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Next generation 5G cellular networks will consist of multiple technologies for devices to access the network at the edge. One of the keys to 5G is, therefore, the ability of devices to intelligently select its radio access technology (RAT). There have been several proposals for RAT selection in the last few years. Understanding the performance and limitation of these RAT selection solutions is important for their deployment in the future 5G heterogeneous networks. In this paper, we provide a taxonomy and comparative performance analysis of recent RAT selection algorithms, including the different network models that were used to evaluate these works. We combine these different network models to build a benchmark for evaluating the RAT selection algorithms in a 5G environment. We implement the representative algorithms of different approaches and cross compare them in our benchmark. From the experiments conducted, we illustrate how the different network parameters, such as the number of base stations visible to a user and the available link bandwidths, could impact the performance of these algorithms.

This paper describes a procedure for Angles-of-Arrival (AoA) estimation for a uniform linear array (ULA) with missing sensors. The novelty of the approach is that, rather than using AoA estimates obtained from contiguous subarrays, we use estimates for the corresponding signal subspaces. The report shows that the ESPRIT invariance equations for each contiguous subarray define an operator that “propagates” the signal sub-space beyond the physical array. Care needs to be taken to ensure the same bases are used for each subarray. The estimates of the signal subspaces for the missing array elements are appropriately combined to yield an estimate of the signal subspace for the complete ULA. The paper only addressed the case of one missing sensor, but the approach can be readily generalised. Simulations show that the proposed method yields AoA estimates which are very close to those obtained if there was no missing sensor, in contrast to the case where the measurements from the missing sensor were zero. In order to appropriately combine the estimates for the missing signal subspace terms, the report assessed the accuracy of signal subspace estimation as a function of the number N of ULA elements. Simulations indicate that, in the examples considered, the variance of these estimates decreases only as $N^{1/3}$ which is surprising given that the variance of AoA estimates (at least for one source) decrease as N3. The paper suggests that further study of this empirical result is warranted.

The objective of this research is to measure the effectiveness of a game-based learning approach for cyber safety education for children in elementary school. To date, few cyber safety games have been developed and evaluated for elementary school children. To address this gap in knowledge, we developed a novel cyber safety computer game and conducted a study with elementary school children ranging from age 7 to 10 (n=49). Children were taught concepts of cyber safety through either our plain TEXT, our interactive E-LEARNING website, or our GAME. Knowledge increased significantly more in the group that played the game, indicating that elementary school children can learn about the concepts of cyber safety more effectively through a game-based learning approach compared to traditional text and/or e-Learning based approaches.

Recent experiments on the Mercury pulsed power accelerator were performed with a large-area bremsstrahlung diode operated at 5 MV. Extensive measurements of the x-ray dose distribution were made using TLDs, both in the near field and far field of the x-ray beam. In order to understand the operation of the diode and the properties of the x-ray beam, numerical simulations of the diode and beam were performed. Particle-in-cell simulations of the diode were driven using the measured currents from the experiment in order to simulate the electron beam produced by the accelerator. These beam electrons were then used as a source in Monte Carlo simulations of the bremsstrahlung converter to compute the generated x-ray beam. The photons from this simulation were then transported to the near- and far-field TLD locations and doses were computed. The dose predictions were compared to measurements, showing reasonably good agreement.

The paper proposes a new family of broadband sonar pulses. Each pulse is formed by a combination of orthogonal wavelet basis functions as subpulses with variable scaling factor and shifts. The wavelet adopted is a complex rational orthogonal wavelet (CROW) with a fractional scale dilation factor valued between 1 and 2. The number of subpulses, scaling factor and their time shifts are designed to ensure that the sonar pulse conforms to a designated signal spectrum. The issue of peak-to-average power ratio (PAPR) of the sonar pulse is explored and design example is presented with minimized PAPR. The proposed waveform design features efficient detection algorithm based on CROW analysis filter bank (FB). The detection performance is analyzed regarding Doppler resistance, noise mitigation and multipath by simulations. Preliminary results show that the designed detection system is able to cope with severe multipath, high ambient noise and sever Doppler dispersion.

