Publications
Information security for situational awareness in computer network defense
January 1, 2011
Book Chapter
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Chapter Six, Situational Awareness in Computer Network Defense: Principles, Methods, and Applications, 2011, pp. 86-103.
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Summary
Situational awareness - the perception of "what's going on" - is crucial in every field of human endeavor, especially so in the cyber world where most of the protections afforded by physical time and distance are taken away. Since ancient times, military science emphasized the importance of preserving your awareness of the battlefield and at the same time preventing your adversary from learning the true situation for as long as possible. Today cyber is officially recognized as a contested military domain like air, land, and sea. Therefore situational awareness in computer networks will be under attacks of military strength and will require military-grade protection. This chapter describes the emerging threats for computer SA, and the potential avenues of defense against them.
Summary
Situational awareness - the perception of "what's going on" - is crucial in every field of human endeavor, especially so in the cyber world where most of the protections afforded by physical time and distance are taken away. Since ancient times, military science emphasized the importance of preserving your awareness...
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Modeling and detection techniques for counter-terror social network analysis and intent recognition
March 7, 2009
Conference Paper
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2009 IEEE Aerospace Conf., March 7-14 2009.
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In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less progress has been made in the development of automated tools for analyzing the results of information extraction to ?connect the dots.? Hence, our Counter-Terror Social Network Analysis and Intent Recognition (CT-SNAIR) work focuses on development of automated techniques and tools for detection and tracking of dynamically-changing terrorist networks as well as recognition of capability and potential intent. In addition to obtaining and working with real data for algorithm development and test, we have a major focus on modeling and simulation of terrorist attacks based on real information about past attacks. We describe the development and application of a new Terror Attack Description Language (TADL), which is used as a basis for modeling and simulation of terrorist attacks. Examples are shown which illustrate the use of TADL and a companion simulator based on a Hidden Markov Model (HMM) structure to generate transactions for attack scenarios drawn from real events. We also describe our techniques for generating realistic background clutter traffic to enable experiments to estimate performance in the presence of a mix of data. An important part of our effort is to produce scenarios and corpora for use in our own research, which can be shared with a community of researchers in this area. We describe our scenario and corpus development, including specific examples from the September 2004 bombing of the Australian embassy in Jakarta and a fictitious scenario which was developed in a prior project for research in social network analysis. The scenarios can be created by subject matter experts using a graphical editing tool. Given a set of time ordered transactions between actors, we employ social network analysis (SNA) algorithms as a filtering step to divide the actors into distinct communities before determining intent. This helps reduce clutter and enhances the ability to determine activities within a specific group. For modeling and simulation purposes, we generate random networks with structures and properties similar to real-world social networks. Modeling of background traffic is an important step in generating classifiers that can separate harmless activities from suspicious activity. An algorithm for recognition of simulated potential attack scenarios in clutter based on Support Vector Machine (SVM) techniques is presented. We show performance examples, including probability of detection versus probability of false alarm tradeoffs, for a range of system parameters.
Summary
In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less...
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SARA: Survivable Autonomic Response Architecture
June 12, 2001
Conference Paper
Published in:
DARPA Information Survivability Conf. and Exposition II, 12-14 June 2001, pp. 77-88.
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This paper describes the architecture of a system being developed to defend information systems using coordinated autonomic responses. The system will also be used to test the hypothesis that an effective defense against fast, distributed information attacks requires rapid, coordinated, network-wide responses. The core components of the architecture are a run-time infrastructure (RTI), a communication language, a system model, and defensive components. The RTI incorporates a number of innovative design concepts and provides fast, reliable, exploitation-resistant communication and coordination services to the components defending the network, even when challenged by a distributed attack. The architecture can be tailored to provide scalable information assurance defenses for large, geographically distributed, heterogeneous networks with multiple domains, each of which uses different technologies and requires different policies. The architecture can form the basis of a field-deployable system. An initial version is being developed for evaluation in a testbed that will be used to test the autonomic coordination and response hypothesis.
Summary
This paper describes the architecture of a system being developed to defend information systems using coordinated autonomic responses. The system will also be used to test the hypothesis that an effective defense against fast, distributed information attacks requires rapid, coordinated, network-wide responses. The core components of the architecture are a...
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Estimation of handset nonlinearity with application to speaker recognition
September 1, 2000
Journal Article
Published in:
IEEE Trans. Speech Audio Process., Vol. 8, No. 5, September 2000, pp. 567-584.
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A method is described for estimating telephone handset nonlinearity by matching the spectral magnitude of the distorted signal to the output of a nonlinear channel model, driven by an undistorted reference. This "magnitude-only" representation allows the model to directly match unwanted speech formants that arise over nonlinear channels and that are a potential source of degradation in speaker and speech recognition algorithms. As such, the method is particularly suited to algorithms that use only spectral magnitude information. The distortion model consists of a memoryless nonlinearity sandwiched between two finite-length linear filters. Nonlinearities considered include arbitrary finite-order polynomials and parametric sigmoidal functionals derived from a carbon-button handset model. Minimization of a mean-squared spectral magnitude distance with respect to model parameters relies on iterative estimation via a gradient descent technique. Initial work has demonstrated the importance of addressing handset nonlinearity, in addition to linear distortion, in speaker recognition over telephone channels. A nonlinear handset "mapping" applied to training or testing data to reduce mismatch between different types of handset microphone outputs, improves speaker verification performance relative to linear compensation only. Finally, a method is proposed to merge the mapper strategy with a method of likelihood score normalization (hnorm) for further mismatch reduction and speaker verification performance improvement.
Summary
A method is described for estimating telephone handset nonlinearity by matching the spectral magnitude of the distorted signal to the output of a nonlinear channel model, driven by an undistorted reference. This "magnitude-only" representation allows the model to directly match unwanted speech formants that arise over nonlinear channels and that...
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Automatic speaker clustering from multi-speaker utterances
March 15, 1999
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. II, 15-19 March 1999, pp. 817-820.
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Blind clustering of multi-person utterances by speaker is complicated by the fact that each utterance has at least two talkers. In the case of a two-person conversation, one can simply split each conversation into its respective speaker halves, but this introduces error which ultimately hurts clustering. We propose a clustering algorithm which is capable of associating each conversation with two clusters (and therefore two-speakers) obviating the need for splitting. Results are given for two speaker conversations culled from the Switchboard corpus, and comparisons are made to results obtained on single-speaker utterances. We conclude that although the approach is promising, our technique for computing inter-conversation similarities prior to clustering needs improvement.
Summary
Blind clustering of multi-person utterances by speaker is complicated by the fact that each utterance has at least two talkers. In the case of a two-person conversation, one can simply split each conversation into its respective speaker halves, but this introduces error which ultimately hurts clustering. We propose a clustering...
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Blind clustering of speech utterances based on speaker and language characteristics
November 30, 1998
Conference Paper
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5th Int. Conf. Spoken Language Processing (ICSLP), 30 November - 4 December 1998.
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Classical speaker and language recognition techniques can be applied to the classification of unknown utterances by computing the likelihoods of the utterances given a set of well trained target models. This paper addresses the problem of grouping unknown utterances when no information is available regarding the speaker or language classes or even the total number of classes. Approaches to blind message clustering are presented based on conventional hierarchical clustering techniques and an integrated cluster generation and selection method called the d* algorithm. Results are presented using message sets derived from the Switchboard and Callfriend corpora. Potential applications include automatic indexing of recorded speech corpora by speaker/language tags and automatic or semiautomatic selection of speaker specific speech utterances for speaker recognition adaptation.
Summary
Classical speaker and language recognition techniques can be applied to the classification of unknown utterances by computing the likelihoods of the utterances given a set of well trained target models. This paper addresses the problem of grouping unknown utterances when no information is available regarding the speaker or language classes...
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Magnitude-only estimation of handset nonlinearity with application to speaker recognition
May 15, 1998
Conference Paper
Published in:
Proc. of the 1998 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. II, Speech Processing II; Neural Networks for Signal Processing, 12-15 May 1998, pp. 745-748.
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Summary
A method is described for estimating telephone handset nonlinearity by matching the spectral magnitude of the distorted signal to the output of a nonlinear channel model, driven by an undistorted reference. The "magnitude-only" representation allows the model to directly match unwanted speech formants that arise over nonlinear channels and that are a potential source of degradation in speaker and speech recognition algorithms. As such, the method is particularly suited to algorithms that use only spectral magnitude information. The distortion model consists of a memoryless polynomial nonlinearity sandwiched between two finite-length linear filters. Minimization of a mean-squared spectral magnitude error, with respect to model parameters, relies on iterative estimation via a gradient descent technique, using a Jacobian in the iterative correction term with gradients calculated by finite-element approximation. Initial work has demonstrated the algorithm's usefulness in speaker recognition over telephone channels by reducing mismatch between high- and low-quality handset conditions.
