fairness criteria. We also discuss the implications of the proposed latency metric on the player strategies as compared to the previously well studied signal-to-interference-plus-noise ratio (SINR) metric.

Wireless power transfer (WPT) promises to deliver energy to devices that are otherwise hard to charge or replace batteries for. This paper presents a new power transfer approach by aligning the phases of a collection of radio frequency (RF) energy chargers at the target receiver device. Our approach can ship energy over tens of meters and to mobile targets. More importantly, our approach leads to a highly asymmetric energy density distribution in the charging area: the energy density at the target receiver is much higher than the energy density at other locations. It is a departure from existing beamforming based WPT systems that have high energy along the energy beam path. Such a technology can enable a large array of batteryless Internet of Things applications and render them much more robust and long-running. Thanks to its asymmetric energy distribution, our approach potentially can be scaled up to ship higher level of energy over longer distances. In this paper, we design, prototype, and evaluate the proposed energy transfer approach, referred to as Energy-Ball. We implement an Energy-Ball testbed that consists of 17 N210 and 4 B210 Universal Software Radio Peripheral (USRP) nodes, yielding a 20 x 20 m2 energy delivery area. We conduct carefully designed experiments on the testbed. We demo that the energy density of Energy-Ball at the target spot is considerably higher than the energy density elsewhere, with the peak to average power ratio of 8.72. We show that Energy-Ball can transfer energy to any point within the area. When the receiver moves at a speed of 0.5 m/s, Energy-Ball can transfer 80% of optimal power to the mobile receiver. Further, our results also show Energy-Ball can deliver over 0.6mw RF power that enables batteryless sensors at any point across the area.

In this work, we propose a system that detects approaching cars for smartphone users. In addition to detecting the presence of a vehicle, it can also estimate the vehicle’s driving direction, as well as count the number of cars around the user. We achieve these goals by processing the acoustic signal captured by microphones embedded in the user’s mobile phone. The largest challenge we faced involved addressing the fact that vehicular noise is predominantly due to tire-road friction, and therefore lacked strong (frequency) formant or temporal structure. Additionally, outdoor environments have complex acoustic noise characteristics, which are made worse when the signal is captured by non-professional grade microphones embedded in smartphones. We address these challenges by monitoring a new feature: maximal frequency component that crosses a threshold. We extract this feature with a blurred edge detector. Through detailed experiments, we found our system to be robust across different vehicles and environmental conditions, and thereby support unsupervised car detection and counting. We evaluated our system using audio tracks recorded from seven different models of cars, including SUVs, medium-sized sedans, compact cars, and electric cars. We also tested our system with the user walking in various outdoor environments including parking lots, campus roads, residential areas, and shopping centers. Our results show that we can accurately and robustly detect cars with low CPU and memory requirements.

Unutilized spectra, i.e., spectrum holes, are opportunities that may be used for communication or adapting other services that use radio frequency (RF). Such opportunities can also represent an adversarial target, if his objective is to block the RF system from using such opportunities opened by spectrum holes. In this paper, we explore the challenge of finding spectrum holes in an adversarial environment. First , by means of a simple model, we show that an adversary’s attack designed to close spectrum holes can be more harmful for the spectrum holes than just random jamming. This calls for designing a scanning strategy to detect such an attack. Second , by applying a game-theoretical model, we design the optimal scanning strategy to detect such attacks. In particular, we show the efficiency of such a scanning strategy compared with uninformed random scanning. This efficiency is achieved by focusing scanning efforts on the bands that will be more likely under attack, and neglecting less promising bands. Beyond the benefits, though, such a strategy has also drawbacks since, if the adversary has a different objective, such as sneaking usage of the spectrum, he can sneak usage undetected by using the bands neglected by such specially tuned scanning. To deal with this problem, third , we suggest to combine this strategy with a strategy that maximizes detection probability in a learning algorithm that updates the beliefs about the adversary’s objective. The convergence of the combined algorithm is proven.

Journal of Cybersecurity and Privacy is critical to society at large, and its importance is growing daily. The rapid advancement in technology has caused a large-scale integration of computing, communication, and information technologies into virtually every aspect of our modern society[...]

