Wireless networks are a common point of entry for computer network attacks. Due to high traffic volumes, network mission assurance requires tools that can usefully display network traffic data, automatically detect, and identify attacks to provide increased situational awareness to a network administrator. Many metrics used to analyze wireless network traffic and security depend on full access to all nodes. This is impractical in fielded networks. To address these issues, we propose a new set of metrics based on wireless network packet interarrival times. These metrics are displayed in a novel way to provide administrators with a mechanism for identifying possible attacks and their impact on the network. The performance of this visualizer is validated by the use of a linear classifier system, which shows that the chosen metrics can be used to accurately identify attacks. We further argue that the classifier could be used in conjunction with the visualizer as an effective decision support system to aid in maintaining mission assurance.

Received signal strength (RSS) based source localization papers often ignore the practical effects of range limits in the measurements. In many devices, this results in some sensors not reporting beyond some maximum range; and in others, the RSS is still observed but may exhibit a noise floor at large ranges. This paper models these situations and demonstrates their effect on positioning algorithms. Measured data is used to validate the cooperative and non-cooperative RSS limits. The Fisher information, Cramer-Rao lower bound, and maximum likelihood estimation error are used to quantify effects on positioning.

Cognitive radios have been touted as a solution to communicating in a Dynamic Spectrum Access environment. This paper examines how cognitive radios initially find one another among the expanse of ever-changing open spectrum, termed the rendezvous problem. Specifically, it addresses the problem of rendezvous under varying levels of system capabilities, spectrum policies, and environmental conditions. The focus is on rendezvous when there are are no control channels or centralized controllers, which we term the blind rendezvous problem. Under these conditions, a sequence-based and modular clock blind rendezvous algorithms are proposed, and it is shown that the performance of these algorithms compares favorably to that of a random blind rendezvous algorithm. Specifically, the sequence-based algorithm provides a bounded Time To Rendezvous (TTR) and the ability to prioritize channels where rendezvous is more likely to occur; the modular clock algorithm reduces the expected TTR, requires little precoordination among radios attempting to rendezvous, and is robust to radios sensing different sets of available channels.

In a cognitive network, autonomous and adaptive radios select their operating parameters to achieve individual and network-wide goals. The effectiveness of these adaptations depends on the amount of knowledge about the state of the network that is available to the radios. We examine the price of ignorance in topology control in a cognitive network with power- and spectral-efficiency objectives. We propose distributed algorithms that, if radios possess global knowledge, minimize both the maximum transmit power and the spectral footprint of the network. We show that while local (as opposed to global) knowledge has little effect on the maximum transmission power used by the network, it has a significant effect on the spectral performance. Furthermore, we show that due to the high cost of maintaining network knowledge for highly dynamic networks, the cost/performance tradeoff makes it advantageous for radios to operate under some degree of local knowledge, rather than global knowledge.We also propose distributed algorithms for power and frequency adaptations as radios join or leave the network, and assess how partial knowledge impacts the performance of these adaptations.

Most existing work on dynamic spectrum access deals with creating a spectral and temporal map of spectrum white space, and then filling it. The spectrum can be better utilized by increasing the spatial awareness of secondary users to include knowledge of the locations of all primary and secondary users, as well as the orientations and parameters of their directional or omni-directional antennas. This paper derives a maximum likelihood (ML) algorithm, an approximate ML algorithm, and associated performance bounds for jointly estimating a transmitter's position, orientation, beam width, and transmit power, as well as the environment's path loss exponent, using received signal strength measurements. The methods can be used for either a primary or secondary user. Simulations are used to determine what types of sensor geometries lead to good estimates of each parameter, to evaluate the performance of the estimators, and to determine spectrum availability as a function of spatial coordinates.

This paper presents the results of experiments to measure subjective G.711 audio quality resulting from wireless transmission over encrypted IEEE 802.11b, IEEE 802.11g, and IEEE 802.11n draft 2.0 wireless local area networks (WLANs). Following ITU-T Recommendation P. 800, forty-two human subjects rate audio recordings taken during 12 network conditions. WLAN encryption is alternated between WPA2 and WPA2 with transport mode 3DES IPsec, and simultaneous call volume is alternated between 6 or 10 simultaneous calls. Results indicate that a previous E-model calculation for 802.11b accurately predicts the subjective Mean Opinion Score (MOS). The upper bound for the decline in MOS due to 3DES IPsec is found to be less than 0.65 (90% CI) for all scenarios examined. Additionally, the subjective data suggest that the combination of G.711 encoding and 802.11 transmission makes toll quality (MOS > 4.0) calls over an encrypted 802.11 WLAN impractical.

The idea of the cognitive network, drawn from previous research in cognitive radios and cross-layer design, is an attempt to address issues of complexity and scalability by allowing the network to self-adapt and reach end-to-end goals. This paper identifies three critical design decisions that must be considered when creating a cognitive network: the behavior of the decision-making elements, amount of computational state available to these elements, and the degree of control they have. To measure the effect of these design tradeoffs, we develop and define a metric called the "price of a feature." This metric is used to quantify the effect of these properties on a cognitive network with the objective of maximizing the lifetime of a multicast flow in a wireless environment. For this cognitive network, we show that the price of selfish behavior and partial control are more significant than the price of ignorance.

Wireless topology control is the process of structuring the connectivity between network nodes to achieve some network-wide goal. This paper presents a cognitive network approach to achieving the objectives of power and spectrum management. We cast the problem as a two phased non-cooperative game and use the properties of potential game theory to ensure the existence of, and convergence to, a desirable Nash equilibrium. Although this is a multi-objective optimization and the spectrum management problem is NP-hard, this selfish cognitive network constructs a topology that minimizes the maximum transmission power while simultaneously using, on average, less than 12% extra spectrum, as compared to the ideal solution.

Satellite-based mobile multicasting is a largely unexplored and untested area of networking. This paper examines the performance associated with applying distance vector multicast routing protocol (DVMRP) and on demand multicast routing protocol (ODMRP) to a six plane, 66-satellite low Earth orbit satellite (LEOs) constellation. ODMRP provides high quality-of-service performance at the expense of bandwidth efficiency. In contrast, DVMRP is over twice as efficient, with only a 10% lower quality-of-service. Both protocols are stressed under a single satellite failure. For DVMRP, this leads to greater end-to-end delay and slightly higher efficiency. ODMRP shows no significant change from the non-failure case.