Preventing Poisoning Attacks on Threat Intelligence Systems

April 22nd, 2018

Preventing Poisoning Attacks on Threat Intelligence Systems

Nitika Khurana, Graduate Student, UMBC

11:00-12:00 Monday, 23 April 2018, ITE346, UMBC

As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We also maintain the provenance of the output generated to ensure information and source reliability and identify the background data that caused an attack. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.


UMBC at SemEval-2018 Task 8: Understanding Text about Malware

April 21st, 2018

UMBC at SemEval-2018 Task 8: Understanding Text about Malware

Ankur Padia, Arpita Roy, Taneeya Satyapanich, Francis Ferraro, Shimei Pan, Anupam Joshi and Tim Finin, UMBC at SemEval-2018 Task 8: Understanding Text about Malware, Int. Workshop on Semantic Evaluation (collocated with NAACL-HLT), New Orleans, LA, June 2018.

We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieved F1 scores of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at https://bit.ly/cybr2vec.


Cognitively Rich Framework to Automate Extraction & Representation of Legal Knowledge

April 15th, 2018

Cognitively Rich Framework to Automate Extraction and Representation of Legal Knowledge

Srishty Saha, UMBC
11-12 Monday, 16 April 2018, ITE 346

With the explosive growth in cloud-based services, businesses are increasingly maintaining large datasets containing information about their consumers to provide a seamless user experience. To ensure privacy and security of these datasets, regulatory bodies have specified rules and compliance policies that must be adhered to by organizations. These regulatory policies are currently available as text documents that are not machine processable and so require extensive manual effort to monitor them continuously to ensure data compliance. We have developed a cognitive framework to automatically parse and extract knowledge from legal documents and represent it using an Ontology. The legal ontology captures key-entities and their relations, the provenance of legal-policy and cross-referenced semantically similar legal facts and rules. We have applied this framework to the United States government’s Code of Federal Regulations (CFR) which includes facts and rules for individuals and organizations seeking to do business with the US Federal government.


2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning

April 11th, 2018

2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning

The 2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL) is a student-run, one-day event on speech, language & machine learning research to be held at the University of Maryland, Baltimore County  (UMBC) from 10:00am to 6:00pm on Saturday May 12.  There is no registration charge and lunch and refreshments will be provided.  Students, postdocs, faculty and researchers from universities & industry are invited to participate and network with other researchers working in related fields.

Students and postdocs are encouraged to submit abstracts describing ongoing, planned, or completed research projects, including previously published results and negative results. Research in any field applying computational methods to any aspect of human language, including speech and learning, from all areas of computer science, linguistics, engineering, neuroscience, information science, and related fields is welcome. Submissions and presentations must be made by students or postdocs. Accepted submissions will be presented as either posters or talks.

Important Dates are:

  • Submission deadline (abstracts): April 16
  • Decisions announced: April 21
  • Registration opens: April 10
  • Registration closes: May 6
  • Colloquium: May 12

AI for Cybersecurity: Intrusion Detection Using Neural Networks

March 25th, 2018

AI for Cybersecurity: Intrusion Detection Using Neural Networks

Sowmya Ramapatruni, UMBC

11:00-12:00 Monday 26 March, 2018, ITE346, UMBC

The constant growth in the use of computer networks raised concerns about security and privacy. Intrusion attacks on computer networks is a very common attack on internet today. Intrusion detection systems have been considered essential in keeping network security and therefore have been commonly adopted by network administrators. A possible disadvantage is the fact that such systems are usually based on signature systems, which make them strongly dependent on updated database and consequently inefficient against novel attacks (unknown attacks). In this study we analyze the use of machine learning in the development of intrusion detection system.

The focus of this presentation is to analyze the various machine learning algorithms that can be used to perform classification of network attacks. We will also analyze the common techniques used to build and fine tune artificial neural networks for network attack classification and address the drawbacks in these systems. We will also analyze the data sets and the information that is critical for the classification. The understanding of network packet data is essential for the feature engineering, which is an essential precursor activity for any machine learning systems. Finally, we study the drawbacks of existing machine learning systems and walk through the further study possible in this area.


paper: Cleaning Noisy Knowledge Graphs

January 27th, 2018

Cleaning Noisy Knowledge Graphs

Ankur Padia, Cleaning Noisy Knowledge Graphs, Proceedings of the Doctoral Consortium at the 16th International Semantic Web Conference, October 2017.