Summary
A method is described for estimating telephone handset nonlinearity by matching the spectral magnitude of the distorted signal to the output of a nonlinear channel model, driven by an undistorted reference. The "magnitude-only" representation allows the model to directly match unwanted speech formants that arise over nonlinear channels and that...
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AM-FM separation using auditory-motivated filters
January 9, 1997
Journal Article
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IEEE Trans. Speech Audio Process., Vol. 5, No. 5, September 1997, pp. 465-480.
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An approach to the joint estimation of sine-wave amplitude modulation (AM) and frequency modulation (FM) is described based on the transduction of frequency modulation into amplitude modulation by linear filters, being motivated by the hypothesis that the auditory system uses a similar transduction mechanism in measuring sine-wave FM. An AM-FM estimation is described that uses the amplitude envelope of the output of two transduction filters of piecewise-linear spectral shape. The piecewise-linear constraint is then relaxed, allowing a wider class of transduction-filter pairs for AM-FM separation under a monotonicity constraint of the filters' quotient. The particular case of Gaussian filters, and measured auditory filters, although not leading to a solution in closed form, provide for iterative AM-FM estimation. Solution stability analysis and error evaluation are performed and the FM transduction method is compared with the energy separation algorithm, based on the Teager energy operator, and the Hilbert transform method for AM-FM estimation. Finally, a generalization to two-dimensional (2-D) filters is described.
Summary
An approach to the joint estimation of sine-wave amplitude modulation (AM) and frequency modulation (FM) is described based on the transduction of frequency modulation into amplitude modulation by linear filters, being motivated by the hypothesis that the auditory system uses a similar transduction mechanism in measuring sine-wave FM. An AM-FM...
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The effects of telephone transmission degradations on speaker recognition performance
May 9, 1995
Conference Paper
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Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, Speech, 9-12 May 1995, pp. 329-332.
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The two largest factors affecting automatic speaker identification performance are the size of the population an the degradations introduced by noisy communication, channels (e.g., telephone transmission). To examine experimentally these two factors, this paper presents text-independent speaker identification results for varying speaker population sizes up to 630 speakers for both clean, wideband speech and telephone speech. A system based on Gaussian mixture speaker identification and experiments are conducted on the TIMIT and NTIMIT databases. This is believed to be the first speaker identification experiments on the complete 630 speaker TIMIT and NTIMIT databases and the largest text-independent speaker identification task reported to date. Identification accuracies of 99.5% and 60.7% are achieved on the TIMIT and NTIMIT databases, respectively. This paper also presents experiments which examine and attempt to quantify the performance loss associated with various telephone degradations by systematically degrading the TIMIT speech in a manner consistent with measured NTIMIT degradations and measuring the performance loss at each step. It is found that the standard degradations of filtering and additive noise do not account for all of the performance gap between the TIMIT and NTIMIT data. Measurements of nonlinear microphone distortions are also...
Summary
The two largest factors affecting automatic speaker identification performance are the size of the population an the degradations introduced by noisy communication, channels (e.g., telephone transmission). To examine experimentally these two factors, this paper presents text-independent speaker identification results for varying speaker population sizes up to 630 speakers for both...
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Far-echo cancellation in the presence of frequency offset (full duplex modem)
June 1, 1989
Journal Article
Published in:
IEEE Trans. Commun., Vol. 37, No. 6, June 1989, pp. 635-644.
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In this paper, we present a design for a full-duplex echo-cancelling data modem based on a combined adaptive reference algorithm and adaptive channel equalizer. The adaptive reference algorithm has the advantage that interference to the echo canceller caused by the far-end signal can be eliminated by subtracting an estimate of the far-end signal based on receiver decisions. This technique provides a new approach for full-duplex far-echo cancellation in which the far echo can be cancelled in spite of carrier frequency offset. To estimate the frequency offset, the system uses a separate receiver structure for the far echo which provides equalization of the far-echo channel and tracks the frequency offset in the far echo. The feasibility of the echo-cancelling algorithms is demonstrated by computer simulation with realistic channel distortions and with 4800 bits/s data transmission at which rate frequency offset in the far echo becomes important.
Summary
In this paper, we present a design for a full-duplex echo-cancelling data modem based on a combined adaptive reference algorithm and adaptive channel equalizer. The adaptive reference algorithm has the advantage that interference to the echo canceller caused by the far-end signal can be eliminated by subtracting an estimate of...
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