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Predicting exploitation of disclosed software vulnerabilities using open-source data

March 24, 2017
  |
Conference Paper
Author:
Benjamin L. Bullough
…
Published in:
3rd ACM Int. Workshop on Security and Privacy Analytics, IWSPA 2017, 24 March 2017.
Topic:
cyber security
R&D area:
R&D group:

Summary

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that infl ate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility.
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Summary

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are...

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Predicting exploitation of disclosed software vulnerabilities using open-source data

High-efficiency large-angle Pancharatnam phase deflector based on dual-twist design

March 20, 2017
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Journal Article
Author:
Kun Gao
…
Published in:
Opt. Express, Vol. 25, No. 6, 20 March 2017.
Topic:
optics
R&D area:
R&D group:

Summary

We have previously shown through simulation that an optical beam deflector based on the Pancharatnam (geometric) phase can provide high efficiency with up to 80° deflection using a dual-twist structure for polarization-state control [Appl. Opt. 54, 10035 (2015)]. In this report, we demonstrate that its optical performance is as predicted and far beyond what could be expected for a conventional diffractive optical device. We provide details about construction and characterization of a ± 40° beam-steering device with 90% diffraction efficiency based on our dual-twist design at a 633nm wavelength.
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Summary

We have previously shown through simulation that an optical beam deflector based on the Pancharatnam (geometric) phase can provide high efficiency with up to 80° deflection using a dual-twist structure for polarization-state control [Appl. Opt. 54, 10035 (2015)]. In this report, we demonstrate that its optical performance is as predicted...

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High-efficiency large-angle Pancharatnam phase deflector based on dual-twist design

Causal inference under network interference: a framework for experiments on social networks

January 1, 2017
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Thesis
Author:
Edward K. Kao
Published in:
Thesis (Ph.D.)--Harvard University, 2017.
Topic:
networking
R&D area:
R&D group:

Summary

No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect the outcomes of other units, a phenomenon known as interference. This thesis develops a causal framework and inference methodology for experiments where interference takes place on a network of influence (i.e. network interference). In this framework, the network potential outcomes serve as the key quantity and flexible building blocks for causal estimands that represent a variety of primary, peer, and total treatment effects. These causal estimands are estimated via principled Bayesian imputation of missing outcomes. The theory on the unconfoundedness assumptions leading to simplified imputation highlights the importance of including relevant network covariates in the potential outcome model. Additionally, experimental designs that result in balanced covariates and sizes across treatment exposure groups further improve the causal estimate, especially by mitigating potential outcome model mis-specification. The true potential outcome model is not typically known in real-world experiments, so the best practice is to account for interference and confounding network covariates through both balanced designs and model-based imputation. A full factorial simulated experiment is formulated to demonstrate this principle by comparing performance across different randomization schemes during the design phase and estimators during the analysis phase, under varying network topology and true potential outcome models. Overall, this thesis asserts that interference is not just a nuisance for analysis but rather an opportunity for quantifying and leveraging peer effects in real-world experiments.
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Summary

No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect the outcomes of other units, a phenomenon known as interference. This thesis develops a...

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Causal inference under network interference: a framework for experiments on social networks

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

December 7, 2016
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Conference Paper
Author:
Benjamin A. Miller
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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

LLTools: machine learning for human language processing

December 5, 2016
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Conference Paper
Author:
Cagri K. Dagli
…
Published in:
30th Conf. on Neural Info. Processing Syst., NIPS 2016, 5-10 December 2016.
Topic:
big data
R&D area:
R&D group:

Summary

Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components into larger applications. For the speech processing component, the pySLGR (Speaker, Language, Gender Recognition) tool provides signal processing, standard feature analysis, speech utterance embedding, and machine learning modeling methods in Python. The text processing component in LLTools extracts semantically meaningful insights from unstructured data via entity extraction, topic modeling, and document classification. The entity resolution component in LLTools provides approximate string matching, author recognition and graph-based methods for identifying and linking different instances of the same real-world entity. We show through two applications that LLTools can be used to rapidly create and train research prototypes for human language processing.
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Summary

Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components...

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LLTools: machine learning for human language processing

Sparse-coded net model and applications

September 13, 2016
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Conference Paper
Author:
Youngjune L. Gwon
…
Published in:
2016 IEEE Int. Workshop on Machine Learning for Signal Processing, 13-16 September 2016.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding should also be built as a holistic learning unit optimizing on the supervised task objectives more explicitly. In this paper, we propose sparse-coded net, a feedforward model that integrates sparse coding and task-driven output layers, and describe training methods in detail. After pretraining a sparse-coded net via semi-supervised learning, we optimize its task-specific performance in a novel backpropagation algorithm that can traverse nonlinear feature pooling operators to update the dictionary. Thus, sparse-coded net can be applied to supervised dictionary learning. We evaluate sparse-coded net with classification problems in sound, image, and text data. The results confirm a significant improvement over semi-supervised learning as well as superior classification performance against deep stacked autoencoder neural network and GMM-SVM pipelines in small to medium-scale settings.
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Summary

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task...

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Sparse-coded net model and applications

Cross-domain entity resolution in social media

July 11, 2016
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Conference Paper
Author:
William M. Campbell
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Published in:
4th Int. Workshop on Natural Language Processing for Social Media, SocialNLP with IJCAI, 11 July 2016.
Topic:
social network
R&D area:
R&D group:

Summary

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques. Our methods fall into three categories: profile, content, and graph based. For the profile-based methods, we consider techniques based on approximate string matching. For content-based methods, we perform author identification. Finally, for graph-based methods, we apply novel cross-domain community detection methods and generate neighborhood-based features. The three categories of methods are applied to a large graph of users in Twitter and Instagram to understand challenges, determine performance, and understand fusion of multiple methods. Final results demonstrate an equal error rate less than 1%.
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Summary

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution...

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Cross-domain entity resolution in social media

Generating a multiple-prerequisite attack graph

May 17, 2016
Author:
Richard P. Lippmann
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Published in:
PATENT-9344444
Topic:
attack graphs
R&D area:
R&D group:

Summary

In one aspect, a method to generate an attack graph includes determining if a potential node provides a first precondition equivalent to one of preconditions provided by a group of preexisting nodes on the attack graph. The group of preexisting nodes includes a first state node, a first vulnerability instance node, a first prerequisite node, and a second state node. The method also includes, if the first precondition is equivalent to one of the preconditions provided by the group of preexisting nodes, coupling a current node to a preexisting node providing the precondition equivalent to the first precondition using a first edge and if the first precondition is not equivalent to one of the preconditions provided by the group of preexisting nodes, generating the potential node as a new node on the attack graph and coupling the new node to the current node using a second edge.
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Summary

In one aspect, a method to generate an attack graph includes determining if a potential node provides a first precondition equivalent to one of preconditions provided by a group of preexisting nodes on the attack graph. The group of preexisting nodes includes a first state node, a first vulnerability instance...

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Generating a multiple-prerequisite attack graph

A reverse approach to named entity extraction and linking in microposts

April 11, 2016
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Conference Paper
Author:
Kara B. Greenfield
…
Published in:
Proc. of the 6th Workshop on "Making Sense of Microposts" (part of: 25th Int. World Wide Web Conf., 11 April 2016), #Microposts2016, pp. 67-69.
Topic:
topic recognition
R&D area:
R&D group:

Summary

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.
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Summary

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions...

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A reverse approach to named entity extraction and linking in microposts

Named entity recognition in 140 characters or less

April 11, 2016
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Conference Paper
Author:
Kelly L. Geyer
…
Published in:
Proc. of the 6th Workshop on "Making Sense of Microposts" (part of: 25th Int. World Wide Web Conf., 11 April 2016), #Microposts2016, pp. 78-79.
Topic:
topic recognition
R&D area:
R&D group:

Summary

In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an increasingly important genre. This paper presents the MIT Information Extraction Toolkit (MITIE) and explores its adaptability to the micropost genre.
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Summary

In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an increasingly...

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Named entity recognition in 140 characters or less
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