The Spectrum Collaboration Challenge (SC2) was started by DARPA in 2016 to further expand the research on spectrum usage efficiency, and mitigate the ever-growing problem of spectrum scarcity. Teams that participated in SC2 designed and developed wireless networks, called Collaborative Intelligent Radio networks (CIRNs), to compete with other teams’ CIRNs. The scoring system was created to motivate maximizing both their own and other networks’ data throughput. To improve the spectrum usage efficiency, teams were encouraged to use Artificial Intelligence, as well as to collaborate with other teams and agree on spectrum usage schedules that work best for all parties. To facilitate this collaboration, DARPA has established the CIRN Interaction Language (CIL) – a language CIRNs can use to communicate with other networks and establish common spectrum goals and ways to achieve them. One of CIL’s main functionalities was to enable teams to announce their intended spectrum usage and provide information other teams can use to adapt their own channel selection. While potentially a beneficial concept, CIL’s effect on ensemble throughput of all networks was never evaluated, as a proper evaluation framework was never provided by DARPA, since it was not possible to disable it. This paper describes a simplified simulation of the spectrum usage announcement functionality of the CIL, explains the experiments run to evaluate CILs gains, and showcases the obtained results.

Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.

Due to the shared and open-access nature of the wireless medium, wireless networks are vulnerable to jamming attacks. In this paper we study the problem of resource assigning in a single carrier communication system, where a user is communicating with a destination in the presence of a jammer. The jammer’s channel to the destination is assumed flat fading, and its gain is known in probabilistic terms. In particular, the jammer’s channel gain could take any value out of a finite set, with an a priori known probability. We model the problem in a Bayesian jamming game framework with utility the user throughput. We prove the existence and uniqueness of Nash and Stackelberg equilibria, and derive the equilibrium strategies in closed form. Our theoretical results, also supported by simulations, suggest that the Nash strategy is more sensitive to varying a priori probabilities, as compared to the Stackelberg strategy.

Underlay is an aggressive spectrum sharing paradigm that allows secondary nodes to be active simultaneously with the primary nodes through interference cancelation (IC). In this paper, we design an online admission control algorithm to handle dynamic session arrival and departure in the underlay coexistence paradigm for multi-hop primary and secondary networks. For IC, we employ multiple antennas at each secondary node. Through distributed computation and degree-of-freedom (DoF) allocation at each secondary node, our algorithm ensures that all interference to/from the multi-hop primary network and interference within the multi-hop secondary network are canceled properly so that data transport is free of interference in both multi-hop primary and secondary networks. Further, we show that the DoF allocation by our algorithm is feasible (implementable) at the physical layer at all time. Through extensive performance evaluation, we find that our online admission control algorithm can offer competitive performance when compared to an offline centralized algorithm.

Mobile ad hoc networks face many challenges when they are deployed, particularly problems associated with security and network failures arising from the dynamics of their network topologies. To address these issues, there have been many efforts to introduce security into the actual network protocols, most of which unfortunately struggle with the trade-off between security and network overhead. Consequently, it is often desirable to have a lightweight approach to introduce security and robustness, where one employs conventional and well-understood network protocols, and attempts to achieve the assurability of the network through higher-layer, overlay mechanisms. This paper explores a lightweight overlay tunneling scheme operating at the Internet protocol layer as a policy tool that can mitigate network failures and attacks directed at the network without involving the redesign of the underlying routing protocols. Moreover, two optimizations are proposed that lead to improved performance when additional information is available. Copyright © 2016 John Wiley & Sons, Ltd.

We propose a distributed access point selection scheme by which nodes select an appropriate access point to associate with based upon each individual device's channel utilization. In this paper, we define channel utilization as the ratio of required bandwidth to estimated available bandwidth. By incorporating channel utilization into the access point selection protocol, we can effectively reduce unnecessary reassociations and improve upper layer performance such as throughput and packet delivery delay. We have further enhanced our association protocol by using reinforcement learning to dynamically schedule the probing of neighboring access points (APs), ultimately bringing down the probing overhead by learning from past experience. When channel utilization is combined with adaptive probing, we observe a significant performance improvement compared to traditional association approaches.

In this paper, we suggest a new class of anti-jamming problems where the type of intelligence associated with a jamming attack is unknown. Specifically, we consider a problem where the nodes of a peer-to-peer network do not know whether the network is under attack by a random jammer (which might be considered as a natural background noise), or an intelligent one (i.e., the jammer who can adapt his strategy based on knowledge gained during attacks). The goal of the nodes is to identify the type of the attack based on knowledge obtained from the attack in previous time slots, and thereby to reduce the efficiency of the jamming attack. First, we model the problem as a Bayesian game for a single time slot attack, and reduce it to the solution of dual linear programming (LP) problems. Additionally, the convergence of the fictitious play algorithm for finding the equilibrium is established. Then, we develop the problem for a repeated jamming attack, where the nodes adapt their beliefs based on history of the previous attacks. In particular, we have shown that it is possible for the nodes in the network to always be able to identify the jammer’s type within a finite number of time slots.