My dissertation research is developing an approach to identify and explain errors in a knowledge graph constructed by extracting entities and relations from text. Information extraction systems can automatically construct knowledge graphs from a large collection of documents, which might be drawn from news articles, Web pages, social media posts or discussion forums. The language understanding task is challenging and current extraction systems introduce many kinds of errors. Previous work on improving the quality of knowledge graphs uses additional evidence from background knowledge bases or Web searches. Such approaches are diffuclt to apply when emerging entities are present and/or only one knowledge graph is available. In order to address the problem I am using multiple complementary techniques including entitylinking, common sense reasoning, and linguistic analysis.

 


Jennifer Sleeman receives AI for Earth grant from Microsoft

December 12th, 2017

Jennifer Sleeman receives AI for Earth grant from Microsoft

Visiting Assistant Professor Jennifer Sleeman (Ph.D. ’17)  has been awarded a grant from Microsoft as part of its ‘AI for Earth’ program. Dr. Sleeman will use the grant to continue her research on developing algorithms to model how scientific disciplines such as climate change evolve and predict future trends by analyzing the text of articles and reports and the papers they cite.

AI for Earth is a Microsoft program aimed at empowering people and organizations to solve global environmental challenges by increasing access to AI tools and educational opportunities, while accelerating innovation. Via the Azure for Research AI for Earth award program, Microsoft provides selected researchers and organizations access to its cloud and AI computing resources to accelerate, improve and expand work on climate change, agriculture, biodiversity and/or water challenges.

UMBC is among the first grant recipients of AI for Earth, first launched in July 2017. The grant process was a competitive and selective process and was awarded in recognition of the potential of the work and power of AI to accelerate progress.

As part of her dissertation research, Dr. Sleeman developed algorithms using dynamic topic modeling to understand influence and predict future trends in a scientific discipline. She applied this to the field of climate change and used assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. Her custom dynamic topic modeling algorithm identified topics for both datasets and apply cross-domain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time.

Dr. Sleeman’s award is part of an inaugural set of 35 grants in more than ten countries for access to Microsoft Azure and AI technology platforms, services and training.  In an post on Monday, AI for Earth can be a game-changer for our planet, Microsoft announced its intent to put $50 million over five years into the program, enabling grant-making and educational trainings possible at a much larger scale.

More information about AI for Earth can be found on the Microsoft AI for Earth website.


paper: Automated Knowledge Extraction from the Federal Acquisition Regulations System

November 28th, 2017

Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS)

Srishty Saha and Karuna Pande Joshi, Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS), 2nd International Workshop on Enterprise Big Data Semantic and Analytics Modeling, IEEE Big Data Conference, December 2017.

With increasing regulation of Big Data, it is becoming essential for organizations to ensure compliance with various data protection standards. The Federal Acquisition Regulations System (FARS) within the Code of Federal Regulations (CFR) includes facts and rules for individuals and organizations seeking to do business with the US Federal government. Parsing and gathering knowledge from such lengthy regulation documents is currently done manually and is time and human intensive.Hence, developing a cognitive assistant for automated analysis of such legal documents has become a necessity. We have developed semantically rich approach to automate the analysis of legal documents and have implemented a system to capture various facts and rules contributing towards building an ef?cient legal knowledge base that contains details of the relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules. In this paper, we describe our framework along with the results of automating knowledge extraction from the FARS document (Title48, CFR). Our approach can be used by Big Data Users to automate knowledge extraction from Large Legal documents.


Voices in AI, Episode 20: A Conversation with Marie desJardins

November 20th, 2017

Voices in AI – Episode 20: A Conversation with Marie desJardins

Byron Reese interviewed UMBC CSEE Professor Marie desJardins as part of his Voices in AI podcast series on Gigaom. In the episode, they talk about the Turing test, Watson, autonomous vehicles, and language processing.  Visit the Voices in AI site to listen to the podcast and read the interview transcript.

Here’s the start of the wide-ranging, hour-long interview.

Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. Today I’m excited that our guest is Marie des Jardins. She is an Associate Dean for Engineering and Information Technology as well as a professor of Computer Science at the University of Maryland, Baltimore County. She got her undergrad degree from Harvard, and a Ph.D. in computer science from Berkeley, and she’s been involved in the National Conference of the Association for the Advancement of Artificial Intelligence for over 12 years. Welcome to the show, Marie.