The smoothing problem is here considered for Gauss-Markov random fields defined on a kind of spherical lattice. Various observation models are included in the setting of this paper, such as the case of Gaussian noisy (even correlated) observations available only on a subset of sites, as well as a variable number of process components being measured. An efficient optimal smoothing algorithm is derived, based on the sparse representation of the potential matrix of the random field and on gaussian elimination. In view of applications in weather forecasting, an example using real data is presented, showing the capability of the proposed setting in a task of reconstruction of temperature maps.

In the past few years, advances have been made on how mixed reality humans (MRHs) can be used for interpersonal communication skills training for medical teams; however, little research has looked at how MRHs can influence communication skills during training. One way to influence communication skills is to leverage MRHs as models of communication behavior. We created a mixed reality medical team training exercise designed to impact communication behaviors that are critical for patient safety. We recruited anesthesia residents to go through an operating room training exercise with MRHs to assess and influence residents' closed loop communication behaviors during medication administration. We manipulated the behavior of the MRHs to determine if the MRHs could influence the residents' closed loop communication behavior. Our results showed that residents' closed loop communications behaviors were influenced by MRHs. Additionally, we found there was a statistically significant difference between groups based on which MRH behavior residents observed. Because the MRHs significantly impacted how residents communicated in simulation, this work expands the boundaries for how VR can be used and demonstrates that MRHs could be used as tools to address complex communication dynamics in a team setting.

Radio access technology (RAT) selection has recently received much attention from the research community due to the increasing deployment of heterogeneous wireless networks. Most prior works mainly focus on proposing an efficient algorithm that yields good performances, and evaluate their solutions on a certain network model, particularly in cases when every user can connect to all the available base stations (BSs). In this paper, we evaluate the impact of different aspects of network models, including (i) network topology and (ii) bandwidth allocation, on the performance of RAT selection algorithms to fully understand their limitations. Using simulations on realistic network models, this paper evaluates how different network parameters such as link density, user density or bandwidth distribution can lead to significant differences in algorithm performance. The paper demonstrates that among all the parameters, the performance of adaptive RAT selection algorithms are most significantly effected by the number of base stations that a user can connect to.

Over the last 10 years we have seen a continual growth in the number of break-ins at government sites as well as commercial businesses. We have seen large data breaches at governmental organizations such as the South Carolina Department of Revenue (DoR) and commercial ventures such as Target. In the case of the South Carolina DoR the credentials of an employee were compromised by an external entity. The intruders then further compromised other parts of the network in order to find potentially profitable data and began the exfiltration of information from within the network. In this paper we will present the concept of a Honey Community that provides a means for alerting local and state communities and large organizations when they are under attack. It also provides a means to analyze the changing state of the attacks on their systems in order to determine ways to improve security.

Real world targets are intelligent and almost always move with a destination in mind. This paper introduces a new target tracking algorithm for multi-camera networks based on a hidden reciprocal chain (HRC), which is able to capture the local dynamics and intention of a real world target in a statistical way. The model is non-causal and therefore fundamentally different to standard Markovian motion models which underpin most trackers, such as the Kalman filter. However it is less computationally expensive than more sophisticated models like Markov decision processes, which can capture complex behaviours but require approximate algorithms for inference. We argue that HRCs are a natural extension to existing Markovian models by presenting exact online inference and detection algorithms which scale well with the number of cameras and targets. Finally we demonstrate the potential benefits by presenting results on synthetic data for the problem of multi-target tracking across multiple cameras.

