UMBC ebiquity
UMBC eBiquity Blog

new paper: App behavioral analysis using system calls

Tim Finin, 10:40am 14 March 2017

Prajit Kumar Das, Anupam Joshi and Tim Finin, App behavioral analysis using system calls, MobiSec: Security, Privacy, and Digital Forensics of Mobile Systems and Networks, IEEE Conference on Computer Communications Workshops, May 2017.

System calls provide an interface to the services made available by an operating system. As a result, any functionality provided by a software application eventually reduces to a set of fixed system calls. Since system calls have been used in literature, to analyze program behavior we made an assumption that analyzing the patterns in calls made by a mobile application would provide us insight into its behavior. In this paper, we present our preliminary study conducted with 534 mobile applications and the system calls made by them. Due to a rising trend of mobile applications providing multiple functionalities, our study concluded, mapping system calls to functional behavior of a mobile application was not straightforward. We use Weka tool and manually annotated application behavior classes and system call features in our experiments to show that using such features achieves mediocre F1-measure at best, for app behavior classification. Thus leading to the conclusion that system calls were not sufficient features for app behavior classification.


 

SADL: Semantic Application Design Language

Tim Finin, 9:19am 4 March 2017

SADL – Semantic Application Design Language

Dr. Andrew W. Crapo
GE Global Research

 10:00 Tuesday, 7 March 2017

The Web Ontology Language (OWL) has gained considerable acceptance over the past decade. Building on prior work in Description Logics, OWL has sufficient expressivity to be useful in many modeling applications. However, its various serializations do not seem intuitive to subject matter experts in many domains of interest to GE. Consequently, we have developed a controlled-English language and development environment that attempts to make OWL plus rules more accessible to those with knowledge to share but limited interest in studying formal representations. The result is the Semantic Application Design Language (SADL). This talk will review the foundational underpinnings of OWL and introduce the SADL constructs meant to capture, validate, and maintain semantic models over their lifecycle.

 

Dr. Crapo has been part of GE’s Global Research staff for over 35 years. As an Information Scientist he has built performance and diagnostic models of mechanical, chemical, and electrical systems, and has specialized in human-computer interfaces, decision support systems, machine reasoning and learning, and semantic representation and modeling. His work has included a graphical expert system language (GEN-X), a graphical environment for procedural programming (Fuselet Development Environment), and a semantic-model-driven user-interface for decision support systems (ACUITy). Most recently Andy has been active in developing the Semantic Application Design Language (SADL), enabling GE to leverage worldwide advances and emerging standards in semantic technology and bring them to bear on diverse problems from equipment maintenance optimization to information security.


 

Context-Dependent Privacy and Security Management on Mobile Devices

Tim Finin, 11:05pm 27 February 2017

Mobile devices and provide better services if then can model, recognize and adapt to their users' context.

Context-Dependent Privacy and Security Management on Mobile Devices

Prajit Das, UMBC

10:00am Tuesday, 27 February, 2017

Security and privacy of mobile devices is a challenging research domain. A prominent aspect of this research focuses on discovering software vulnerabilities for mobile operating systems and mobile apps. The other aspect of research focuses on user privacy and using feedback, generates privacy profiles for controlling data privacy. Profile based or role-based security can be restrictive as they require prior definition of such roles or profiles. As a result, it is better to use attribute-based access control and let the attributes define granularity of policy definition. This problem may thus be defined as, a security and privacy personalization problem. A critical issue in the process of capturing personalized policy is one of creating a system that is adaptive and knows when user’s preferences have been captured. Presented in this work you will learn about Mithril, a framework for capturing user access control policies that are fine-grained, context-sensitive and are represented using Semantic Web technologies and thereby manages access control decisions for user data on mobile devices. Violation metric has been used in this work as a measure to determine system state. A hierarchical context ontology has been used to define fine-grained access control policies and simplifying the process of policy modification for a user. A secondary goal of this research was to determine behavioral traits of mobile applications with a goal to detect outlier applications. Some preliminary research on this topic will also be discussed.


 

Large Scale Cross Domain Temporal Topic Modeling for Climate Change Research

Tim Finin, 8:43am 23 December 2016

Jennifer Sleeman, Milton Halem, Tim Finin, Mark Cane, Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports (poster), American Geophysical Union Fall Meeting 2016, American Geophysical Union, December 2016.

One way of understanding the evolution of science within a particular scientific discipline is by studying the temporal influences that research publications had on that discipline. We provide a methodology for conducting such an analysis by employing cross-domain topic modeling and local cluster mappings of those publications with the historical texts to understand exactly when and how they influenced the discipline. We apply our method to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and the citations therein. The IPCC reports were compiled by thousands of Earth scientists and the assessments were issued approximately every five years over a 30 year span, and includes over 200,000 research papers cited by these scientists.


 

PhD Proposal: Understanding the Logical and Semantic Structure of Large Documents

Tim Finin, 8:54am 9 December 2016

business documents

Dissertation Proposal

Understanding the Logical and Semantic
Structure of Large Documents 

Muhammad Mahbubur Rahman

11:00-1:00 Monday, 12 December 2016, ITE325b, UMBC

Up-to-the-minute language understanding approaches are mostly focused on small documents such as newswire articles, blog posts, product reviews and discussion forum entries. Understanding and extracting information from large documents such as legal documents, reports, business opportunities, proposals and technical manuals is still a challenging task. The reason behind this challenge is that the documents may be multi-themed, complex and cover diverse topics.

We aim to automatically identify and classify a document’s sections and subsections, infer their structure and annotate them with semantic labels to understand the semantic structure of a document. This document’s structure understanding will significantly benefit and inform a variety of applications such as information extraction and retrieval, document categorization and clustering, document summarization, fact and relation extraction, text analysis and question answering.

Committee: Drs. Tim Finin (Chair), Anupam Joshi, Tim Oates, Cynthia Matuszek, James Mayfield (JHU)


 

PhD Proposal: Ankur Padia, Dealing with Dubious Facts in Knowledge Graphs

Tim Finin, 9:25pm 29 November 2016

the skeptic

Dissertation Proposal

Dealing with Dubious Facts
in Knowledge Graphs

Ankur Padia

1:00-3:00pm Wednesday, 30 November 2016, ITE 325b, UMBC

Knowledge graphs are structured representations of facts where nodes are real-world entities or events and edges are the associations among the pair of entities. Knowledge graphs can be constructed using automatic or manual techniques. Manual techniques construct high quality knowledge graphs but are expensive, time consuming and not scalable. Hence, automatic information extraction techniques are used to create scalable knowledge graphs but the extracted information can be of poor quality due to the presence of dubious facts.

An extracted fact is dubious if it is incorrect, inexact or correct but lacks evidence. A fact might be dubious because of the errors made by NLP extraction techniques, improper design consideration of the internal components of the system, choice of learning techniques (semi-supervised or unsupervised), relatively poor quality of heuristics or the syntactic complexity of underlying text. A preliminary analysis of several knowledge extraction systems (CMU’s NELL and JHU’s KELVIN) and observations from the literature suggest that dubious facts can be identified, diagnosed and managed. In this dissertation, I will explore approaches to identify and repair such dubious facts from a knowledge graph using several complementary approaches, including linguistic analysis, common sense reasoning, and entity linking.

Committee: Drs. Tim Finin (Chair), Anupam Joshi, Tim Oates, Paul McNamee (JHU), Partha Talukdar (IISc, India)


 

Understanding Large Documents

Tim Finin, 10:49am 28 November 2016

business documents

In this week’s ebiquity meeting, Muhammad Mahbubur Rahman will about about his work on understanding large documents, such as business RFPs.

Large Document Understanding

Muhammad Mahbubur Rahman

Up-to-the-minute language understanding approaches are mostly focused on small documents such as newswire articles, blog posts, product reviews and discussion forum entries. Understanding and extracting information from large documents such as legal documents, reports, business opportunities, proposals and technical manuals is still a challenging task. The reason behind this challenge is that the documents may be multi-themed, complex and cover diverse topics.

We aim to automatically identify and classify a document’s sections and subsections, infer their structure and annotate them with semantic labels to understand the semantic structure of a document. This document’s structure understanding will significantly benefit and inform a variety of applications such as information extraction and retrieval, document categorization and clustering, document summarization, fact and relation extraction, text analysis and question answering.


 

PhD proposal: Sandeep Nair Narayanan, Cognitive Analytics Framework to Secure Internet of Things

Tim Finin, 12:47pm 26 November 2016

cognitive car

Dissertation Proposal

Cognitive Analytics Framework to Secure Internet of Things

Sandeep Nair Narayanan

1:00-3:30pm, Monday, 28 November 2016, ITE 325b

Recent years have seen the rapid growth and widespread adoption of Internet of Things in a wide range of domains including smart homes, healthcare, automotive, smart farming and smart grids. The IoT ecosystem consists of devices like sensors, actuators and control systems connected over heterogeneous networks. The connected devices can be from different vendors with different capabilities in terms of power requirements, processing capabilities, etc. As such, many security features aren’t implemented on devices with lesser processing capabilities. The level of security practices followed during their development can also be different. Lack of over the air update for firmware also pose a very big security threat considering their long-term deployment requirements. Device malfunctioning is yet another threat which should be considered. Hence, it is imperative to have an external entity which monitors the ecosystem and detect attacks and anomalies.

In this thesis, we propose a security framework for IoTs using cognitive techniques. While anomaly detection has been employed in various domains, some challenges like online approach, resource constraints, heterogeneity, distributed data collection etc. are unique to IoTs and their predecessors like wireless sensor networks. Our framework will have an underlying knowledge base which has the domain-specific information, a hybrid context generation module which generates complex contexts and a fast reasoning engine which does logical reasoning to detect anomalous activities. When raw sensor data arrives, the hybrid context generation module queries the knowledge base and generates different simple local contexts using various statistical and machine learning models. The inferencing engine will then infer global complex contexts and detects anomalous activities using knowledge from streaming facts and and domain specific rules encoded in the Ontology we will create. We will evaluate our techniques by realizing and validating them in the vehicular domain.

Committee: Drs. Dr. Anupam Joshi (Chair), Dr. Tim Finin, Dr. Nilanjan Banerjee, Dr. Yelena Yesha, Dr. Wenjia Li, NYIT, Dr. Filip Perich, Google


 

Dealing with Dubious Facts in Knowledge Graphs

Tim Finin, 10:47am 22 November 2016

In this week’s meeting, Ankur Padia will about about his work on the problem of identifying and managing ‘dubious facts’ extracted from text and added to a knowledge graph.

Dealing with Dubious Facts in Knowledge Graphs

Ankur Padia

Knowledge graphs are used to represent real-world facts and events with entities as nodes and relations as labeled edges. Generally, a knowledge graph is automatically constructed by extracting facts from text corpus using information extraction (IE) techniques. Such IE techniques are scalable but often extract low quality (or dubious) facts due to errors caused by NLP libraries, internal components of an extraction system, choice of learning techniques, heuristics and syntactic complexity of underlying text. We wish to explore techniques to process such dubious facts and improve the quality of a knowledge graph.


 

Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports

Tim Finin, 1:40pm 19 November 2016

IPCC Assessment Reports

A temporal analysis of the 200,000 documents cited in thirty years worth of Intergovernmental Panel on Climate Change (IPCC) assessment reports sheds light on how climate change research is evolving.

Jenifer Sleeman, Milton Halem, Tim Finin and Mark Cane, Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports, Big Data Challenges, Research, and Technologies in the Earth and Planetary Sciences Workshop, IEEE Int. Conf. on Big Data, December 2016.

A common Big Data problem is the need to integrate large temporal data sets from various data sources into one comprehensive structure. Having the ability to correlate evolving facts between data sources can be especially useful in supporting a number of desired application functions such as inference and influence identification. As a real world application we use climate change publications based on the Intergovernmental Panel on Climate Change, which publishes climate change assessment reports every five years, with currently over 25 years of published content. Often these reports reference thousands of research papers. We use dynamic topic modeling as a basis for combining report and citation domains into one structure. We are able to correlate documents between the two domains to understand how the research has influenced the reports and how this influence has changed over time. In this use case, the topic report model used a total number of 410 documents and 5911 terms in the vocabulary while in the topic citations the vocabulary consisted of 25,154 terms and the number of documents was closer to 200,000 research papers.


 

Capturing policies for fine-grained access control on mobile devices

Tim Finin, 8:33am 8 November 2016

In this week’s ebiquity meeting (11:30 8 Nov. 2016) Prajit Das will present his work on capturing policies for fine-grained access control on mobile devices.

As of 2016, there are more mobile devices than humans on earth. Today, mobile devices are a critical part of our lives and often hold sensitive corporate and personal data. As a result, they are a lucrative target for attackers, and managing data privacy and security on mobile devices has become a vital issue. Existing access control mechanisms in most devices are restrictive and inadequate. They do not take into account the context of a device and its user when making decisions. In many cases, the access granted to a subject should change based on context of a device. Such fine-grained, context-sensitive access control policies have to be personalized too. In this paper, we present the Mithril system, that uses policies represented in Semantic Web technologies and captured using user feedback, to handle access control on mobile devices. We present an iterative feedback process to capture user specific policy. We also present a policy violation metric that allows us to decide when the capture process is complete.

 

Inferring Relations in Knowledge Graphs with Tensor Decomposition

Tim Finin, 10:44pm 6 November 2016

kgraph

Ankur Padia, Kostantinos Kalpakis, and Tim Finin, Inferring Relations in Multi-relational Knowledge Graphs with Tensor Decomposition, IEEE BigData, Dec. 2016.

Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.