Localization of wireless devices in wireless networks is important because the location of wireless devices is a critical input for many higher‐level networking tasks. Both the placement and the selection of anchors can affect the accuracy of location estimation significantly, and consequently impact the widespread deployment of location‐based services (LBSs). This chapter investigates the problems of anchor placement and anchor selection. It discusses how the geometric layout of the anchors affects the localization performance. Different approaches that include heuristic search methods, acute triangular‐based deployment, adaptive beacon placement, and optimal placement solutions via the maximum lambda and minimum error (maxL‐minE) algorithm are discussed. The chapter studies how smartly selected anchors can achieve comparable or better localization results as compared with using all the available anchors. A variety of effective strategies are investigated, including a joint clustering technique, entropy‐based information gain, convex hull selection, and smart selection in the presence of high density of anchors.

Graph theoretical analysis is a powerful tool for quantitatively evaluating brain connectivity networks. Conventionally, brain connectivity is assumed to be temporally stationary, whereas increasing evidence suggests that functional connectivity exhibits temporal variations during dynamic brain activity. Although a number of methods have been developed to estimate time-dependent brain connectivity, there is a paucity of studies examining the utility of brain dynamics for assessing brain disease states. Therefore, this paper aims to assess brain connectivity dynamics in Parkinson's disease (PD) and determine the utility of such dynamic graph measures as potential components to an imaging biomarker. Resting-state functional magnetic resonance imaging data were collected from 29 healthy controls and 69 PD subjects. Time-varying functional connectivity was first estimated using a sliding windowed sparse inverse covariance matrix. Then, a collection of graph measures, including the Fiedler value, were computed and the dynamics of the graph measures were investigated. The results demonstrated that PD subjects had a lower variability in the Fiedler value, modularity, and global efficiency, indicating both abnormal dynamic global integration and local segregation of brain networks in PD. Autoregressive models fitted to the dynamic graph measures suggested that Fiedler value, characteristic path length, global efficiency, and modularity were all less deterministic in PD. With canonical correlation analysis, the altered dynamics of functional connectivity networks, and particularly dynamic Fiedler value, were shown to be related with disease severity and other clinical variables including age. Similarly, Fiedler value was the most important feature for classification. Collectively, our findings demonstrate altered dynamic graph properties, and in particular the Fiedler value, provide an additional dimension upon which to non-invasively and quantitatively assess PD.

In cognitive radio network, cognitive radios dynamically reconfigure themselves based on the spectrum opportunity. It is envisioned to be the key of overcoming the spectrum shortage. However, such reconfigurability also amplifies potential harmful interferences from non-compliant radios. In this paper, we propose ROSTER, a radio context attestation protocol for cognitive radio network. The proposed protocol is based on our observation that the compliance of a radio transmission depends on software configuration, radio configuration as well as the location and time of the device, which we call radio context. We believe radio context attestation, which allows the authority to verify the operational integrity of individual cognitive radio, is a fundamental security function for cognitive radio networks. To the best of our knowledge, we are the first to study this important problem. Different from conventional software attestation, ROSTER is designed to handle dynamic configurations in cognitive radios. Furthermore, special considerations are given in the protocol design to accommodate different levels of sensitivity in spectrum databases. Besides protocol design and security analysis, we also build a prototype of the proposed system using Raspberry Pi, USRP, and Amazon AWS. Network simulation using the benchmark measurements from the prototype shows the scalauility of our proposed protocol.

Ensuring confidentiality of communication is fundamental to securing the operation of a wireless system, where eavesdropping is easily facilitated by the broadcast nature of the wireless medium. By applying distributed phase alignment among distributed transmitters, we show that a new approach for assuring physical layer secrecy, without requiring any knowledge about the eavesdropper or injecting any additional cover noise, is possible if the transmitters frequently perturb their phases around the proper alignment phase while transmitting messages. This approach is readily applied to amplitude-based modulation schemes, such as PAM or QAM. We present our secrecy mechanisms, prove several important secrecy properties, and develop a practical secret communication system design. We further implement and deploy a prototype that consists of 16 distributed transmitters using USRP N210s in a 20 × 20 × 3 m3 area. By sending more than 160M bits over our system to the receiver, depending on system parameter settings, we measure that the eavesdroppers failed to decode 30% — 60% of the bits cross multiple locations while the intended receiver has an estimated bit error ratio of 3 × 10−6.

Ensuring confidentiality of communication is fundamental to securing the operation of a wireless system, where eavesdropping is easily facilitated by the broadcast nature of the wireless medium. By applying distributed beamforming among a coalition, we show that a new approach for assuring physical layer secrecy, without requiring any knowledge about the eavesdropper or injecting any additional cover noise, is possible if the transmitters frequently perturb their phases around the proper alignment phase while transmitting messages. This approach is readily applied to amplitude-based modulation schemes, such as PAM or QAM. We present our secrecy mechanisms, prove several important secrecy properties, and develop a practical secret communication system design. We further implement and deploy a prototype that consists of 16 distributed transmitters using USRP N210s in a 20×20×3m3 area. By sending more than 160M bits over our system to the receiver, depending on system parameter settings, we measure that the eavesdroppers failed to decode 30%–60% of the bits cross multiple locations while the intended receiver has an estimated bit error ratio of 3×10−6.

