Kristin Glass

Obstacle avoidance for redundant robots using configuration control

A redundant robot control scheme is provided for avoiding obstacles in a workspace during motion of an end effector along a preselected trajectory by stopping motion of the critical point on the robot closest to the obstacle when the distance therebetween is reduced to a predetermined sphere of influence surrounding the obstacle. Algorithms are provided for conveniently determining the critical point and critical distance.

Adaptive regulation of manipulators using only position measurements

This article considers the motion-control problem for uncertain robot manipulators in the case where only joint-position mea surements are available, and proposes an adaptive controller as a solution to this problem. The proposed control strategy is general and computationally effrcient, requires very little infor mation regarding the manipulator model or the payload, and ensures that the position-regulation error possesses desirable convergence properties: semiglobal asymptotic convergence if no external disturbances are present, and semiglobal con vergence to an arbitrarily small neighborhood of zero in the presence of bounded disturbances. It is shown that the adaptive controller can be modified to provide accurate trajectory track ing control through the introduction of feedforrvard elements in the control law. The adaptive regulation and tracking schemes have been implemented in laboratory experiments with an IMI Zebra Zero manipulator. These experiments demonstrate that accurate and robust motion control can be achieved by using the proposed approach.

Estimating sentiment orientation in social media for intelligence monitoring and analysis

This paper presents a computational approach to inferring the sentiment orientation of “social media” content (e.g., blog posts) which focuses on the challenges associated with Web-based analysis. The proposed methodology formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and uses this framework to develop a semi-supervised sentiment classifier that is well-suited for social media domains. In particular, the proposed algorithm is capable of combining prior knowledge regarding the sentiment orientation of a few documents and words with information present in unlabeled data, which is abundant online. We demonstrate the utility of the approach by showing it outperforms several standard methods for the task of inferring the sentiment of online movie reviews, and illustrate its potential for security informatics through a case study involving the estimation of Indonesian public sentiment regarding the July 2009 Jakarta hotel bombings.

Proactive defense for evolving cyber threats

There is significant interest to develop proactive approaches to cyber defense, in which future attack strategies are anticipated and these insights are incorporated into defense designs. This paper considers the problem of protecting computer networks against intrusions and other attacks, and leverages the coevolutionary relationship between attackers and defenders to derive two new methods for proactive network defense. The first method is a bipartite graph-based machine learning algorithm which enables information concerning previous attacks to be “transferred” for application against novel attacks, thereby substantially increasing the rate with which defense systems can successfully respond to new attacks. The second approach involves exploiting basic threat information (e.g., from cyber security analysts) to generate “synthetic” attack data for use in training defense systems, resulting in networks defenses that are effective against both current and (near) future attacks. The utility of the proposed methods is demonstrated by showing that they outperform standard techniques for the task of detecting malicious network activity in two publicly-available cyber datasets.

Web Analytics for Security Informatics

An enormous volume of security-relevant information is present on the Web, for instance in the content produced each day by millions of bloggers worldwide, but discovering and making sense of these data is very challenging. This paper considers the problem of exploring and analyzing the Web to realize three fundamental objectives: 1.) security-relevant information is covery, 2.) target situational awareness, typically by making (near) real-time inferences concerning events and activities from available observations, and 3.) predictive analysis, to include providing early warning for crises and forming predictions regarding likely outcomes of emerging issues and contemplated interventions. The proposed approach involves collecting and integrating three types of Web data -- textual, relational, and temporal -- to perform assessments and generate insights that would be difficult or impossible to obtain using standard methods. We demonstrate the efficacy of the framework by summarizing a number of successful real-world deployments of the methodology.

Predictability-oriented defense against adaptive adversaries

There are substantial potential benefits to considering predictability when designing defenses against adaptive adversaries, including increasing the ability of defense systems to predict new attacker behavior and reducing the capacity of adversaries to anticipate defensive actions. This paper adopts such a perspective, leveraging the coevolutionary relationship between attackers and defenders to derive methods for predicting and countering attacks and for limiting the extent to which adversaries can learn about defense strategies. The proposed approach combines game theory with machine learning to model adversary adaptation in the learners feature space, thereby producing classes of predictive and “moving target” defenses which are scientifically-grounded and applicable to problems of real-world scale and complexity. Case studies with large cyber security datasets demonstrate that the proposed algorithms outperform gold-standard techniques, offering effective and robust defense against evolving adversaries.

Predictive defense against evolving adversaries

Adaptive adversaries are a primary concern in several domains, including cyber defense, border security, counterterrorism, and fraud prevention, and consequently there is great interest in developing defenses that maintain their effectiveness in the presence of evolving adversary strategies and tactics. This paper leverages the coevolutionary relationship between attackers and defenders to derive two new approaches to predictive defense, in which future attack techniques are anticipated and these insights are incorporated into defense designs. The first method combines game theory with machine learning to model and predict future adversary actions in the learners “feature space”; these predictions form the basis for synthesizing robust defenses. The second approach to predictive defense involves extrapolating the evolution of defense configurations forward in time, in the space of defense parameterizations, as a way of generating defenses which work well against evolving threats. Case studies with a large cyber security dataset assembled for this investigation demonstrate that each method provides effective, scalable defense against current and future attacks, outperforming gold-standard techniques. Additionally, preliminary tests indicate that a simple variant of the proposed design methodology yields defenses which are difficult for adversaries to reverse-engineer.

Automatically identifying the sources of large Internet events

The Internet occasionally experiences large disruptions, arising from both natural and manmade disturbances, and it is of significant interest to develop methods for locating within the network the source of a given disruption (i.e., the network element(s) whose perturbation initiated the event). This paper presents a near real-time approach to realizing this logical localization objective. The proposed methodology consists of three steps: 1.) data acquisition/preprocessing, in which publicly available measurements of Internet activity are acquired, “cleaned”, and assembled into a format suitable for computational analysis, 2.) event characterization via tensor factorization-based time series analysis, and 3.) localization of the source of the disruption through graph theoretic analysis. This procedure provides a principled, automated approach to identifying the root causes of network disruptions at “whole-Internet” scale. The considerable potential of the proposed analytic method is illustrated through a computer simulation study and empirical analysis of a recent, large-scale Internet disruption.

Early warning analysis for social diffusion events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance enabling early identification of contentious “triggering” incidents that are likely to grow into large, self-sustaining mobilization events. Recently we have shown, using theoretical analysis, that the dynamics of social diffusion may depend crucially upon the interactions of social network communities, that is, densely connected groupings of individuals which have only relatively few links to other groups. This paper presents an empirical investigation of two hypotheses which follow from this finding: 1.) the presence of even just a few inter-community links can make diffusion activity in one community a significant predictor of activity in otherwise disparate communities and 2.) very early dispersion of a diffusion process across network communities is a reliable early indicator that the diffusion will ultimately involve a substantial number of individuals. We explore these hypotheses with case studies involving emergence of the Swedish Social Democratic Party at the turn of the 20th century, the spread of SARS in 2002–2003, and blogging dynamics associated with potentially incendiary real world occurrences. These empirical studies demonstrate that network community-based diffusion metrics do indeed possess predictive power, and in fact can be significantly more predictive than standard measures.

Agile Sentiment Analysis of Social Media Content for Security Informatics Applications

Inferring the sentiment of social media content, for instance blog posts and forum threads, is both of great interest to security analysts and technically challenging to accomplish. This paper presents a new method for estimating social media sentiment which addresses the challenges associated with Web-based analysis. The approach formulates the task as one of learning-based text classification, models the data as a bipartite graph of documents and words, and provides accurate sentiment estimation using only a small lexicon of words of known sentiment orientation, in particular, good performance is obtained without the need for labeled training documents. This capability for effective learning without (labeled) exemplar documents is realized by 1.)exploiting the information present in unlabeled documents and words, which are abundant online, and 2.) appropriately smoothing the sentiment polarity estimates for documents and words in the bipartite graph data model. The utility of the proposed algorithm is demonstrated through implementation with a standardsentiment analysis task involving online consumer product reviews. Additionally, we illustrate the potential of the method for security informatics by inferring regional public opinion regarding the Egyptian revolution via analysis of Arabic, Indonesian, and Danish blog posts.