The Hough transform line detection method has been established as a viable technique for track-before-detect (TBD). However, its basic operation of binning and accumulating votes in the parameter space is computationally expensive. A more critical weakness of Hough transform is its dependence on parameter tuning (e.g., bin size and various thresholds), which can be non-intuitive and data-dependent. This leads to low detection rates in data with low signal-to-noise ratio and significant clutter. In this paper we propose a line detection algorithm with guaranteed global optimality for TBD. Our algorithm is based on the plane sweep algorithm for robust linear regression, with novel modifications to ensure its applicability under the TBD setting. Unlike the Hough transform, our algorithm has only one parameter to set (essentially the sensor false alarm rate) and can deterministically find the best solution according to a well-defined criterion. Simulation results on multi-dimensional TBD problems validate the accuracy and efficiency of our method.

A 20 GHz channel emulator for broadband differential data links has been developed using a trace overlaid with lossy materials and a two stage programmable Finite Impulse Response (FIR) filter. It allows to emulate the main channel and the cross-talk in the data links. The hardware design and emulation procedure are described. The emulated channel matches the target channel, validating the design methodology.

The cybersecurity aspects of critical infrastructure systems have become a hot topic for countries all across the globe. Information Technology has become pervasive in all aspects of our lives and this includes elements referred to as critical infrastructures. This minitrack examines aspects associated with the security of information technology used by governments and critical infrastructures (with an emphasis on automated control systems) and explores ways that IT can enhance the ability of governments to ensure the safety and security of its citizens. The names for these systems vary, from industrial control systems to SCADA to process control networks and more. The systems they control range from electricity (Smartgrid), pipelines, chemical plants, manufacturing, traffic control and more.

The United States has US CYBERCOM to protect the US Military Infrastructure and DHS to protect the nation's critical cyber infrastructure. These organizations deal with wide ranging issues at a national level. This leaves local and state governments to largely fend for themselves in the cyber frontier. This paper will focus on how to determine the threat to a community and what indications and warnings can lead us to suspect an attack is underway. To try and help answer these questions we utilized the concepts of Honey pots and Honey nets and extended them to a multi-organization concept within a geographic boundary to form a Honey Community. The initial phase of the research done in support of this paper was to create a fictitious community with various components to entice would-be attackers and determine if the use of multiple sectors in a community would aid in the determination of an attack.

This research explored the possibility of building channel emulators by utilizing fused deposition modeling (FDM) 3D printing technology, which provides a rapid and low cost method with high flexibility to produce parts with many shapes of interest. Algorithms are developed for obtaining the printing pattern and loss profile based on S parameters of a target channel to be emulated. Parts with different dielectric constants, and loss tangents can be printed using filament with high dielectric constant or conductive fillers. Those parts will be placed on a low loss transmission line. As a result, different channel emulators can be built with the advantage of avoiding complicated electronic components, and only being limited in the frequency range by the base trace and its connectors. Keywords—3D printing; channel emulator; dielectric constants; FDM; transmission line

Long traces or cables usually show a smooth rolloff with increasing frequency leading to Inter-Symbol Interference Jitter. The paper proposes a method for emulating such channel link performance by using a short microstrip trace with added lossy material. The paper focuses on the characterization and selection of lossy materials for emulating long channels using a short low loss trace with added lossy material.