Many cyber physical networks will involve ad hoc deployments utilizing peer-to-peer communications. Examples include transportation systems where a group of moving cars communicate in order to avoid collisions, teams of robotic agents that work together in support of disaster recovery, and sensor networks deployed for health-care monitoring, monitoring the operation of a factory plant or to coordinate and actuate mechanisms for energy conservation in a building. These networks may face a variety of threats that puncture their connectivity and, should their performance degrade, the result could be catastrophic. Consider, for example, a vehicular ad hoc network where communication assists collision avoidance. In such a case, degradation could lead to vehicle accidents. Therefore, in order to overcome network performance degradations and the puncture of a network (such as blackhole or jamming) which is under attack, we propose an algorithm called the Fiedler Value Power Adjustment Topology Adaption (FVPATA). FVPATA aims to dynamically adapt an ad hoc network’s topology, even if the attacker varies its location and in the case of an interference-style attack by increasing the interference power. The algorithm utilizes the formulation from the graph theory which works with the Fiedler value to guide each node in wireless ad hoc network utilizing power adjustments to enhance the network’s overall robustness. The advantage of the proposed mechanism is that it is a light-weight approach which is totally distributed, based on topology updates inherent in the Optimized Link State Routing (OLSR) protocol and, hence, it is unnecessary to introduce additional messages. Additionally, an algorithm was developed to resolve problems involving asymmetric links that arise in ad hoc networks by eliminating unnecessary energy consumption of Fiedler nodes. Simulation results using NS3 show that the proposed mechanism successfully decreases the average amount of hops used by 50% and the delay of flows when nodes are migrating at a modest rate below 60 m/min.

There has recently been significant interest in applying the principles of information-theoretical security and signal processing to secure physical layer systems. Although the community has made progress in understanding how the physical layer can support confidentiality and authentication, it is important to realize that there are many important issues that must be addressed if physical layer security is ever to be adopted by real and practical security systems. In this article, I briefly review several different flavors of physical layer security (at least for wireless systems), and then identify aspects (a.k.a. weaknesses) where the foundation for physical layer security needs to be strengthened. I then highlight that the opportunities for applying physical layer security to real systems will be quite rich if the community can overcome these challenges. In the course of the article, I note new directions for the community to investigate, with the objective of keeping physical layer security research targeted at having a practical impact on real systems.

The performance and resilience of communication networks is intimately tied to their underlying topological structure. Networks whose topological graphs do not exhibit sufficient interconnection are brittle and can be easily broken by network attacks that break links or remove nodes from the network. In this chapter, we explore the notion of network connectivity as quantified by the Fiedler value. We illustrate how it can be used by an adversary to determine locations where to attack a communication network and, conversely, how it can be used by network defense protocols to strengthen the network, thereby providing resilience to network attacks.

The paper considers a communication system consisting of a communication node utilizing multiple antennas in order to communicate with a group of receivers, while potentially facing interference from one or more jammers. The jammers impact the scenario by possibly interfering some of the receivers. The objective of the jammers is to reduce the throughput of nearby receivers, while taking into account the cost/risk of jamming. The fact that jammers face a cost implies that they might not choose to interfere, and thus the communication node faces uncertainty about which of its receivers will be jammed. This uncertainty is modeled by the communicator having only a priori probabilities about whether each receiver will face hostile interference or not, and if he does face such jamming, whether the jamming attack is smart or not. The goal of the communication node is to distribute total power resources to maximize the total throughput associated with communicating with all of the receivers. The problem is formulated as a Bayesian game between the communication system and the jammers. A waterfilling equation to find the equilibrium is derived, and its uniqueness is proven. The threshold value on the power budget is established for the receivers to be non-altruistic.

Many new "networkable" devices, which constitute the Internet of Things, are low energy and lightweight. These devices must devote most of their available energy and computation to executing core application functionality, making the task of affordably supporting security and privacy quite challenging.

The Internet of Things (IoT) is a recent trend that extends the boundary of the Internet to include a wide variety of computing devices. Connecting many stand-alone IoT systems through the Internet introduces many challenges, with security being front-and-center since much of the collected information will be exposed to a wide and often unknown audience. Unfortunately, due to the intrinsic capability limits of low-end IoT devices, which account for a majority of the IoT end hosts, many traditional security methods cannot be applied to secure IoT systems, which open a door for attacks and exploits directed both against IoT services and the broader Internet. This paper addresses this issue by introducing a unified IoT framework based on the MobilityFirst future Internet architecture that explicitly focuses on supporting security for the IoT. Our design integrates local IoT systems into the global Internet without losing usability, interoperability and security protection. Specifically, we introduced an IoT middleware layer that connects heterogeneous hardware in local IoT systems to the global MobilityFirst network. We propose an IoT name resolution service (IoT-NRS) as a core component of the middleware layer, and develop a lightweight keying protocol that establishes trust between an IoT device and the IoT-NRS.

