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Secure channel establishment in disadvantaged networks: optimizing TLS using intercepting proxies

Published in:
MILCOM 2010, IEEE Military Communications Conference , 31 October-3 November 2010.

Summary

Transport Layer Security (TLS) is a secure communication protocol that is used in many secure electronic applications. In order to establish a TLS connection, a client and server engage in a handshake, which usually involves the transmission of digital certificates. In this paper we present a practical speedup of TLS handshakes over bandwidth-constrained, high-latency (i .e. disadvantaged) links by reducing the communication overhead associated with the transmission of digital certificates. This speedup is achieved by deploying two specialized TLS proxies across such links. Working in tandem, one proxy replaces certificate data in packets being sent across the disadvantaged link with a short reference, while the proxy on the other side of the link restores the certificate data in the packet. Local or remote caches supply the certificate data. Our solution preserves the end-to-end security of TLS and is designed to be transparent to third-party applications, and will thus facilitate rapid deployment by removing the need to modify existing installations of TLS clients and TLS servers. Testing shows that this technique can reduce the overall bandwidth used during a handshake by 50% in test emulation and by over 20% of TLS session volume in practice. In addition, it can reduce the time required to establish a secure channel by over 40% across Iridium, a widely used satellite link in practice.
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Summary

Transport Layer Security (TLS) is a secure communication protocol that is used in many secure electronic applications. In order to establish a TLS connection, a client and server engage in a handshake, which usually involves the transmission of digital certificates. In this paper we present a practical speedup of TLS...

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Physical layer considerations for wideband cognitive radio

Published in:
MILCOM 2010, IEEE Military Communications Conference , 31 October-3 November 2010, pp. 2113-2118.

Summary

Next generation cognitive radios will benefit from the capability of transmitting and receiving communications waveforms across many disjoint frequency channels spanning hundreds of megahertz of bandwidth. The information theoretic advantages of multi-channel operation for cognitive radio (CR), however, come at the expense of stringent linearity requirements on the analog transmit and receive hardware. This paper presents the quantitative advantages of multi-channel operation for next generation CR, and the advanced digital compensation algorithms to mitigate transmit and receive nonlinearities that enable broadband multi-channel operation. Laboratory measurements of the improvement in the performance of a multi-channel CR communications system operating below 2 GHz in over 500 MHz of instantaneous bandwidth are presented.
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Summary

Next generation cognitive radios will benefit from the capability of transmitting and receiving communications waveforms across many disjoint frequency channels spanning hundreds of megahertz of bandwidth. The information theoretic advantages of multi-channel operation for cognitive radio (CR), however, come at the expense of stringent linearity requirements on the analog transmit...

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TALENT: dynamic platform heterogeneity for cyber survivability of mission critical applications

Published in:
Proc. Secure and Resilient Cyber Architecture Conf., SRCA, 29 October 2010.

Summary

Despite the significant amount of effort that often goes into securing mission critical systems, many remain vulnerable to advanced, targeted cyber attacks. In this work, we design and implement TALENT (Trusted dynAmic Logical hEterogeNeity sysTem), a framework to live-migrate mission critical applications across heterogeneous platforms. TALENT enables us to change the hardware and operating system on top of which a sensitive application is running, thus providing cyber survivability through platform diversity. Using containers (a.k.a. operating system-level virtualization) and a portable checkpoint compiler, TALENT creates a virtual execution environment and migrates a running application across different platforms while preserving the state of the application. The state, here, refers to the execution state of the process as well as its open files and sockets. TALENT is designed to support a general C application. By changing the platform on-the-fly, TALENT creates a moving target against cyber attacks and significantly raises the bar for a successful attack against a critical application. Our measurements show that a full migration can be completed in about one second.
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Summary

Despite the significant amount of effort that often goes into securing mission critical systems, many remain vulnerable to advanced, targeted cyber attacks. In this work, we design and implement TALENT (Trusted dynAmic Logical hEterogeNeity sysTem), a framework to live-migrate mission critical applications across heterogeneous platforms. TALENT enables us to change...

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Hogs and slackers: using operations balance in a genetic algorithm to optimize sparse algebra computation on distributed architectures

Published in:
Parallel Comput., Vol. 36, No. 10-11, October-November 2010, pp. 635-644.

Summary

We present a framework for optimizing the distributed performance of sparse matrix computations. These computations are optimally parallelized by distributing their operations across processors in a subtly uneven balance. Because the optimal balance point depends on the non-zero patterns in the data, the algorithm, and the underlying hardware architecture, it is difficult to determine. The Hogs and Slackers genetic algorithm (GA) identifies processors with many operations - hogs, and processors with few operations - slackers. Its intelligent operation-balancing mutation operator swaps data blocks between hogs and slackers to explore new balance points. We show that this operator is integral to the performance of the genetic algorithm and use the framework to conduct an architecture study that varies network specifications. The Hogs and Slackers GA is itself a parallel algorithm with near linear speedup on a large computing cluster.
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Summary

We present a framework for optimizing the distributed performance of sparse matrix computations. These computations are optimally parallelized by distributing their operations across processors in a subtly uneven balance. Because the optimal balance point depends on the non-zero patterns in the data, the algorithm, and the underlying hardware architecture, it...

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Graph-embedding for speaker recognition

Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 2742-2745.

Summary

Popular methods for speaker classification perform speaker comparison in a high-dimensional space, however, recent work has shown that most of the speaker variability is captured by a low-dimensional subspace of that space. In this paper we examine whether additional structure in terms of nonlinear manifolds exist within the high-dimensional space. We will use graph embedding as a proxy to the manifold and show the use of the embedding in data visualization and exploration. ISOMAP will be used to explore the existence and dimension of the space. We also examine whether the manifold assumption can help in two classification tasks: data-mining and standard NIST speaker recognition evaluations (SRE). Our results show that the data lives on a manifold and that exploiting this structure can yield significant improvements on the data-mining task. The improvement in preliminary experiments on all trials of the NIST SRE Eval-06 core task are less but significant.
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Summary

Popular methods for speaker classification perform speaker comparison in a high-dimensional space, however, recent work has shown that most of the speaker variability is captured by a low-dimensional subspace of that space. In this paper we examine whether additional structure in terms of nonlinear manifolds exist within the high-dimensional space...

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Simple and efficient speaker comparison using approximate KL divergence

Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 362-365.

Summary

We describe a simple, novel, and efficient system for speaker comparison with two main components. First, the system uses a new approximate KL divergence distance extending earlier GMM parameter vector SVM kernels. The approximate distance incorporates data-dependent mixture weights as well as the standard MAP-adapted GMM mean parameters. Second, the system applies a weighted nuisance projection method for channel compensation. A simple eigenvector method of training is presented. The resulting speaker comparison system is straightforward to implement and is computationally simple? only two low-rank matrix multiplies and an inner product are needed for comparison of two GMM parameter vectors. We demonstrate the approach on a NIST 2008 speaker recognition evaluation task. We provide insight into what methods, parameters, and features are critical for good performance.
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Summary

We describe a simple, novel, and efficient system for speaker comparison with two main components. First, the system uses a new approximate KL divergence distance extending earlier GMM parameter vector SVM kernels. The approximate distance incorporates data-dependent mixture weights as well as the standard MAP-adapted GMM mean parameters. Second, the...

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Multi-pitch estimation by a joint 2-D representation of pitch and pitch dynamics

Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 645-648.

Summary

Multi-pitch estimation of co-channel speech is especially challenging when the underlying pitch tracks are close in pitch value (e.g., when pitch tracks cross). Building on our previous work, we demonstrate the utility of a two-dimensional (2-D) analysis method of speech for this problem by exploiting its joint representation of pitch and pitch-derivative information from distinct speakers. Specifically, we propose a novel multi-pitch estimation method consisting of 1) a data-driven classifier for pitch candidate selection, 2) local pitch and pitch-derivative estimation by k-means clustering, and 3) a Kalman filtering mechanism for pitch tracking and assignment. We evaluate our method on a database of all-voiced speech mixtures and illustrate its capability to estimate pitch tracks in cases where pitch tracks are separate and when they are close in pitch value (e.g., at crossings).
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Summary

Multi-pitch estimation of co-channel speech is especially challenging when the underlying pitch tracks are close in pitch value (e.g., when pitch tracks cross). Building on our previous work, we demonstrate the utility of a two-dimensional (2-D) analysis method of speech for this problem by exploiting its joint representation of pitch...

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Transcript-dependent speaker recognition using mixer 1 and 2

Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 2102-2015.

Summary

Transcript-dependent speaker-recognition experiments are performed with the Mixer 1 and 2 read-transcription corpus using the Lincoln Laboratory speaker recognition system. Our analysis shows how widely speaker-recognition performance can vary on transcript-dependent data compared to conversational data of the same durations, given enrollment data from the same spontaneous conversational speech. A description of the techniques used to deal with the unaudited data in order to create 171 male and 198 female text-dependent experiments from the Mixer 1 and 2 read transcription corpus is given.
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Summary

Transcript-dependent speaker-recognition experiments are performed with the Mixer 1 and 2 read-transcription corpus using the Lincoln Laboratory speaker recognition system. Our analysis shows how widely speaker-recognition performance can vary on transcript-dependent data compared to conversational data of the same durations, given enrollment data from the same spontaneous conversational speech. A...

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Generating client workloads and high-fidelity network traffic for controllable, repeatable experiments in computer security

Published in:
13th Int. Symp. on Recent Advances in Intrusion Detection, 14 September 2010, pp. 218-237.

Summary

Rigorous scientific experimentation in system and network security remains an elusive goal. Recent work has outlined three basic requirements for experiments, namely that hypotheses must be falsifiable, experiments must be controllable, and experiments must be repeatable and reproducible. Despite their simplicity, these goals are difficult to achieve, especially when dealing with client-side threats and defenses, where often user input is required as part of the experiment. In this paper, we present techniques for making experiments involving security and client-side desktop applications like web browsers, PDF readers, or host-based firewalls or intrusion detection systems more controllable and more easily repeatable. First, we present techniques for using statistical models of user behavior to drive real, binary, GUI-enabled application programs in place of a human user. Second, we present techniques based on adaptive replay of application dialog that allow us to quickly and efficiently reproduce reasonable mock-ups of remotely-hosted applications to give the illusion of Internet connectedness on an isolated testbed. We demonstrate the utility of these techniques in an example experiment comparing the system resource consumption of a Windows machine running anti-virus protection versus an unprotected system.
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Summary

Rigorous scientific experimentation in system and network security remains an elusive goal. Recent work has outlined three basic requirements for experiments, namely that hypotheses must be falsifiable, experiments must be controllable, and experiments must be repeatable and reproducible. Despite their simplicity, these goals are difficult to achieve, especially when dealing...

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Machine learning in adversarial environments

Published in:
Mach. Learn., Vol. 81, No. 2, November 2010, pp. 115-119.

Summary

Whenever machine learning is used to prevent illegal or unsanctioned activity and there is an economic incentive, adversaries will attempt to circumvent the protection provided. Constraints on how adversaries can manipulate training and test data for classifiers used to detect suspicious behavior make problems in this area tractable and interesting. This special issue highlights papers that span many disciplines including email spam detection, computer intrusion detection, and detection of web pages deliberately designed to manipulate the priorities of pages returned by modern search engines. The four papers in this special issue provide a standard taxonomy of the types of attacks that can be expected in an adversarial framework, demonstrate how to design classifiers that are robust to deleted or corrupted features, demonstrate the ability of modern polymorphic engines to rewrite malware so it evades detection by current intrusion detection and antivirus systems, and provide approaches to detect web pages designed to manipulate web page scores returned by search engines. We hope that these papers and this special issue encourages the multidisciplinary cooperation required to address many interesting problems in this relatively new area including predicting the future of the arms races created by adversarial learning, developing effective long-term defensive strategies, and creating algorithms that can process the massive amounts of training and test data available for internet-scale problems.
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Summary

Whenever machine learning is used to prevent illegal or unsanctioned activity and there is an economic incentive, adversaries will attempt to circumvent the protection provided. Constraints on how adversaries can manipulate training and test data for classifiers used to detect suspicious behavior make problems in this area tractable and interesting...

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