Marie des Jardins: Hi, it’s nice to be here.

I often open the show with “What is artificial intelligence?” because, interestingly, there’s no consensus definition of it, and I get a different kind of view of it from everybody. So I’ll start with that. What is artificial intelligence?

Sure. I’ve always thought about artificial intelligence as just a very broad term referring to trying to get computers to do things that we would consider intelligent if people did them. What’s interesting about that definition is it’s a moving target, because we change our opinions over time about what’s intelligent. As computers get better at doing things, they no longer seem that intelligent to us.

We use the word “intelligent,” too, and I’m not going to dwell on definitions, but what do you think intelligence is at its core?

So, it’s definitely hard to pin down, but I think of it as activities that human beings carry out, that we don’t know of lower order animals doing, other than some of the higher primates who can do things that seem intelligent to us. So intelligence involves intentionality, which means setting goals and making active plans to carry them out, and it involves learning over time and being able to react to situations differently based on experiences and knowledge that we’ve gained over time. The third part, I would argue, is that intelligence includes communication, so the ability to communicate with other beings, other intelligent agents, about your activities and goals.

Well, that’s really useful and specific. Let’s look at some of those things in detail a little bit. You mentioned intentionality. Do you think that intentionality is driven by consciousness? I mean, can you have intentionality without consciousness? Is consciousness therefore a requisite for intelligence?

I think that’s a really interesting question. I would decline to answer it mainly because I don’t think we ever can really know what consciousness is. We all have a sense of being conscious inside our own brains—at least I believe that. But of course, I’m only able to say anything meaningful about my own sense of consciousness. We just don’t have any way to measure consciousness or even really define what it is. So, there does seem to be this idea of self-awareness that we see in various kinds of animals—including humans—and that seems to be a precursor to what we call consciousness. But I think it’s awfully hard to define that term, and so I would be hesitant to put that as a prerequisite on intentionality.


new paper: Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling

November 17th, 2017

Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling

Jennifer Sleeman, Milton Halem, Tim Finin and Mark Cane, Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling, International Conference on Big Data, IEEE, December 2017.

We describe an approach using dynamic topic modeling to model influence and predict future trends in a scientific discipline. Our study focuses on climate change and uses assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. We use a custom dynamic topic modeling algorithm to generate topics for both datasets and apply crossdomain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time. For the IPCC use case, the report topic model used 410 documents and a vocabulary of 5911 terms while the citations topic model was based on 200K research papers and a vocabulary more than 25K terms. We show that our approach can predict the importance of its extracted topics on future IPCC assessments through the use of cross domain correlations, Jensen-Shannon divergences and cluster analytics.


A Practitioners Introduction to Deep Learning, 1pm Fri 11/17

November 14th, 2017

ACM Tech Talk Series

A Practitioner’s Introduction to Deep Learning

Ashwin Kumar Ganesan, PhD student

1:00-2:00pm Friday, 17 November 2017?, ITE325, UMBC

In recent years, Deep Neural Networks have been highly successful at performing a number of tasks in computer vision, natural language processing and artificial intelligence in general. The remarkable performance gains have led to universities and industries investing heavily in this space. This investment creates a thriving open source ecosystem of tools & libraries that aid the design of new architectures, algorithm research as well as data collection.

This talk (and hands-on session) introduce people to some of the basics of machine learning, neural networks and discusses some of the popular neural network architectures. We take a dive into one of the popular libraries, Tensorflow, and an associated abstraction library Keras.

To participate in the hands-on aspects of the workshop, bring a laptop computer with Python installed and install the following libraries using pip.  For windows or (any other OS) consider doing an installation of anaconda that has all the necessary libraries.

  • numpy, scipy & scikit-learn
  • tensorflow / tensoflow-gpu (The first one is the GPU version)
  • matplotlib for visualizations (if necessary)
  • jupyter & ipython (We will use python2.7 in our experiments)

Following are helpful links:

Contact Nisha Pillai (NPillai1 at umbc.edu) with any questions regarding this event.


W3C Recommendation: Time Ontology in OWL

October 26th, 2017

W3C Recommendation: Time Ontology in OWL

The Spatial Data on the Web Working Group has published a W3C Recommendation of the Time Ontology in OWL specification. The ontology provides a vocabulary for expressing facts about  relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional Gregorian calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars.