Efficient IP table lookup via adaptive stratified trees with selective reconstructions

IP address lookup is a critical operation for high bandwidth routers in packet switching networks such as Internet. The lookup is a non-trivial operation since it requires searching for the longest prefix, among those stored in a (large) given table, matching the IP address. Ever increasing routing tables size, traffic volume and links speed demand new and more efficient algorithms. Moreover, the imminent move to IPv6 128-bit addresses will soon require a rethinking of previous technical choices. This article describes a the new data structure for solving the IP table look up problem christened the Adaptive Stratified Tree (AST). The proposed solution is based on casting the problem in geometric terms and on repeated application of efficient local geometric optimization routines. Experiments with this approach have shown that in terms of storage, query time and update time the AST is at a par with state of the art algorithms based on data compression or string manipulations (and often it is better on some of the measured quantities)

Practical Analysis of Encrypted Network Traffic

The growing use of encryption in network communications is an undoubted boon for user privacy. However, the limitations of real-world encryption schemes are still not well understood, and new side-channel attacks against encrypted communications are disclosed every year. Furthermore, encrypted network communications, by preventing inspection of packet contents, represent a significant challenge from a network security perspective: our existing infrastructure relies on such inspection for threat detection. Both problems are exacerbated by the increasing prevalence of encrypted traffic: recent estimates suggest that 65% or more of downstream Internet traffic will be encrypted by the end of 2016. This work addresses these problems by expanding our understanding of the properties and characteristics of encrypted network traffic and exploring new, specialized techniques for the handling of encrypted traffic by network monitoring systems. We first demonstrate that opaque traffic, of which encrypted traffic is a subset, can be identified in real-time and how this ability can be leveraged to improve the capabilities of existing IDS systems. To do so, we evaluate and compare multiple methods for rapid identification of opaque packets, ultimately pinpointing a simple hypothesis test (which can be implemented on an FPGA) as an efficient and effective detector of such traffic. In our experiments, using this technique to “winnow”, or filter, opaque packets from the traffic load presented to an IDS system significantly increased the throughput of the system, allowing the identification of many more potential threats than the same system without winnowing. Second, we show that side channels in encrypted VoIP traffic enable the reconstruction of approximate transcripts of conversations. Our approach leverages techniques from linguistics, machine learning, natural language processing, and machine translation to accomplish this task despite the limited information leaked by such side channels. Our ability to do so underscores both the potential threat to user privacy which such side channels represent and the degree to which this threat has been underestimated. Finally, we propose and demonstrate the effectiveness of a new paradigm for identifying HTTP resources retrieved over encrypted connections. Our experiments demonstrate how the predominant paradigm from prior work fails to accurately represent real-world situations and how our proposed approach offers significant advantages, including the ability to infer partial information, in comparison. We believe these results represent both an enhanced threat to user privacy and an opportunity for network monitors and analysts to improve their own capabilities with respect to encrypted traffic.Doctor of Philosoph

Stable Isotopes in Tree Rings

This Open Access volume highlights how tree ring stable isotopes have been used to address a range of environmental issues from paleoclimatology to forest management, and anthropogenic impacts on forest growth. It will further evaluate weaknesses and strengths of isotope applications in tree rings. In contrast to older tree ring studies, which predominantly applied a pure statistical approach this book will focus on physiological mechanisms that influence isotopic signals and reflect environmental impacts. Focusing on connections between physiological responses and drivers of isotope variation will also clarify why environmental impacts are not linearly reflected in isotope ratios and tree ring widths. This volume will be of interest to any researcher and educator who uses tree rings (and other organic matter proxies) to reconstruct paleoclimate as well as to understand contemporary functional processes and anthropogenic influences on native ecosystems. The use of stable isotopes in biogeochemical studies has expanded greatly in recent years, making this volume a valuable resource to a growing and vibrant community of researchers

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Progress Report No. 20

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1983 to 30 June 1984

Characterizing the evolution and mechanisms of bacterial epitope perception and evasion of the plant immune systems

Both plants and animals are impacted by diverse biotic threats. To limit disease, plants use protein receptors to recognize and respond to pathogen protein epitopes or effectors. Pathogens have evolved strategies to circumvent recognition to proliferate and cause disease. Pathogens can also persist on non-hosts, leading to reservoir populations and subsequent costly outbreaks. Despite considerable resources focused on understanding the interactions between pathogens and model organisms, we lack considerable knowledge in how the natural diversity of bacterial pathogens, particularly Gram-positive actinobacteria, impact plant immune perception, colonization, and disease susceptibility. Using a combination of comparative genomics, genetics, and biochemistry, I leveraged natural genetic variation to understand the evolution of pathogen epitopes and elucidate a driver of pathogen evasion in a Gram-positive actinobacteria. Pathogen recognition and receptor signaling is crucial in host-pathogen interactions, but most studies use a single pathogen epitope and thus, the impact of multi-copy epitopes on pathogen outcomes is unknown. Through comparative genomics of thousands of plant-associated bacterial genomes, I characterized the naturally-evolved bacterial epitope landscape and their impact on pathogen outcomes. I revealed that natural variation was constrained yet experimentally testable and both epitope sequence and copy number variation altered pathogen-immune outcomes. Through genetic and biochemical analyses, I uncovered a mechanism for pathogen immune evasion, intrabacterial antagonism, where a non-immunogenic epitope blocks perception of immunogenic forms encoded in a single genome. One such intrabacterial antagonist, cold shock protein CspB, was conserved in actinobacteria including Clavibacter, a genus comprised of several crop pathogens including tomato, potato, wheat, and corn. As a non-model system, I developed a genetic toolkit to manipulate Clavibacter and test the role of CspB in blocking immune perception of one host species, tomato. While I was able to build and validate the genetic tools through deletion of several critical virulence genes, I was unable to generate a null mutant of the cspB gene in C. michiganensis, likely due to its high GC-content between 73-78%. Instead, I validated our intrabacterial antagonism model though a combination of biochemical assays and genetic transfer of cspB to another foliar pathogen of tomato, Pseudomonas syringae pathovar tomato DC3000. I show via bacterial titers that expression of antagonist cspB blocked perception of other native encoded immunogenic cold shock proteins in a receptor-dependent manner. Collectively, I revealed a mechanism for immune evasion and showcased the importance of analyzing all epitope copies within a genome. I also provided evidence that Gram-positive actinobacteria interface with the plant immune system, a paradigm previously put into question due to insufficient evidence. Finally, I developed a genetic toolkit which may aid in characterizing other genotypic-phenotypic outcomes in the non-model bacterium. While my research has shown that we can leverage natural genetic variation to generate hypotheses and understand their impact on phenotypic outcomes, major questions remain in the evolution, functional biology, and signaling in plant-microbe interactions, which is addressed in the final chapter. Findings from the research questions posed may provide critical insights for subsequent advancements in bioengineering for disease resistance

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition