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Interactive synthesis of code-level security rules

January 1, 2017
  |
Thesis
Author:
Leo St. Amour
Published in:
Thesis (M.S.)--Northeastern University, 2017.
Topic:
cyber security
R&D area:
R&D group:

Summary

Software engineers inadvertently introduce bugs into software during the development process and these bugs can potentially be exploited once the software is deployed. As the size and complexity of software systems increase, it is important that we are able to verify and validate not only that the software behaves as it is expected to, but also that it does not violate any security policies or properties. One of the approaches to reduce software vulnerabilities is to use a bug detection tool during the development process. Many bug detection techniques are limited by the burdensome and error prone process of manually writing a bug specification. Other techniques are able to learn specifications from examples, but are limited in the types of bugs that they are able to discover. This work presents a novel, general approach for synthesizing security rules for C code. The approach combines human knowledge with an interactive logic programming synthesis system to learn Datalog rules for various security properties. The approach has been successfully used to synthesize rules for three intraprocedural security properties: (1) out of bounds array accesses, (2) return value validation, and (3) double freed pointers. These rules have been evaluated on randomly generated C code and yield a 0% false positive rate and a 0%, 20%, and 0% false negative rate, respectively for each rule.
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Summary

Software engineers inadvertently introduce bugs into software during the development process and these bugs can potentially be exploited once the software is deployed. As the size and complexity of software systems increase, it is important that we are able to verify and validate not only that the software behaves as...

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Interactive synthesis of code-level security rules

Suppressing relaxation in superconducting qubits by quasiparticle pumping

December 23, 2016
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Journal Article
Author:
Simon Gustavsson
…
Published in:
Sci., Vol. 354, No. 6319, 23 December 2016, pp. 1573-77.
Topic:
quantum
R&D area:
R&D group:

Summary

Dynamical error suppression techniques are commonly used to improve coherence in quantum systems. They reduce dephasing errors by applying control pulses designed to reverse erroneous coherent evolution driven by environmental noise. However, such methods cannot correct for irreversible processes such as energy relaxation. We investigate a complementary, stochastic approach to reducting errors: instead of deterministically reversing the unwanted qubit evolution, we use control pulses to shape the noise environment dynamically. in the context of superconducting qubits, we implement a pumping sequence to reduce the number of unpaired electrons (quasiparticles) in close proximity to the device. A 70% reduction in the quasiparticle density reesults in a threefold enhancement in qubit relaxation times and a comparable reduction in coherence variablity.
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Summary

Dynamical error suppression techniques are commonly used to improve coherence in quantum systems. They reduce dephasing errors by applying control pulses designed to reverse erroneous coherent evolution driven by environmental noise. However, such methods cannot correct for irreversible processes such as energy relaxation. We investigate a complementary, stochastic approach to...

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Suppressing relaxation in superconducting qubits by quasiparticle pumping

Multi-lingual deep neural networks for language recognition

December 13, 2016
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Conference Paper
Author:
Luis R. Murphy Marcos
…
Published in:
SLT 2016, IEEE Spoken Language Technology Workshop, 13-16 December 2016.
Topic:
language recognition
R&D area:
R&D group:

Summary

Multi-lingual feature extraction using bottleneck layers in deep neural networks (BN-DNNs) has been proven to be an effective technique for low resource speech recognition and more recently for language recognition. In this work we investigate the impact on language recognition performance of the multi-lingual BN-DNN architecture and training configurations for the NIST 2011 and 2015 language recognition evaluations (LRE11 and LRE15). The best performing multi-lingual BN-DNN configuration yields relative performance gains of 50% on LRE11 and 40% on LRE15 compared to a standard MFCC/SDC baseline system and 17% on LRE11 and 7% on LRE15 relative to a single language BN-DNN system. Detailed performance analysis using data from all 24 Babel languages, Fisher Spanish and Switchboard English shows the impact of language selection and the amount of training data on overall BN-DNN performance.
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Summary

Multi-lingual feature extraction using bottleneck layers in deep neural networks (BN-DNNs) has been proven to be an effective technique for low resource speech recognition and more recently for language recognition. In this work we investigate the impact on language recognition performance of the multi-lingual BN-DNN architecture and training configurations for...

