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Dynamic buffer overflow detection

June 10, 2005
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Conference Paper
Author:
Michael A. Zhivich
…
Published in:
Workshop on the Evaluation of Software Defect Detection Tools, 10 June 2005.
Topic:
cyber security
R&D area:
R&D group:

Summary

The capabilities of seven dynamic buffer overflow detection tools (Chaperon, Valgrind, CCured, CRED, Insure++, ProPolice and TinyCC) are evaluated in this paper. These tools employ different approaches to runtime buffer overflow detection and range from commercial products to open source gcc-enhancements. A comprehensive test suite was developed consisting of specifically-designed test cases and model programs containing real-world vulnerabilities. Insure++, CCured and CRED provide the highest buffer overflow detection rates, but only CRED provides an open-source, extensible and scalable solution to detecting buffer overflows. Other tools did not detect off-by-one errors, did not scale to large programs, or performed poorly on complex programs.
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Summary

The capabilities of seven dynamic buffer overflow detection tools (Chaperon, Valgrind, CCured, CRED, Insure++, ProPolice and TinyCC) are evaluated in this paper. These tools employ different approaches to runtime buffer overflow detection and range from commercial products to open source gcc-enhancements. A comprehensive test suite was developed consisting of specifically-designed...

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Dynamic buffer overflow detection

Application of a development time productivity metric to parallel software development

May 15, 2005
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Conference Paper
Author:
Andrew P. Funk
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Published in:
SE-HPCS '05, 2nd Int. Worskhop on Software Engineering for High Performance Computing System Applications, 15 May 2005, pp. 8-12.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Evaluation of High Performance Computing (HPC) systems should take into account software development time productivity in addition to hardware performance, cost, and other factors. We propose a new metric for HPC software development time productivity, defined as the ratio of relative runtime performance to relative programmer effort. This formula has been used to analyze several HPC benchmark codes and classroom programming assignments. The results of this analysis show consistent trends for various programming models. This method enables a high-level evaluation of development time productivity for a given code implementation, which is essential to the task of estimating cost associated with HPC software development.
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Summary

Evaluation of High Performance Computing (HPC) systems should take into account software development time productivity in addition to hardware performance, cost, and other factors. We propose a new metric for HPC software development time productivity, defined as the ratio of relative runtime performance to relative programmer effort. This formula has...

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Application of a development time productivity metric to parallel software development

Measuring translation quality by testing English speakers with a new Defense Language Proficiency Test for Arabic

May 2, 2005
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Conference Paper
Author:
Douglas A. Jones
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Published in:
Int. Conf. on Intelligence Analysis, 2-5 May 2005.
Topic:
machine translation
R&D area:
R&D group:

Summary

We present results from an experiment in which educated English-native speakers answered questions from a machine translated version of a standardized Arabic language test. We compare the machine translation (MT) results with professional reference translations as a baseline for the purpose of determining the level of Arabic reading comprehension that current machine translation technology enables an English speaker to achieve. Furthermore, we explore the relationship between the current, broadly accepted automatic measures of performance for machine translation and the Defense Language Proficiency Test, a broadly accepted measure of effectiveness for evaluating foreign language proficiency. In doing so, we intend to help translate MT system performance into terms that are meaningful for satisfying Government foreign language processing requirements. The results of this experiment suggest that machine translation may enable Interagency Language Roundtable Level 2 performance, but is not yet adequate to achieve ILR Level 3. Our results are based on 69 human subjects reading 68 documents and answering 173 questions, giving a total of 4,692 timed document trials and 7,950 question trials. We propose Level 3 as a reasonable nearterm target for machine translation research and development.
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Summary

We present results from an experiment in which educated English-native speakers answered questions from a machine translated version of a standardized Arabic language test. We compare the machine translation (MT) results with professional reference translations as a baseline for the purpose of determining the level of Arabic reading comprehension that...

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Measuring translation quality by testing English speakers with a new Defense Language Proficiency Test for Arabic

Using leader-based communication to improve the scalability of single-round group membership algorithms

April 4, 2005
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Conference Paper
Author:
Roger I. Khazan
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Published in:
IPDPS 2005: 19th Int. Parallel and Distributed Processing Symp., 4-8 April 2005, pp. 280-287.
Topic:
algorithms
R&D area:
R&D group:

Summary

Sigma, the first single-round group membership (GM) algorithm, was recently introduced and demonstrated to operate consistently with theoretical expectations in a simulated WAN environment. Sigma achieved similar quality of membership configurations as existing algorithms but required fewer message exchange rounds. We now consider Sigma in terms of scalability. Sigma involves all-to-all (A2A) type of communication among members. A2A protocols have been shown to perform worse than leader-based (LB) protocols in certain networks, due to greater message overhead and higher likelihood of message loss. Thus, although LB protocols often involve additional communication steps, they can be more efficient in practice, particularly in fault-prone networks with large numbers of participating nodes. In this paper, we present Leader-Based Sigma, which transforms the original all-to-all version into a more scalable centralized communication scheme, and discuss the rounds vs. messages tradeoff involved in optimizing GM algorithms for deployment in large-scale, fault-prone dynamic network environments.
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Summary

Sigma, the first single-round group membership (GM) algorithm, was recently introduced and demonstrated to operate consistently with theoretical expectations in a simulated WAN environment. Sigma achieved similar quality of membership configurations as existing algorithms but required fewer message exchange rounds. We now consider Sigma in terms of scalability. Sigma involves...

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Using leader-based communication to improve the scalability of single-round group membership algorithms

An annotated review of past papers on attack graphs

March 31, 2005
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Project Report
Author:
Richard P. Lippmann
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Published in:
MIT Lincoln Laboratory Report IA-1
Topic:
attack graphs
R&D area:
R&D group:

Summary

This report reviews past research papers that describe how to construct attack graphs, how to use them to improve security of computer networks, and how to use them to analyze alerts from intrusion detection systems. Two commercial systems are described [I, 2], and a summary table compares important characteristics of past research studies. For each study, information is provided on the number of attacker goals, how graphs are constructed, sizes of networks analyzed, how well the approach scales to larger networks, and the general approach. Although research has made significant progress in the past few years, no system has analyzed networks with more than 20 hosts, and computation for most approaches scales poorly and would be impractical for networks with more than even a few hundred hosts. Current approaches also are limited because many require extensive and difficult-to-obtain details on attacks, many assume that host-to-host reachability information between all hosts is already available, and many produce an attack graph but do not automatically generate recommendations from that graph. Researchers have suggested promising approaches to alleviate some of these limitations, including grouping hosts to improve scaling, using worst-case default values for unknown attack details, and symbolically analyzing attack graphs to generate recommendations that improve security for critical hosts. Future research should explore these and other approaches to develop attack graph construction and analysis algorithms that can be applied to large enterprise networks.
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Summary

This report reviews past research papers that describe how to construct attack graphs, how to use them to improve security of computer networks, and how to use them to analyze alerts from intrusion detection systems. Two commercial systems are described [I, 2], and a summary table compares important characteristics of...

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An annotated review of past papers on attack graphs

Speaker adaptive cohort selection for Tnorm in text-independent speaker verification

March 19, 2005
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Conference Paper
Author:
Douglas E. Sturim
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Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, 19-23 March 2005, pp. I-741 - I-744.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

In this paper we discuss an extension to the widely used score normalization technique of test normalization (Tnorm) for text-independent speaker verification. A new method of speaker Adaptive-Tnorm that offers advantages over the standard Tnorm by adjusting the speaker set to the target model is presented. Examples of this improvement using the 2004 NIST SRE data are also presented.
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Summary

In this paper we discuss an extension to the widely used score normalization technique of test normalization (Tnorm) for text-independent speaker verification. A new method of speaker Adaptive-Tnorm that offers advantages over the standard Tnorm by adjusting the speaker set to the target model is presented. Examples of this improvement...

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Speaker adaptive cohort selection for Tnorm in text-independent speaker verification

Measuring human readability of machine generated text: three case studies in speech recognition and machine translation

March 19, 2005
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Conference Paper
Author:
Douglas A. Jones
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Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vol. 5, ICASSP, 19-23 March 2005, pp. V-1009 - V-1012.
Topic:
machine translation
R&D area:
R&D group:

Summary

We present highlights from three experiments that test the readability of current state-of-the art system output from (1) an automated English speech-to-text system (2) a text-based Arabic-to-English machine translation system and (3) an audio-based Arabic-to-English MT process. We measure readability in terms of reaction time and passage comprehension in each case, applying standard psycholinguistic testing procedures and a modified version of the standard Defense Language Proficiency Test for Arabic called the DLPT*. We learned that: (1) subjects are slowed down about 25% when reading system STT output, (2) text-based MT systems enable an English speaker to pass Arabic Level 2 on the DLPT* and (3) audio-based MT systems do not enable English speakers to pass Arabic Level 2. We intend for these generic measures of readability to predict performance of more application-specific tasks.
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Summary

We present highlights from three experiments that test the readability of current state-of-the art system output from (1) an automated English speech-to-text system (2) a text-based Arabic-to-English machine translation system and (3) an audio-based Arabic-to-English MT process. We measure readability in terms of reaction time and passage comprehension in each...

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Measuring human readability of machine generated text: three case studies in speech recognition and machine translation

The 2004 MIT Lincoln Laboratory speaker recognition system

March 19, 2005
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Conference Paper
Author:
Douglas A. Reynolds
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Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 1, 19-23 March 2005, pp. I-177 - I-180.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The MIT Lincoln Laboratory submission for the 2004 NIST Speaker Recognition Evaluation (SRE) was built upon seven core systems using speaker information from short-term acoustics, pitch and duration prosodic behavior, and phoneme and word usage. These different levels of information were modeled and classified using Gaussian Mixture Models, Support Vector Machines and N-gram language models and were combined using a single layer perception fuser. The 2004 SRE used a new multi-lingual, multi-channel speech corpus that provided a challenging speaker detection task for the above systems. In this paper we describe the core systems used and provide an overview of their performance on the 2004 SRE detection tasks.
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Summary

The MIT Lincoln Laboratory submission for the 2004 NIST Speaker Recognition Evaluation (SRE) was built upon seven core systems using speaker information from short-term acoustics, pitch and duration prosodic behavior, and phoneme and word usage. These different levels of information were modeled and classified using Gaussian Mixture Models, Support Vector...

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The 2004 MIT Lincoln Laboratory speaker recognition system

Evaluating static analysis tools for detecting buffer overflows in C code

March 1, 2005
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Thesis
Author:
Kendra J. Kratkiewicz
Published in:
Thesis (MLA)--Harvard University, 2005.
Topic:
software
R&D area:
R&D group:

Summary

This project evaluated five static analysis tools using a diagnostic test suite to determine their strengths and weaknesses in detecting a variety of buffer overflow flaws in C code. Detection, false alarm, and confusion rates were measured, along with execution time. PolySpace demonstrated a superior detection rate on the basic test suite, missing only one out of a possible 291 detections. It may benefit from improving its treatment of signal handlers, and reducing both its false alarm rate (particularly for C library functions) and execution time. ARCHER performed quite well with no false alarms whatsoever; a few key enhancements, such as in its inter-procedural analysis and handling of C library functions, would boost its detection rate and should improve its performance on real-world code. Splint detected significantly fewer overflows and exhibited the highest false alarm rate. Improvements in its loop handling, and reductions in its false alarm rate would make it a much more useful tool. UNO had no false alarms, but missed a broad variety of overflows amounting to nearly half of the possible detections in the test suite. It would need improvement in many areas to become a very useful tool. BOON was clearly at the back of the pack, not even performing well on the subset of test cases where it could have been expected to function. The project also provides a buffer overflow taxonomy, along with a test suite generator and other tools, that can be used by others to evaluate code analysis tools with respect to buffer overflow detection.
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Summary

This project evaluated five static analysis tools using a diagnostic test suite to determine their strengths and weaknesses in detecting a variety of buffer overflow flaws in C code. Detection, false alarm, and confusion rates were measured, along with execution time. PolySpace demonstrated a superior detection rate on the basic...

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Evaluating static analysis tools for detecting buffer overflows in C code

Advances in channel compensation for SVM speaker recognition

January 1, 2005
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Conference Paper
Author:
Alex Solomonoff
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Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, Vol. 1, 19-23 March 2005, pp. I-629 - I-631.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Cross-channel degradation is one of the significant challenges facing speaker recognition systems. We study the problem for speaker recognition using support vector machines (SVMs). We perform channel compensation in SVM modeling by removing non-speaker nuisance dimensions in the SVM expansion space via projections. Training to remove these dimensions is accomplished via an eigenvalue problem. The eigenvalue problem attempts to reduce multisession variation for the same speaker, reduce different channel effects, and increase "distance" between different speakers. We apply our methods to a subset of the Switchboard 2 corpus. Experiments show dramatic improvement in performance for the cross-channel case.
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Summary

Cross-channel degradation is one of the significant challenges facing speaker recognition systems. We study the problem for speaker recognition using support vector machines (SVMs). We perform channel compensation in SVM modeling by removing non-speaker nuisance dimensions in the SVM expansion space via projections. Training to remove these dimensions is accomplished...

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Advances in channel compensation for SVM speaker recognition
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