In most medical facilities, the systems, software, and data that support clinical and business operations have been developed independently and in isolation. This makes it difficult for the systems to efficiently and effectively access the data and metrics needed to improve clinical processes, achieve desired patient outcomes, or make informed business decisions. Furthermore, determining the benefit of current or proposed clinical processes or business practices from these systems is often inefficient and ad hoc, even though the data required is available within these systems. In response to this, we have developed a unified “Packets-to-Patients” approach to measuring key elements of the clinical, business, and technical operations of the medical enterprise. From these elements we derive benefits that are validated as meaningful within those domains. Our approach automatically and efficiently collects data that establishes whether the stages of Electronic Health Record (EHR) Meaningful Use criteria are being met. It also measures EHR adoption rates, clinical benefits like quality of care and patient safety, and business benefits like decreased length of stay and improved workflow efficiency. Our analytical approach has been field-tested and proven through nearly three years of development. This methodology has measured baseline system performance at several medical treatment facilities and also provided those organizations with an in-depth understanding of how health Information Technology (health IT) performance impacts clinical and business operations. More importantly, our approach has enabled rigorous analysis and fact-based decision-making on how best to integrate health IT into clinical operations. Most importantly, this approach allows health care delivery organizations to design a business case based on hard data and increases the likelihood of success in adopting and optimizing health IT.

The popularity of ZigBee devices continues to grow in home automation, transportation, traffic management, and Industrial Control System (ICS) applications given their low-cost and low-power. However, the decentralized architecture of ZigBee ad-hoc networks creates unique security challenges for network intrusion detection and prevention. In the past, ZigBee device authentication reliability was enhanced by Radio Frequency-Distinct Native Attribute (RF-DNA) fingerprinting using a Fisher-based Multiple Discriminant Analysis and Maximum Likelihood (MDA-ML) classification process to distinguish between devices in low Signal-to-Noise Ratio (SNR) environments. However, MDA-ML performance inherently degrades when RF-DNA features do not satisfy Gaussian normality conditions, which often occurs in real-world scenarios where radio frequency (RF) multipath and interference from other devices is present. We introduce non-parametric Random Forest (RndF) and Multi-Class AdaBoost (MCA) ensemble classifiers into the RF-DNA fingerprinting arena, and demonstrate improved ZigBee device authentication. Results are compared with parametric MDA-ML and Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier results using identical input feature sets. Fingerprint dimensional reduction is examined using three methods, namely a pre-classification Kolmogorov-Smirnoff Test (KS-Test), a post-classification RndF feature relevance ranking, and a GRLVQI feature relevance ranking. Using the ensemble methods, an SNR=18.0 dB improvement over MDA-ML processing is realized at an arbitrary correct classification rate (%C) benchmark of %C=90%; for all SNR ∈ [0, 30] dB considered, %C improvement over MDA-ML ranged from 9% to 24%. Relative to GRLVQI processing, ensemble methods again provided improvement for all SNR, with a best improvement of %C=10% achieved at the lowest tested SNR=0.0 dB. Network penetration, measured using rogue ZigBee devices, show that at the SNR=12.0 dB (%C=90%) the ensemble methods correctly reject 31 of 36 rogue access attempts based on Receiver Operating Characteristic (ROC) curve analysis and an arbitrary Rogue Accept Rate of . This performance is better than MDA-ML, and GRLVQI which rejected 25/36, and 28/36 rogue access attempts respectively. The key benefit of ensemble method processing is improved rogue rejection in noisier environments; gains of 6.0 dB, and 18.0 dB are realized over GRLVQI, and MDA-ML, respectively. Collectively considering the demonstrated %C and rogue rejection capability, the use of ensemble methods improves ZigBee network authentication, and enhances anti-spoofing protection afforded by RF-DNA fingerprinting.

Side-channel analysis has been used to successfully attack many cryptographic systems. However, to improve trace quality and make collection of side-channel data easier, the attacker typically modifies the target device to add a trigger signal. This trigger implies a very powerful attacker with virtually complete control over the device. This paper describes a method to collect side-channel data using a software defined radio (SDR) in real-time without requiring a collection device trigger. A correlation-based frequency-dependent leakage mapping technique is introduced to evaluate a 32-bit microprocessor, revealing that individual key bytes leak at different frequencies. Key byte-dependent leakage is observed in both SDR collected and triggered oscilloscope-based collections (which serve to validate the SDR data). This research is the first to demonstrate effective differential attack using SDRs. Successful attacks are presented using two SDRs, including a US$20 digital television receiver with modified drivers.

We propose a generalized framework to evaluate the side-channel information leakage of symmetric block ciphers. The leakage mapping methodology enables the systematic and efficient identification and mitigation of problematic information leakages by exhaustively considering relevant leakage models. The evaluation procedure bounds the anticipated resistance of an implementation to the general class of univariate differential side-channel analysis techniques. Typical applications are demonstrated using the well-known Hamming weight and Hamming distance leakage models, with recommendations for the incorporation of more accurate models. The evaluation results are empirically validated against correlation-based differential side-channel analysis attacks on two typical unprotected implementations of the Advanced Encryption Standard.

Radio-frequency distinct native attribute (RF-DNA) fingerprinting is adapted as a physical-layer technique to improve the security of integrated circuit (IC)-based multifactor authentication systems. Device recognition tasks (both identification and verification) are accomplished by passively monitoring and exploiting the intrinsic features of an IC's unintentional RF emissions without requiring any modification to the device being analyzed. Device discrimination is achieved using RF-DNA fingerprints comprised of higher order statistical features based on instantaneous amplitude, phase, and frequency responses as a device executes a sequence of operations. The recognition system is trained using multiple discriminant analysis to reduce data dimensionality while retaining class separability, and the resultant fingerprints are classified using a linear Bayesian classifier. Demonstrated identification and verification performance includes average identification accuracy of greater than 99.5% and equal error rates of less than 0.05% for 40 near-identical devices. Depending on the level of required classification accuracy, RF-DNA fingerprint-based authentication is well-suited for implementation as a countermeasure to device cloning, and is promising for use in a wide variety of related security problems.

In the boundless digital world and global society of the Internet, anonymity and privacy are becoming increasingly important issues. Many anonymous networking protocols have been proposed and numerous empirical investigations over these networks analyzed; however, no known taxonomies exist for consumers to quickly and easily identify which anonymity properties are offered by the disparate wired and wireless anonymous networks. This paper proposes a novel tree-based taxonomy (TBT) which allows the consumer to choose a desired classical or state-of-the-art anonymity protocol depending upon their specific network environment and anonymity requirements. The two key anonymity properties of unidentifiability and unlinkability and various network types are explored. This paper highlights the expanding definition of anonymity and aids consumer and researcher classification of state-of-the-art technological privacy-preserving anonymous networks.

Detecting network intruders and malicious software is a significant problem for network administrators and security experts. New threats are emerging at an increasing rate, and current signature and statistics-based techniques are failing to keep pace. Intelligent systems that can adapt to new threats are needed to mitigate these new strains of malware as they are released. This research develops a system that uses contextual relationships and information across different layers of abstraction to detect malware based on its qualia, or essence. By looking for the underlying concepts that make a piece of software malicious, this system avoids the pitfalls of static solutions that focus on predefined signatures or anomaly thresholds. This type of qualia-based system provides a framework for developing intelligent classification and decision-making systems for any number of application areas.

With the aim to preserve privacy over a communications network, a plethora of anonymous protocols have been proposed along with many empirical investigations into specific adversary attacks over those networks. However, no known taxonomies exist that address anonymity in the diverse set of both wired and wireless anonymous communications networks. This paper proposes such a novel cubic taxonomy which explores the three key components of anonymity property, adversary capability, and network type. This taxonomy expands the definition of anonymity and aids in the advancement of state-of-the-art technological privacy-preserving mechanisms in anonymous networks against any adversary.

This paper develops a novel communications protocol for autonomous swarms of unmanned aerial vehicles (UAVs) searching a 2-dimensional grid. The search protocol, the UAV search mission protocol (USMP), combines inter-UAV communication with geographic routing to improve search efficiency in terms of total searches, distance traveled by UAVs, and the minimization of UAV direction changes. By determining where search state updates impact search decisions, messages are geographically routed to improve search efficiency. USMP and the geographic greedy perimeter stateless routing (GPSR) protocol are studied via simulation using OPNET modeler 12.0. Geographic routing degrades performance by at least 20% in total searches and distance traveled, but improves direction changes by 6.7%. Overall, USMP improves performance by as much as 188% compared to scenarios without inter-UAV communication.

Abstract This paper investigates the application of a secure group communication architecture to a swarm of autonomous unmanned aerial vehicles (UAVs). A multicast secure group communication architecture for the low earth orbit (LEO) satellite environment is evaluated to determine if it can be effectively adapted to a swarm of UAVs and provide secure, scalable, and efficient communications. The performance of the proposed security architecture is evaluated with two other commonly used architectures using a discrete event computer simulation developed using MATLAB. Performance is evaluated in terms of the scalability and efficiency of the group key distribution and management scheme when the swarm size, swarm mobility, multicast group join and departure rates are varied. The metrics include the total keys distributed over the simulation period, the average number of times an individual UAV must rekey, the average bandwidth used to rekey the swarm, and the average percentage of battery consumed by a UAV to rekey over the simulation period. The proposed security architecture can successfully be applied to a swarm of autonomous UAVs using current technology. The proposed architecture is more efficient and scalable than the other tested and commonly used architectures. Over all the tested configurations, the proposed architecture distributes 55.2–94.8% fewer keys, rekeys 59.0–94.9% less often per UAV, uses 55.2–87.9% less bandwidth to rekey, and reduces the battery consumption by 16.9–85.4%. Published in 2008 by John Wiley & Sons, Ltd.

With the aim to preserve privacy over a communications network, a plethora of anonymous protocols have been proposed along with many empirical investigations into specific adversary attacks over those networks. However, few formal methods have been adequately developed and applied towards anonymous systems with the goal of modeling how an adversary reasons about anonymity. Indeed, many analyses assume a passive, global adversary but fail to provide a rigorous approach to defining and modeling anonymity concepts to ensure information and data assurance as is customary when formally proving other security aspects of a system. Hence, this paper proposes the possibilistic anonymity logical model (PALM) for capturing the knowledge and reasoning ability of an adversary in an anonymous network.

Unauthorized reverse-engineering of programs and algorithms is a major problem for the software industry. Reverse-engineers search for security holes in the program to exploit or try to steal competitors' vital algorithms. To discourage reverse-engineering, developers use a variety of static software protections to obfuscate their programs. Metamorphic software protections add another layer of protection to traditional static obfuscation techniques, forcing reverse-engineers to adjust their attacks as the protection changes. Program fragmentation combines two obfuscation techniques, outlining and obfuscated jump tables, into a new, metamorphic protection. Sections of code are removed from the main program flow and placed throughout memory, reducing the program's locality. These fragments move and are called using obfuscated jump tables, making program execution difficult to follow. This research assesses the performance overhead of a program fragmentation engine and provides analysis of its effectiveness against reverse-engineering techniques. Results show that program fragmentation has low overhead and is an effective technique to complicate disassembly of programs using two common disassembler/debugger tools.

A novel client puzzle protocol, the memoryless puzzle protocol (MPP), is proposed and investigated. The goal is to show that MPP is a viable solution for mitigating distributed denial-of-service (DDoS) attacks in an anonymous routing environment. One such environment, Tor, provides anonymity for interactive Internet services. However, Tor relies on the transport layer security (TLS) protocol, making it vulnerable to distributed denial-of-service (DDoS) attacks. Although client puzzles are often proposed as a solution to denial-of-service attacks, this research is the first to explore TLS DDoS attack mitigation in the Tor anonymous routing environment. Using the MPP, the central processing unit (CPU) utilization and user-data latency measures are analyzed under four increasing DDoS attack intensities and four different puzzle probability distribution levels. For results, typical CPU utilization rates of 80-100% drop to below 70% signifying successful mitigation. Furthermore, even if a client only has a 30% chance of receiving a puzzle or the maximum puzzle strength is used, MPP effectively mitigates attacks. Finally, user-data latency decreases approximately 50% under large-scale attacks. Hence, the MPP is a suitable solution for increasing the robustness and reliability of Tor.

The skip graph, an application-layer data structure for routing and indexing, may be used in a sensor network to facilitate queries of the distributed k-dimensional data collected by the nodes, such as their geographic positions. Nodes in a standard skip graph sort keys in layered groups, where group membership is determined by random membership vectors. We propose a skip graph extension that inverts the key and membership vector roles, wherein group membership is based on deterministic z-ordering of k-dimensional data and sorting within groups is based on random keys. This extension retains the structure of a standard skip graph, and can be modified for use in 3D environments such as unmanned aerial vehicle swarms. Our results indicate this extension reduces the number of nodes contacted relative to the query precision, the volume of the multidimensional space, and the skip graph's height. We discuss our extension's performance relative to metrics at the application and network layers for static and mobile 2D networks.

A skip graph is a resilient application-layer routing structure that supports range queries of distributed k-dimensional data. By sorting deterministic keys into groups based on locally computed random membership vectors, nodes in a standard skip graph can optimize range query performance in mobile networks such as unmanned aerial vehicle swarms. We propose a skip graph extension that inverts the key and membership vector roles and bases group membership on deterministic vectors derived from the z-ordering of k-dimensional data and sorting within groups is based on locally computed random keys.

In the drive to create ever smaller transistors, conventional silicon CMOS devices are becoming more difficult to fabricate reliably as process size shrinks. New technologies are being investigated to replace silicon CMOS. While offering greater numbers of devices per unit area, all of these technologies are more difficult to fabricate, and more likely to fail in operation than current technologies. Nanotechnology research has identified the need for fault and defect tolerance at the architectural level so that future devices can be used in large-scale electronics circuits. This paper examines the problem of creating reliable caches using extremely unreliable technologies. We incorporate support logic (i.e., control, datapath, and self-test logic) into the analysis, and propose a novel Content Addressable Memory-based design incorporating "best practice" fault tolerant design techniques. The design requires 15 times the number of devices of a conventional design, but enables the use of device technologies with defect rates higher than 10-6, a three order of magnitude improvement over non-fault tolerant designs

The Bluetooth discovery process requires use of the inquiry substate which not only consumes significant power, but prevents normal data traffic flow and simultaneously acts as a potential noise source for neighboring networks. Therefore, the inquiry substate dwell time should be limited to discovering an acceptable number of neighboring devices. Although an estimate of the probability density function of the time to discover scanning devices is straightforward, the complex temporal and spectral interactions between two devices make precise inquiry time characterization difficult. We characterize these interactions and derive detailed analytical expressions for the probability distribution of the inquiry time for a Bluetooth-enabled device that follows v1.1 of the Bluetooth specification and uses the default 11.25 ms inquiry scan window that opens every 1.28 s. Subsequently, we show a single inquirer will locate 99 percent of all scanning devices within transmission range in 5.12 seconds rather than the 10.24 s recommended in the specification. Using specification v1.2, we show that the inquiry time can be reduced to 3.84 seconds and 1.28 seconds using the standard and interlaced inquiry scan modes, respectively. Substantial inquiry time reduction results in reduced power requirements and increased throughput by increasing data traffic and reducing interference with neighboring piconets. Our results are validated via comparison with existing simulation models and measurement studies. The models used to derive the distribution also lend themselves to characterizing the discovery time with variations in the discovery process.

This research focuses on performance of two ultra wide band (UWB) techniques for implementing multiple access (MA) communications. Specifically, a Gaussian monocycle with time hopping pulse position modulation (TH-PPM) and direct sequence binary phase shift keying (DS-BPSK) is considered. Previous research on UWB system performance using Gold spreading sequences forms the basis for this work. The knowledge base of UWB MA performance characterization is expanded here using codes generated from a random integer selection process and a simulated annealing code generation process. Communication performance is first validated for a single user operating over an AWGN channel and subsequently extended to incorporate multiusers, TH-PPM with Gold coding provides an average MA BER improvement factor of 2.0 /spl times/ 10/sup -2/ and 5.1 /spl times/ 10/sup -2/ over random integer and simulated annealing codes, respectively. Likewise, DS-BPSK with Gold coding provides an average MA BER improvement factor of 3.8/spl times/ 10/sup -4/ and 9.8 /spl times/ 10/sup -4/ over random integer and simulated annealing codes, respectively.

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.