Unutilized spectrum, i.e. spectrum holes, are opportunities that may be used for communication or other RF services. In this paper, we explore adversarial attacks that reduce the size of spectrum holes by showing their advantage compared to a random jammer. Using a game-theoretical approach, we design an optimal scanning strategy that provides an increased probability of detecting such an attack. The advantage of our strategy is achieved by focusing scanning efforts on bands that are more likely to be attacked, and neglecting the others. However, such focused scanning is a disadvantage since, if the adversary has a different objective, he can safely sneak usage of the bands neglected by such a specially-tuned spectrum scanner. To deal with this problem, we also derive the optimal scanning allocation that balances between applying the anti-spectrum holes attack scanning strategy and scanning the neglected bands so as to prevent the possibility of the adversary using those bands without being detected.

Wireless networks are vulnerable to many malicious attacks, like active interference, due to the shared and open-access nature of the wireless medium. One difficulty in dealing with interference attacks is the lack of knowledge about whether the attack will be present or not. In this paper, we investigate how the lack of knowledge of whether jamming will or will not be used against an OFDM communication impacts the anti-jamming strategy one should use. Our model can be applied more generally to jamming attacks with a fixed frequency where, at each time slot, it is unknown whether the interference will be used or not. We have derived the equilibrium strategy in waterfilling form, and shown that the obtained solution includes as boundary cases two classical solutions in OFDM transmission for the case where there is no adversary, and the case where the adversary attacks with certainty. Uniqueness of the equilibrium strategies is proven, and an algorithm for finding the parameters of the waterfilling equations is developed. The efficiency of the algorithm is illustrated by numerical examples.

The articles in this special section is designed to provide a venue for state-of-the-art research being doing in how signal and information processing is advancing the field of information privacy.

Wireless networks are increasingly moving towards a heterogeneous operating model involving the sharing of spectrum resources by different access technologies. Sharing wireless resources between different wireless technologies requires protocols that share spectrum in an equitable manner. In this paper, we examine the time-sharing of wireless channels by Wi-Fi and LTE-U networks. To design fair access protocol for the networks we use \(\alpha \) fairness criterion. It allows to find a continuum of fair protocols (a protocol per \(\alpha \)). To find the most fair from this continuum of fair protocols we apply Nash bargaining approach. In particular, we show that such a time-sharing bargaining protocol, in spite of the interference between signals, can lead to a gain for both networks under an increase of the transmission power to one of them.

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.

Practical generation of secret bits using wireless channel measurements between two terminals is presented in this paper. The model used for this implementation assumes imperfect reciprocity between the channel measurements made by the two terminals in the presence of a passive eavesdropper. The channel measurements (e.g., received signal strength values) are used to estimate the secret key rate in order to establish the number of secret bits that can be generated between any two terminals. The main contributions of this paper are: 1) algorithm to generate uncorrelated channel measurements even when channel measurement statistics between the two terminals are time-varying; 2) development of a reconciliation algorithm based on BCH codes, where the statistics of error between bits generated by the two terminals is not known a priori; and 3) implementation of secret bit generation using channel reciprocity in a Linux environment. The channel measurement data that was collected for this paper is available at http://downloads.i-a-i.com/RAPOR.

As people become increasingly accustomed to using smartphones while they walk, there will be a corresponding concern about pedestrian safety as smartphone users might become distracted. In this work, we address this concern by developing a system for smartphones that warns users when it detects an oncoming car. In addition to detecting the presence of a vehicle, it can also estimate the vehicle's driving direction. We achieve these goals by processing the acoustic signal captured by microphones embedded in the user's mobile phone. In order to achieve more robust and timely detection, we also explore two novel feature, namely, Power on Certain Frequencies and Top Right frequency.

Maintaining both reliable and secret communication involves different objectives: ensuring sufficient throughput for reliable communication and ensuring the secrecy of the associated communication. The optimal solution for one of these objectives might not be optimal or even suitable for another objective and thus a question arises as to what is the appropriate criteria for fairly balancing these objectives. We solve this problem in two steps. In the first step,we use the α-fairness criteria for formulating the tradeoff between these objectives. Its advantage is that it allows one to consider a continuum of fairness criteria in a unified framework, from cooperative fulfilment of both objectives (for α = 0) to a maxmin scenario as α tends to infinity. In the second step, by means of bargaining over the continuum of obtained optimal fair solutions, we find the solution that is optimally fair. We illustrate the suggested approach by a closed-form solution obtained for transmission in the low SNR regime.

This paper focuses on the challenge of maintaining reliable connectivity in an ad hoc network, where interference is possible. To cope with such interference, the paper introduces throughput connectivity and weighted throughput connectivity. Throughput connectivity reflects the possibility of establishing communication between nodes for given a signal power level, while weighted throughput connectivity associates the throughput as a weight in the associated network graph. Throughput connectivity is less sensitive to network's parameters than the one based on weighted throughput connectivity. It makes maintaining throughput connectivity protocol less resource consuming (say, by sending less frequently channel state information (CSI)). Whereas, weighted throughput protocol is more efficient in power allocation due to employing a continuous scale in Laplacian matrix. To illustrate these notions, two approaches to maximize connectivity were considered: (a) an adaptive transmission protocol that re-allocates transmission power between nodes, and (b) detecting and eliminating a malicious threat to maintain accumulated connectivity over time slots. The first problem was modeled by a maxmin problem, and solved by Semi-Definite Programming. The second problem was modeled by a stochastic game and solved explicitly.

Multiple-input multiple-output (MIMO) technologies are a popular choice for emerging wireless systems due to their promised gains in throughput and reliability. In order to realize any gains over traditional non-MIMO communication systems, these systems must possess accurate knowledge of the wireless channel. In this paper, we investigate strategies for disrupting MIMO communications by developing attacks that target the often overlooked, but essential, channel estimation procedure. Our study focuses on the two most popular and well-known MIMO techniques: the capacity achieving SVD-based MIMO scheme, and the Alamouti space-time block code (STBC), which spans many protocols including 802.11n, WiMAX, and 3GPP. We augment theoretical and simulation results with real-world experimentation using the USRP/GNU Radio software defined radio platform. We also present novel methodology to protect the channel estimation procedure from such attacks by embedding authentication messages into physical layer features of the transmissions.

We describe a method for estimating achievable data rates in urban microcells using multiple-input/multiple-output (MIMO) techniques. Specifically, we use site maps and a versatile ray-tracing tool to compute MIMO gain matrices as a function of terminal location; and we use these matrices to determine achievable rates for various MIMO transmission modes (spatial multiplexing, beamforming, and diversity). Numerical results are generated for specific paths in Boston and Manhattan, though our results are shown to be fairly insensitive to neighborhood or city. We also show that, in urban microcells, data rate prediction using site-specific ray tracing is more informative than using stochastic models; and that adaptive switching among MIMO transmission modes as a terminal moves along its trajectory can help sustain high data rates. A new mode-switching algorithm is proposed that requires switching rates lower than those for the optimal scheme by a factor greater than 10, with little loss in average data rate. We also propose a novel software algorithm for optimally placing microcell bases. For a Manhattan neighborhood of area 0.5 km2, we find that full coverage can be obtained using only 5 bases, and that the highest total throughput is achieved using a frequency reuse factor of 1.

Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that access spectrum resources in a proper, disciplined manner. For a cognitive radio network to achieve this goal, spectrum policies and the ability to enforce them are necessary. Detection of an unauthorized (anomalous) usage is one of the critical issues in spectrum etiquette enforcement. In this paper, we present a network structure for dynamic spectrum access and formulate the anomalous usage detection problem using statistical significance testing. The detection problem investigated considers two cases, namely, the authorized (primary) transmitter is (i) mobile and (ii) fixed. We propose a detection scheme for each case by exploiting the spatial pattern of received signal energy across a network of sensors. Analytical models are formulated when the distribution of the energy measurements is given and, due to the intractability of the general problem, we present an algorithm using machine learning techniques to solve the general case when the statistics of the energy measurements are unknown. Our simulation results show that our approaches can effectively detect unauthorized spectrum usage with a detection probability above 0.9 while keeping the false alarm rate less than 0.1 when only one unauthorized radio is present, and the detection probability is even higher for more unauthorized radios.

Sniffing encrypted data packets traveling across networks can often be useful in inferring nontrivial information about their contents because of the manner in which the transmission of such packets is handled by lower layers in the communications protocol stack. In this paper, we formally study the side-channel formed by variable packet sizes, and explore obfuscation approaches to prevent information leakage while jointly considering the practical cost of obfuscation. We show that randomized algorithms for obfuscation perform best and can be studied as well-known information-theoretic constructs, such as discrete channels with and without memory. We envision a separate layer called a Bit - Trap, that employs buffering and bit-padding as orthogonal methods for obfuscating such side channels. For streams of packets, we introduce the use of mutual-information rate as an appropriate metric for the level of obfuscation that captures nonlinear relationships between original and modified streams. Using buffering-delay and average bit-padding as the respective costs, a Bit - Trap formulates a constrained optimization problem with bounds on the average costs, to implement the best possible obfuscation policy. We find that combining small amounts of delay and padding together can create much more obfuscation than either approach alone, and that a simple convex trade-off exists between buffering delay and padding for a given level of obfuscation.