This paper considers the exploitation of temporal correlation in inci- dent sources in a narrowband array processing scenario. The MLE and CRB are derived for parameter estimation of spatially uncorre- lated first order Gaussian autoregressive source signals with additive Gaussian spatially and temporally uncorrelated sensor noise. These are compared to the MLE and CRB for the usual uncorrelated (WN) sources model. The paper deals with the case where the number of data snapshots is small. Numerical simulations show that (i) there is no significant performance gain in the correlated signal case, and sig- nificantly, (ii) the WN MLE performance does degrade in the pres- ence of source correlation, which appears to be in contrast to some recently published work. Other signals, which may possess smaller bandwidths, will thus gen- erally yield correlated samples at baseband. It is therefore natural to ask whether this correlation can be exploited in the design of an esti- mator for all incident signals' AoAs. Studies such as (7) have shown that the MLE designed for independent Gaussian data samples is ro- bust to the presence of temporal correlation in the signals' samples, however there are no detailed studies concerning the performance of the MLE designed specifically for correlated signals. Some results are available concerning the CRB for correlated signals, when the spatio-temporal source correlation matrix is known (5), and these re- sults show that the CRB for correlated sources is lower than that for uncorrelated sources. However for large number of data snapshots, the difference between these CRBs becomes smaller. The threshold- ing behaviour of the correlated sources MLE has not been studied. We are specifically interested in those processing scenarios where computational complexity is not a limiting factor and that compu- tationally intensive techniques such as the correlated sources MLE can be justified if better performance can be obtained. It should be pointed out at this stage, that a signal state-space based approach us- ing the Expectation-Maximisation (EM) algorithm for ML AoA esti- mation of general linear Gauss-Markov sources with known models, has been proposed in (8). This algorithm iteratively applies a fixed- interval Kalman smoother to perform estimation of the signals, and a likelihood based method using the resulting signal estimates to find the AoAs.

Communities are becoming more vulnerable to cyber attacks as their reliance on critical cyber infrastructures is growing. Due to the interdependency among different cyber infrastructures, collaborative information sharing is necessary and important to help a community detect potential risks, prevent cyber attacks at an early stage, and facilitate incident response as well as preparedness activities in communities. In this paper we propose a group-centric collaborative information sharing framework that aims to improve community cyber security. By analyzing specific information sharing requirements in the community, we design a formal policy model for this framework. Our model is illustrated by several information sharing scenarios in the community.

This is a Minitrack introduction for the e-Government minitrack titled Infrastructure Security

We present an LED light source for use with standard clinical endoscopes to enable visualization of tissues labeled with quantum dots (QDs). QD-assisted endoscopy may improve the outcome of surgical endoscopic procedures by identifying specific tissue types. QDs offer several advantages over current fluorescent stains due to their high target selectivity, long-lasting fluorescence, large excitation and narrow emission bands, and multiplexing capabilities. The prototype presented is compact, modular in design, and was built at low cost making it competitive with commercially available light sources. The device's efficiency is evaluated by measuring light intensity at discreet locations and by successfully illuminating a chicken tissue sample non-specifically labeled with a 250nM or 500nM QD solution. Ultimately, this device serves as a step towards incorporating QDs into real time, image-guided surgical procedures.

As the reliance of communities on critical cyber infrastructures is growing, they are becoming more vulnerable to cyber attacks. The Community Cyber Security Maturity Model (CCSMM) was proposed to help communities establish viable and sustainable cyber security programs. Information Sharing is an important part of the CCSMM but there are significant aspects to be explored. Collaborative information sharing helps a community detect potential risks and prevent cyber attacks at an early stage. It also facilitates incident response as well as preparedness activities in communities. In this paper we discuss the necessity of information sharing and provide guidance on the types of information needing to be shared. We define threat alert levels of community cyber security and discuss how different levels affect information sharing. We propose a collaborative information sharing framework that aims to improve community cyber security. Finally, we discuss practical issues and areas for further research.

Communities, and the critical infrastructure that they rely upon, are becoming ever increasingly integrated into cyberspace. At the same time, communities are experiencing increasing activity and sophistication from a variety of threat agents. The effect of cyber attacks on communities has been observed, and the frequency and devastation of these attacks can only increase in the foreseeable future. Early detection of these attacks is critical for a fast and effective response. We propose detecting community cyber incidents by comparing indicators from community members across space and time. Performing spatiotemporal differentiation on these indicators requires that community members, such as private and governmental organizations, share information about these indicators. However, community members are, for good reasons, reluctant to share sensitive security related information. Additionally, sharing large amounts of information with a trusted, centralized location introduces scalability and reliability problems. In this paper we define the information sharing requirements necessary for fast, effective community cyber incident detection and response, while addressing both privacy and scalability concerns. Furthermore, we introduce a framework to meet these requirements, and analyze a proof of concept implementation.

Data stealing botnets pose a great risk to the security of networks and the privacy of their users. Most of these botnets use the web as a medium for communication, making them difficult to detect given that web traffic constitutes about 70% of Internet traffic. In addition, they use obfuscation techniques, primarily encryption, to hide their communications and data exfiltration attempts making current botnet detection techniques that depend on content inspection ineffective. In this paper, we present an analysis of the data stealing behaviors of one of the most notorious data stealing botnets, Zeus. In addition, we propose a classification algorithm to identify malicious data stealing attempts within web traffic. Our classifier uses entropy and byte frequency distribution of HTTP POST request contents as features. Our evaluation of the classifier shows high accuracy and high efficiency making it applicable at network perimeter monitoring devices and web proxies.

EMC test programs are often very lengthy and expensive. It is very important to address a number of critical EMC test issues as early in the program as possible to ensure complete and accurate planning, budgeting and design criteria. This paper presents and discusses a number of typical critical EMC test issues needing early resolution in programs. Suggestions are also provided to help control the potential impact of these issues.

In this paper, we evaluate thresholding algorithms applied in wavelet filter bank (FB) based passive detection in radar and sonar systems. Addressing a random emission with unknown parameters, the emission and the received signal are modelled by a Gaussian process with unknown mean and variance. The wavelet FB decomposes the received signal into subchannels. A decision on the absence and presence of the emission is then made by thresholding against the FB outputs. Formulae are derived to prove the detection performance using different thresholding strategies. A graphical comparison of the detection performances with different strategies and in different scenarios is presented. Design principles are derived under this wavelet FB based detection framework.

The concepts of immersion and presence focus on the environment in a virtual environment. We instead focus on embodied conversational agents (ECAs). ECAs occupy the virtual environment as interactive partners. We propose that the ECA analogues of immersion and presence are physicality and social presence. We performed a study to determine the effect of an ECA's physicality on social presence and eliciting realistic behavior from the user. The results showed that increasing physicality can elicit realistic behavior and increase social presence but there was also an interaction effect with plausibility.

Ten percent of full-scale Orion conical ribbon drogue parachutes in the wake of a 10% command module were tested in a subsonic 3×2.1 m (10′×7′) cross section, subsonic atmospheric wind tunnel. The motivation behind the test program is to provide a cost-efficient means to provide both parachute performance data in the wake of the Command Module (CM), and a comprehensive computational fluid dynamics validation dataset, including velocity field measurement in the wake. The subscale parachutes are constructed with the full-scale geometric porosity, number of gores and ribbons, using a novel laser cutting technique. The wind tunnel allows full exploration of the parameter space, including CM pitch plane angle, dynamic pressure (Reynolds number), and trailing distance.

As governments increasingly move to automating various government functions and as the movement to implement e-government increases, an important element that must be considered is security. Numerous communities have learned what can go wrong if security is not addressed as numerous cyber incidents have been experienced. While dependence on cyber infrastructures increase, nations have increased their efforts to prepare for a national cyber incident. Unfortunately for communities, nations are overlooking the need for local governments to be prepared for incidents. This is not only true for incidents that might impact or target only a single community, but it also includes national incidents that will have an impact on every community. Even if an incident is being addressed by organizations at the national level, cities still need to function and if they are not prepared to respond to a cyber incident they may find that they are not able to properly meet the needs of their citizens while relying on the government at the national level to address the incident. This paper addresses a model that has been developed to help communities develop a viable and sustainable cyber security program. By doing so, this paper describes a model which can help secure a nation by working from the "bottom up" in a grass-roots initiative.

Gregory B. White Wm. Arthur Conklin Rayford B. Vaughn, Jr. Center for Infrastructure Dept of Info & Logistics Tech William L. Giles Distinguished Prof. Assurance and Security College of Technology Associate Vice Pres. Of Research UT-San Antonio University of Houston Dir, Critical Infrastructure Prot. Ctr. greg.white@utsa.edu, waconkli@UH.EDU Mississippi State Univ. MS 39762 vaughn@research.msstate.edu This minitrack examines aspects associated with the security of information and information technology used by governments and critical infrastructures (with an emphasis on automated control systems) and explores ways that IT can enhance the ability of governments to ensure the safety and security of its citizens. This year’s submissions cover a broad spectrum of security topics including the continued operation of critical systems during an emergency, technology that can be used to better protect industrial control systems, a maturity model designed to help states and communities protect their cyber assets, a method to securely share information between identified entities, and examinations of one of the most common form of cyber attack today, phishing. ICT-enabled City Government Field Operations: Resiliency during Extreme Events proposes recommendations for radically re-thinking extreme event response scenarios through data integration, training and effective use of ICTs, planning and preparedness. Extreme events include both natural and man-made emergencies, disasters, and catastrophes of exceptional unthinkable magnitude, which would surpass the capacity of local government to effectively respond. A Retrofit Network Intrusion Detection System for MODBUS RTU and ASCII Industrial Control Systems discusses the need to retrofit serial based industrial control systems to add Snort intrusion detection and intrusion prevention capabilities. A Grassroots Cyber Security Program to Protect the Nation describes a program centered around a maturity model that is being used to help states and communities develop viable and sustainable cyber security programs to protect their critical cyber infrastructures. Seeing the Real World: Sharing Protected Data In Real Time describes a new capability for “owners” of protected data to quickly and securely share real-time data among networked decision-support and real-time control devices with whom the “owners” of the data have explicitly decided to “share” the data. This paper was nominated from this mini-track for consideration of the best paper award. The Influences of Social Networks on Phishing Vulnerability outlines an attempt to reduce the overall success rate of a phishing attack by applying the foundations of social network analysis to identify how social network structures among a military company of future US Army officers can be influential in reducing the spread of a phishing attack. How Could I Fall for That? Exploring Phishing Victimization with the Heuristic-Systematic Model proposes a study on victimization by phishing based on the Heuristic-Systematic Model of information processing. 2012 45th Hawaii International Conference on System Sciences

A broadband passive sonar detector based on rational orthogonal wavelet filter banks (FBs) is proposed. This wavelet-based subband energy detection approach is able to detect an emission with unknown frequency content in severe multipath and noisy ocean environment. A geometrical acoustic channel model is adopted to synthesise the echo from a moving source using ray tracing. The performance of the wavelet FB detector is analysed under designated detection scenarios with a single detector and three different random emissions varying in bandwidth, source motion velocity and direction. Two different multipath scenarios, severe multipath and one-dominant-path scenario, are simulated by modifying the depth of the detector. The receiver operating characteristic (ROC) curves are generated for the wavelet FB detector and are compared with the ROC curves of a conventional energy detector (CED) under the same scenario.

We consider the prediction of a target's destination and simultaneously recover its filtered trajectory. Two novel models for trajectories with known destinations are presented using reciprocal stochastic processes and stochastic context-free grammars. We present a destination-aware syntactic tracker which uses conventional state-space estimates from a legacy tracker to perform prediction and trajectory estimation. We also provide statistical signal processing algorithms for model prediction and maximum likelihood sequence estimation using the proposed trajectory models. Simulation results show that both models considered in the paper have superior estimation performance compared to conventional hidden Markov modeling and can reliably predict the target's destination.

Communities, whose reliance on critical cyber infrastructures is growing, are threatened by a wide range of cyber events that can adversely affect these systems and networks. The development of computer security taxonomies to classify computer and network vulnerabilities and attacks has led to a greater insight into the causes, effects, mitigation, and remediation of cyber attacks. In developing these taxonomies researchers are better able to understand and address the many different attacks that can occur. No current taxonomy, however, has been developed that takes into account the community aspects of cyber attacks or other cyber events affecting communities. We present a new taxonomy that considers the motivation, methodology, and effects of cyber events that can affect communities. We include a discussion on how our taxonomy is useful to e-government, industry, and security researchers.

In this paper, we motivate the need for new models for Secure Information Sharing (SIS) in the specific domain of community cyber security. We believe that similar models will be applicable in numerous other domains. The term community in this context refers to a county or larger city size unit with a clearly demarcated geographical boundary aligned more or less with a governance boundary. Our choice of the community domain is based on the decade long experience of the Center for Infrastructure Assurance and Security (CIAS), now part of the Institute for Cyber Security (ICS-CIAS) at the University of Texas at San Antonio. Over the past decade ICS-CIAS has conducted cyber security preparedness exercises and training at communities throughout the nation specifically dealing with communication, incident response, disaster recovery, business continuity, security awareness and similar issues. We discuss the insights gained from these frequent exercises to illustrate the limitations of prior models for SIS, such as discretionary access control, mandatory access control and role-based access control. Specifically, we argue that these traditional models, while effective in addressing the issues that they were developed for, lack the agility to dynamically configure a system to facilitate SIS scenarios such as monitoring and response during a community cyber security incident life cycle. We discuss how our current research efforts at the Institute for Cyber Security on group-centric SIS models directly address the limitations of existing models in such scenarios.

Government computer systems and networks are under increasing attack from foreign governments, organized crime, and other malicious attackers. Security best practice checklists are commonly used to ensure the protection of government computer systems. However, experimental validation of the recommended security measures is lacking. Additionally, administrators have no way to compare the effectiveness of the many recommended security measures, and they have no way to decide which security measures should be implemented first. We present a model of security measures that describes their overall effectiveness, and their effectiveness relative to how soon the security measure is implemented. Our analysis reveals important principals for implementing security measures and highlights the need for improved user interfaces and administrative tools.

This paper addresses the problem of coded waveform design for Multiple Input - Multiple Output (MIMO) radar systems. The paper proposes a signal model based on the assumption of a small array aperture relative to the signal wavelength and a single target in far field. It is argued that near maximum likelihood performance can be obtained by choosing the transmitted waveforms on different antennas to be orthogonal. The Cramer-Rao Bound (CRB) for estimating the target distance, radial velocity and wavenumber is derived. The paper presents a convex optimisation procedure to yield an optimal energy distribution across the transmitted signals. Simulations demonstrate a reduction in CRB in the order of 25% compared to the use of a random unitary code. The paper concludes by indicating a natural generalisation of the method to optimal design for MIMO tracking radars.

An end-to-end process has been developed to investigate and constrain the designs of multi-GHz serial channels. This serial channel design process consists of three sections: (1) technology investigation, (2) pre-route channel modeling, and (3) post-route system modeling and verification.

It is well-known (Narula et al., 1998) that the performance of a multiple input single output (MISO) communication system can be improved if channel information is used. In this work we exploit both temporal and spacial correlation of the fading channel to design the best precoder in sense of minimum mean-square error. This performance of the precoder is compared using an EM- like decoding, a PSP decoding and a new proposed decoding algorithm.

The Department of Homeland Security has recommended the creation of State, Regional, and Community Fusion Centers. These centers, run by state and local governments, are designed to take what may seem to be disparate pieces of information on a variety of subjects and "fuse " them together to be able to recognize indicators of potential terrorist attacks. These centers are generally staffed with personnel who have intelligence and law enforcement backgrounds. Unfortunately, very few have any concept of the cyber environment and do not know what constitutes indicators of potential cyber attacks. This paper discusses the need to develop a cyber capability in fusion centers and the importance of government involvement in coordinating a state's, community's, or region's cyber defense efforts.