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Multi-lingual deep neural networks for language recognition

Approaches for language identification in mismatched environments

December 13, 2016
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Conference Paper
Author:
Shahan C. Nercessian
…
Published in:
SLT 2016, IEEE Spoken Language Technology Workshop, 13-16 December 2016.
Topic:
language recognition
R&D area:
R&D group:

Summary

In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system. The evaluation is performed on a 9-language set that includes data in both conversational telephone speech and narrowband broadcast speech. Multiple experiments are conducted to assess the performance of the system in this condition and a number of alternatives to ameliorate the drop in performance. The best system evaluated is based on deep neural network (DNN) bottleneck features using i-vectors utilizing a combination of all the approaches proposed in this work. The resulting system improved baseline DNN system performance by 30%.
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Summary

In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system. The evaluation is performed on a 9-language set that includes data in...

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Approaches for language identification in mismatched environments

Resilience of cyber systems with over- and underregulation

December 9, 2016
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Journal Article
Author:
Viktoria Gisladottir
…
Published in:
Risk Analysis, Vol. 37, No. 9, 2017, pp. 1644-51, DOI:10.1111/risa.12729.
Topic:
cyber security
R&D area:
R&D group:

Summary

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under- and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad-hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.
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Summary

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber...

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Resilience of cyber systems with over- and underregulation

Intersection and convex combination in multi-source spectral planted cluster detection

December 7, 2016
  |
Conference Paper
Author:
Benjamin A. Miller
…
Published in:
IEEE Global Conf. on Signal and Information Processing, GlobalSIP, 7-9 December 2016.
Topic:
signal processing
R&D area:
R&D group:

Summary

Planted cluster detection is an important form of signal detection when the data are in the form of a graph. When there are multiple graphs representing multiple connection types, the method of aggregation can have significant impact on the results of a detection algorithm. This paper addresses the tradeoff between two possible aggregation methods: convex combination and intersection. For a spectral detection method, convex combination dominates when the cluster is relatively sparse in at least one graph, while the intersection method dominates in cases where it is dense across graphs. Experimental results confirm the theory. We consider the context of adversarial cluster placement, and determine how an adversary would distribute connections among the graphs to best avoid detection.
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Summary

Planted cluster detection is an important form of signal detection when the data are in the form of a graph. When there are multiple graphs representing multiple connection types, the method of aggregation can have significant impact on the results of a detection algorithm. This paper addresses the tradeoff between...

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Intersection and convex combination in multi-source spectral planted cluster detection

Bootstrapping and maintaining trust in the cloud

December 5, 2016
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Conference Paper
Author:
Nabil A. Schear
…
Published in:
32nd Annual Computer Security Applications Conf., ACSAC 2016, 5-9 December 2016.
Topic:
computing
R&D area:
R&D group:

Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. Cloud providers do not offer the ability to create hardware-rooted cryptographic identities for IaaS cloud resources or sufficient information to verify the integrity of systems. Trusted computing protocols and hardware like the TPM have long promised a solution to this problem. However, these technologies have not seen broad adoption because of their complexity of implementation, low performance, and lack of compatibility with virtualized environments. In this paper we introduce keylime, a scalable trusted cloud key management system. keylime provides an end-to-end solution for both bootstrapping hardware rooted cryptographic identities for IaaS nodes and for system integrity monitoring of those nodes via periodic attestation. We support these functions in both bare-metal and virtualized IaaS environments using a virtual TPM. keylime provides a clean interface that allows higher level security services like disk encryption or configuration management to leverage trusted computing without being trusted computing aware. We show that our bootstrapping protocol can derive a key in less than two seconds, we can detect system integrity violations in as little as 110ms, and that keylime can scale to thousands of IaaS cloud nodes.
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Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. Cloud providers do not offer the ability to create hardware-rooted cryptographic identities for IaaS cloud resources or sufficient information to verify the integrity of systems. Trusted computing protocols and hardware...