The nodes in a wireless ad hoc network act as routers in a self-configuring network without infrastructure. An application running on the nodes in the ad hoc network may require that intermediate nodes act as routers, receiving and forwarding data packets to other nodes to overcome the limitations of noise, router congestion and limited transmission power. In existing routing protocols, the ‘self-configuring’ aspects of network construction have generally been limited to the construction of routes that minimize the number of intermediate nodes on a route while ignoring the effects that the resulting traffic has on the overall communication capacity of the network. This paper presents a context-aware routing metric that factors the effects of environmental noise and router congestion into a single time-based metric, and further presents a new cross-layer routing protocol, called Warp-5 (Wireless Adaptive Routing Protocol, Version 5), that uses the new metric to make better routing decisions in heterogeneous network systems. Simulation results for Warp-5 are presented and compared to the existing, well-known AODV (Ad hoc On-Demand Distance Vector) routing protocol and the reinforcement-learning based routing protocol, Q-routing. The results show Warp-5 to be superior to shortest path routing protocols and Q-routing for preventing router congestion and packet loss due to noise. Copyright © 2011 John Wiley & Sons, Ltd. This paper presents a cross-layer predictor for route selection in wireless ad hoc networks that combines the effects of noise and router congestion into a single time-based routing metric based on statistical estimation from recent experience. A new cross-layer, adaptive routing protocol, called Warp-5, uses this new routing metric to make better routing decisions in noisy or congested wireless ad hoc networks.

While conventional cryptographic security mechanisms are essential to the overall problem of securing wireless networks, they do not directly leverage the unique properties of the wireless domain to address security threats. The wireless medium is a powerful source of domain-specific information that can complement and enhance traditional security mechanisms. In this article we argue that new security paradigms which exploit physical layer properties of the wireless medium, such as the rapid spatial, spectral, and temporal decorrelation properties of the radio channel, can enhance confidentiality and authentication services. We outline some basic constructions for these services, and then provide a case study for how such strategies can be integrated into a broader security framework for a wireless network.

We consider a secret communication scenario where Alice wants to transmit secretly to Bob in presence of a passive eavesdropper Eve. The Alice-Bob channel is a fixed-SNR AWGN channel, while the Alice-Eve channel is a fast Rayleigh fading channel, with the channel states only known to Eve. Alice knows the statistics of Alice-Eve channel, but not the exact realizations. We investigate the achievable secrecy rates for this channel model with Gaussian signaling and discrete signaling. For Gaussian signaling, several transmission strategies according to the main channel's relative channel gain are proposed and evaluated. For discrete signaling, achievable secrecy rates with Quadrature Amplitude Modulation (QAM) are evaluated. When Bob's channel is much better than Eve's channel, simple Gaussian signaling can perform close to the upper bound, and is better than the rate achieved with M-QAM. When Bob's channel gain is on average worse than the eavesdropper's average channel gain, positive secrecy rate can still be achieved for Gaussian signaling with artificial noise injection and a burst signaling strategy. Moreover, M-QAM can outperform Gaussian signaling. The key factor that enables secret communication in this case is that both M-QAM and artificial noise limit the leakage of information when Eve's channel is unusually good.

Cybersecurity is critical to society at large, and its importance is growing daily. The rapid advancement in technology has caused a large-scale integration of computing, communication, and information technologies into virtually every aspect of our modern society[...]

Wireless networks are vulnerable to identity-based attacks, including spoofing and Sybil attacks, which allows for many other forms of attacks on the networks. Although the identity of a node can be verified through cryptographic authentication, authentication is not always possible, because it requires key management and additional infrastructural overhead. In this paper, we propose a method for detecting both spoofing and Sybil attacks by using the same set of techniques. We first propose a generalized attack-detection model that utilizes the spatial correlation of received signal strength (RSS) inherited from wireless nodes. We further provide a theoretical analysis of our approach. We then derive the test statistics for detection of identity-based attacks by using the K-means algorithm. Our attack detector is robust when handling the situations of attackers that use different transmission power levels to attack the detection scheme. We further describe how we integrated our attack detector into a real-time indoor localization system, which can also localize the positions of the attackers. We show that the positions of the attackers can be localized using either area- or point-based localization algorithms with the same relative errors as in the normal case. We further evaluated our methods through experimentation in two real office buildings using both an IEEE 802.11 (WiFi) network and an IEEE 802.15.4 (ZigBee) network. Our results show that it is possible to detect wireless identity-based attacks with both a high detection rate and a low false-positive rate, thereby providing strong evidence of the effectiveness of the attack detector utilizing the spatial correlation of RSS and the attack localizer.