Situation-oriented directories help to determine the stakeholder responsible for a given situation. Defining all responsibility assignments for a directory can be challenging if situations need to be distinguished by many attributes and some of the attributes have many potential values. In this paper we present the stakeholder matrix, a compact representation of a situation-oriented directory that makes it easier for users to ensure the completeness and consistency of the assignments in the directory. Our evaluation showed that in a real world case the number of responsibility assignments could be compacted from 33700 to 128. We also review related research and show that compact representations similar to the stakeholder matrix will be useful to support validation of definitions in expert finding systems, content-based access systems and content-based routing systems.

Network anomaly intrusion detection is designed to provide in-depth defense against zero-day attacks. However, attacks often occur at the application level, which means they are payload associated. Since traditional anomaly detection works by monitoring packet headers it provides little support for defending against such activities. In this paper, we will explore how the packet payload can be used for identifying application level attacks. First we will discuss the current status of network anomaly detection, and emphasize the importance of payload based detection research using existing problems. Then we provide a brief introduction to several related approaches on this topic. Based on the discussion, an efficient method to detect payload related attacks will then be proposed. The method is divided into a training phase and a detection phase. In the training phase, we will perform principal component analysis (PCA) on several important packet fields to reduce the data dimension, and then construct the most appropriate profile based on the PCA results. In the detection phase, an anomaly score will be assigned to each incoming packet based on the profile. We then present the experiment based on the DARPA '99 dataset with details to explain our approach. Comparison with other similar mechanisms demonstrates the advantage of the proposed method at identifying payload related attacks.

Most network anomaly detection research is based on packet header fields, while the payload is usually discarded. Preventing unknown attacks and Internet worms has led to a need for application level network anomaly detection. Payload based detection schemes in experiments are often misleading. In this paper, we discuss the problems associated with the experimental results. In the first section, a brief review would be given for application level anomaly detection research. Introduction to several major payload based approaches would be given in section 2. Then we use the DARPA '99 dataset to evaluate the ALAD mechanism, and discuss the problems by using original DARPA '99 datasets for evaluation. In the fourth section, an improved method would be proposed with a focus on detecting payload related attacks. In section 5, we demonstrate how to justify the payload based detection mechanism using the DARPA '99 dataset, and compare with ALAD to demonstrate its advantages

Governments employ information technology and increasingly e-Government to conduct daily operations and to interface with their citizens. Operations must be conducted securely and with an assurance that information is accurate and protected from unauthorized disclosure to protect the privacy of citizens. Governments must also ensure the safety and security of its citizens, and information technology can aid in this arena. This mini-track examines the security of information technology used by governments and critical infrastructures and explores ways that IT can enhance the ability of governments to ensure the safety and security of its citizens.

Requirements for the LCLS injector drive laser present significant challenges to the design of the system. While progress has been demonstrated in spatial shape, temporal shape,UV generation and rep-rate, a laser that meets all of the LCLS specifications simultaneously has yet to be demonstrated. These challenges are compounded by the stability and reliability requirements. The drive laser and transport system has been installed and tested. We will report on the current operational state of the laser and plans for future improvements.

Reports of cyber security breaches are common in the media and security events have affected millions. Even with a greater awareness of cyber security, the problem has not decreased as sectors increase their dependence on critical cyber infrastructures. States and communities have joined the growing list of organizations trying to establish viable and sustainable cyber security programs to prepare for cyber events when they inevitably occur. The Community Cyber Security Maturity Model (CCSMM)1 was developed to assist in the development of programs by providing three important mechanisms: a “yardstick” to determine current cyber security posture and maturity, a “roadmap” to help improve security posture, and a common point of reference for individuals to share experiences and lessons learned. This paper discusses the development of the model and describes initial results in states and communities adopting it. The paper also discusses what remains to be accomplished and areas for further research.