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Bootstrapping and maintaining trust in the cloud

Predicting and analyzing factors in patent litigation

December 5, 2016
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Conference Paper
Author:
William M. Campbell
…
Published in:
30th Conf. on Neural Information Processing System, NIPS 2016, 5-10 December 2016.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

Patent litigation is an expensive and time-consuming process. To minimize its impact on the participants in the patent lifecycle, automatic determination of litigation potential is a compelling machine learning application. In this paper, we consider preliminary methods for the prediction of a patent being involved in litigation using metadata, content, and graph features. Metadata features are top-level easily-extractable features, i.e., assignee, number of claims, etc. The content feature performs lexical analysis of the claims associated to a patent. Graph features use relational learning to summarize patent references. We apply our methods on US patents using a labeled data set. Prior work has focused on metadata-only features, but we show that both graph and content features have significant predictive capability. Additionally, fusing all features results in improved performance. We also perform a preliminary examination of some of the qualitative factors that may have significant importance in patent litigation.
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Summary

Patent litigation is an expensive and time-consuming process. To minimize its impact on the participants in the patent lifecycle, automatic determination of litigation potential is a compelling machine learning application. In this paper, we consider preliminary methods for the prediction of a patent being involved in litigation using metadata, content...

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Predicting and analyzing factors in patent litigation

An overview of the DARPA Data Driven Discovery of Models (D3M) Program

December 5, 2016
  |
Conference Paper
Author:
Richard P. Lippmann
…
Published in:
29th Conf. on Neural Information Processing Systems, NIPS, 5-10 December 2016.
R&D area:
R&D group:

Summary

A new DARPA program called Data Driven Discovery of Models (D3M) aims to develop automated model discovery systems that can be used by researchers with specific subject matter expertise to create empirical models of real, complex processes. Two major goals of this program are to allow experts to create empirical models without the need for data scientists and to increase the productivity of data scientists via automation. Automated model discovery systems developed will be tested on real-world problems that progressively get harder during the course of the program. Toward the end of the program, problems will be both unsolved and underspecified in terms of data and desired outcomes. The program will emphasize creating and leveraging open source technology and architecture. Our presentation reviews the goals and structure of this program which will begin early in 2017. Although the deadline for submitting proposals has past, we welcome suggestions concerning challenge tasks, evaluations, or new open-source data sets to be included for system development and evaluation that would supplement data currently being curated from many sources.
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Summary

A new DARPA program called Data Driven Discovery of Models (D3M) aims to develop automated model discovery systems that can be used by researchers with specific subject matter expertise to create empirical models of real, complex processes. Two major goals of this program are to allow experts to create empirical...

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An overview of the DARPA Data Driven Discovery of Models (D3M) Program

High performance, 3D-printable dielectric nanocomposites for millimeter wave devices

December 5, 2016
  |
Journal Article
Author:
Michael T. Lis
…
Published in:
ACS Appl. Mater. Interfaces, Vol. 8, 2016, 34019-26.
Topic:
additive manufacturing
R&D area:
R&D group:

Summary

The creation of millimeter wave, 3D-printable dielectric nanocomposite is demonstrated. Alumina nanoparticles were combined with styrenic block copolymers and solvent to create shear thinning, viscoelastic inks that are printable at room temperature. Particle loadings of up to 41 vol % were achieved. Upon being dried, the highest-performing of these materials has a permittivity of 4.61 and a loss tangent of 0.00298 in the Ka band (26.5-40 GHz), a combination not previously demonstrated for 3D printing. These nanocomposite materials were used to print a simple resonator device with predictable pass-band features.
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Summary

The creation of millimeter wave, 3D-printable dielectric nanocomposite is demonstrated. Alumina nanoparticles were combined with styrenic block copolymers and solvent to create shear thinning, viscoelastic inks that are printable at room temperature. Particle loadings of up to 41 vol % were achieved. Upon being dried, the highest-performing of these materials...

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High performance, 3D-printable dielectric nanocomposites for millimeter wave devices
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