Wireless sensor networks are typically deployed to monitor phenomena that vary over the spatial region the sensor network covers. The sensor readings may also be dual-used for additional purposes. In this paper, we propose to use the inherent spatial variability in physical phenomena, such as temperature or ambient acoustic energy, to support localization and position verification. We first present the problem of localization using general spatial information fields, and then, propose a theory for exploiting this spatial variability for localization. Our Spatial Correlation Weighting Mechanism (SCWM) uses spatial correlation across different phenomena to isolate an appropriate subset of environmental parameters for better location accuracy. We then develop an array of algorithms employing environmental parameters using a two-level approach: first, we develop the strategies on how the subset of parameters should be chosen, and second, we derive mapping functions for position estimation. Our algorithms support our theoretical model for performing localization utilizing environmental properties. Finally, we provide an experimental evaluation of our approach by using a collection of physical phenomena measured across 100 locations inside a building. Our results provide strong evidence of the viability of using general sensor readings for location-aware applications.

Worldwide inter-operability for microwave access (WiMAX) is a promising technology that provides high data throughput with low delays for various user types and modes of operation. While much research had been conducted on physical and MAC layers, little attention has been paid to a comprehensive and efficient security solution for WiMAX. We propose a hybrid security solution combining identity-based cryptography (IBC) and certificate based approaches. We provide detailed message exchange steps in order to achieve a complete security that addresses the various kind of threats identified in previous research. While attaining this goal, efficient fusion of both techniques resulted in a 53% bandwidth improvement compared to the standard's approach, PKMv2. Also, in this hybrid approach, we have clarified the key revocation procedures and key lifetimes. Consequently, to the best of knowledge our approach is the first work that unites the advantages of both techniques for improved security while maintaining the low overhead for WiMAX.

The radio channel response decorrelates rapidly as the transmitter changes location in an environment with rich scatterers and reflectors. Based on this fact, a channel-based authentication scheme was previously proposed to discriminate between transmitters at different locations, and thus to detect spoofing attacks in wireless networks. In this paper, we study its application in frequency-selective Rayleigh channels, considering channel time variations due to environmental changes and terminal mobility, as well as the channel estimation errors due to the interference from other radios. We propose a generalized likelihood ratio test (GLRT) that is optimal but computationally cumbersome, and a simplified version that requires no a priori knowledge of channel parameters and is therefore more practical. We verify the efficacy of the channel-based spoofing detectors via numerical analysis, showing how performance is improved by using multiple antennas, higher transmit power, and wider system bandwidth. We show that, under a wide variety of practical conditions, spoofing can be detected with better than 90% probability while keeping the probability of falsely rejecting valid transmissions below 10%.

Due to the broadcast nature of the wireless medium, wireless networks are especially vulnerable to Sybil attacks, where a malicious node illegitimately claims a large number of identities and thus depletes system resources. We propose an enhanced physical-layer authentication scheme to detect Sybil attacks, exploiting the spatial variability of radio channels in environments with rich scattering, as is typical in indoor and urban environments. We build a hypothesis test to detect Sybil clients for both wideband and narrowband wireless systems, such as WiFi and WiMax systems. Based on the existing channel estimation mechanisms, our method can be easily implemented with low overhead, either independently or combined with other physical-layer security methods, e.g., spoofing attack detection. The performance of our Sybil detector is verified, via both a propagation modeling software and field measurements using a vector network analyzer, for typical indoor environments. Our evaluation examines numerous combinations of system parameters, including bandwidth, signal power, number of channel estimates, number of total clients, number of Sybil clients, and number of access points. For instance, both the false alarm rate and the miss rate of Sybil attacks are usually below 0.01, with three tones, pilot power of 10 mW, and a system bandwidth of 20 MHz.

We investigate the reliability of radio channel simulators in predicting channel responses throughout a well-specified environment. Indoor environments for which the geometric layout and material properties of surfaces are known lend themselves to such site-specific simulation. We assess the performance of this approach by comparing its predictions with measurements in a specific static environment. The good agreement on path loss, Ricean K-factor and RMS delay spread, over the set of paths measured and simulated, suggests that a well-designed radio simulator can be used reliably to predict system behavior. Typically, wireless channel models obtained through this or similar techniques do not capture the temporal variability in the channel response due to people movement in the environment. We treat the time-varying part of the channel response using stochastic processes. Using channel sounding experiments for several typical office scenarios, we show that autoregressive processes can be used to model the time-varying tap gains for several different motion scenarios.