paper: Early Detection of Cybersecurity Threats Using Collaborative Cognition

October 1st, 2018
The CCS Dashboard’s sections provide information on sources and targets of network events, file operations monitored and sub-events that are part of the APT kill chain. An alert is generated when a likely complete APT is detected after reasoning over events.

The CCS Dashboard’s sections provide information on sources and targets of network events, file operations monitored and sub-events that are part
of the APT kill chain. An alert is generated when a likely complete APT is detected after reasoning over events.

Early Detection of Cybersecurity Threats Using Collaborative Cognition

Sandeep Narayanan, Ashwinkumar Ganesan, Karuna Joshi, Tim Oates, Anupam Joshi and Tim Finin, Early detection of Cybersecurity Threats using Collaborative Cognition, 4th IEEE International Conference on Collaboration and Internet Computing, Philadelphia, October. 2018.

 

The early detection of cybersecurity events such as attacks is challenging given the constantly evolving threat landscape. Even with advanced monitoring, sophisticated attackers can spend more than 100 days in a system before being detected. This paper describes a novel, collaborative framework that assists a security analyst by exploiting the power of semantically rich knowledge representation and reasoning integrated with different machine learning techniques. Our Cognitive Cybersecurity System ingests information from various textual sources and stores them in a common knowledge graph using terms from an extended version of the Unified Cybersecurity Ontology. The system then reasons over the knowledge graph that combines a variety of collaborative agents representing host and network-based sensors to derive improved actionable intelligence for security administrators, decreasing their cognitive load and increasing their confidence in the result. We describe a proof of concept framework for our approach and demonstrate its capabilities by testing it against a custom-built ransomware similar to WannaCry.


talk: Design and Implementation of an Attribute Based Access Controller using OpenStack Services

September 23rd, 2018

Design and Implementation of an Attribute Based Access Controller using OpenStack Services

Sharad Dixit, Graduate Student, UMBC
10:30am Monday, 24 September 2018, ITE346

With the advent of cloud computing, industries began a paradigm shift from the traditional way of computing towards cloud computing as it fulfilled organizations present requirements such as on-demand resource allocation, lower capital expenditure, scalability and flexibility but with that it brought a variety of security and user data breach issues. To solve the issues of user data and security breach, organizations have started to implement hybrid cloud where underlying cloud infrastructure is set by the organization and is accessible from anywhere around the world because of the distinguishable security edges provided by it. However, most of the cloud platforms provide a Role Based Access Controller which does not adequate for complex organizational structures. A novel mechanism is proposed using OpenStack services and semantic web technologies to develop a module which evaluates user’s and project’s multi-varied attributes and run them against access policy rules defined by an organization before granting the access to the user. Henceforth, an organization can deploy our module to obtain a robust and trustworthy access control based on multiple attributes of a user and the project the user has requested in a hybrid cloud platform like OpenStack.


paper: Attribute Based Encryption for Secure Access to Cloud Based EHR Systems

June 4th, 2018

Attribute Based Encryption for Secure Access to Cloud Based EHR Systems

Attribute Based Encryption for Secure Access to Cloud Based EHR Systems

Maithilee Joshi, Karuna Joshi and Tim Finin, Attribute Based Encryption for Secure Access to Cloud Based EHR Systems, IEEE International Conference on Cloud Computing, San Francisco CA, July 2018

 

Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients’ end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR’s access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.


Videos of ISWC 2017 talks

December 16th, 2017

Videos of almost all of the talks from the 16th International Semantic Web Conference (ISWC) held in Vienna in 2017 are online at videolectures.net. They include 89 research presentations, two keynote talks, the one-minute madness event and the opening and closing ceremonies.


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.


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.


Agniva Banerjee on Managing Privacy Policies through Blockchain

October 16th, 2017

Link before you Share: Managing Privacy Policies through Blockchain

Agniva Banerjee

11:00am Monday, 16 October 2017

An automated access-control and audit mechanism that enforces users’ data privacy policies when sharing their data across third parties, by utilizing privacy policy ontology instances with the properties of blockchain.


talk: Automated Knowledge Extraction from the Federal Acquisition Regulations System

September 23rd, 2017

In this week’s meeting, Srishty Saha, Michael Aebig and Jiayong Lin will talk about their work on extracting knowledge from the US FAR System.

Automated Knowledge Extraction from the Federal Acquisition Regulations System

Srishty Saha, Michael Aebig and Jiayong Lin

11am-12pm Monday, 25 September 2017, ITE346, UMBC

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 extracting 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 are developing a semantically rich legal knowledge base representing legal entities and their relationships, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.


2018 Ontology Summit: Ontologies in Context

September 12th, 2017

2018 Ontology Summit: Ontologies in Context

The OntologySummit is an annual series of online and in-person events that involves the ontology community and communities related to each year’s topic. The topic chosen for the 2018 Ontology Summit will be Ontologies in Context, which the summit describes as follows.

“In general, a context is defined to be the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed. Some examples of synonyms include circumstances, conditions, factors, state of affairs, situation, background, scene, setting, and frame of reference. There are many meanings of “context” in general, and also for ontologies in particular. The summit this year will survey these meanings and identify the research problems that must be solved so that contexts can succeed in achieving the full understanding and assessment of an ontology.”

Each year’s Summit comprises of a series of both online and face-to-face events that span about three months. These include a vigorous three-month online discourse on the theme, and online panel discussions, research activities which will culminate in a two-day face-to-face workshop and symposium.

Over the next two months, there will be a sequence of weekly online meetings to discuss, plan and develop the 2018 topic. The summit itself will start in January with weekly online sessions of invited speakers. Visit the the 2018 Ontology Summit site for more information and to see how you can participate in the planning sessions.


Dissertation: Context-Dependent Privacy and Security Management on Mobile Devices

September 10th, 2017

Context-Dependent Privacy and Security Management on Mobile Devices

Prajit Kumar Das, Context-Dependent Privacy and Security Management on Mobile Devices, Ph.D. Dissertation, University of Maryland, Baltimore County, September 2017.

There are ongoing security and privacy concerns regarding mobile platforms that are being used by a growing number of citizens. Security and privacy models typically used by mobile platforms use one-time permission acquisition mechanisms. However, modifying access rights after initial authorization in mobile systems is often too tedious and complicated for users. User studies show that a typical user does not understand permissions requested by applications or are too eager to use the applications to care to understand the permission implications. For example, the Brightest Flashlight application was reported to have logged precise locations and unique user identifiers, which have nothing to do with a flashlight application’s intended functionality, but more than 50 million users used a version of this application which would have forced them to allow this permission. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We have created Mithril as an end-to-end mobile access control framework that allows us to capture access control needs for specific users, by observing violations of known policies. The framework studies mobile application executables to better inform users of the risks associated with using certain applications. The policy capture process involves an iterative user feedback process that captures policy modifications required to mediate observed violations. Precision of policy is used to determine convergence of the policy capture process. Policy rules in the system are written using Semantic Web technologies and the Platys ontology to define a hierarchical notion of context. Policy rule antecedents are comprised of context elements derived using the Platys ontology employing a query engine, an inference mechanism and mobile sensors. We performed a user study that proves the feasibility of using our violation driven policy capture process to gather user-specific policy modifications.

We contribute to the static and dynamic study of mobile applications by defining “application behavior” as a possible way of understanding mobile applications and creating access control policies for them. Our user study also shows that unlike our behavior-based policy, a “deny by default” mechanism hampers usability of access control systems. We also show that inclusion of crowd-sourced policies leads to further reduction in user burden and need for engagement while capturing context-based access control policy. We enrich knowledge about mobile “application behavior” and expose this knowledge through the Mobipedia knowledge-base. We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth, low energy beacons and Nearby Messaging services from Google.


PhD defense: Prajit Das, Context-dependent privacy and security management on mobile devices

August 17th, 2017

Ph.D. Dissertation Defense

Context-dependent privacy and security management on mobile devices

Prajit Kumar Das

8:00-11:00am Tuesday, 22 August 2017, ITE325b, UMBC

There are ongoing security and privacy concerns regarding mobile platforms which are being used by a growing number of citizens. Security and privacy models typically used by mobile platforms use one-time permission acquisition mechanisms. However, modifying access rights after initial authorization in mobile systems is often too tedious and complicated for users. User studies show that a typical user does not understand permissions requested by applications or are too eager to use the applications to care to understand the permission implications. For example, the Brightest Flashlight application was reported to have logged precise locations and unique user identifiers, which have nothing to do with a flashlight application’s intended functionality, but more than 50 million users used a version of this application which would have forced them to allow this permission. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We have created Mithril as an end-to-end mobile access control framework that allows us to capture access control needs for specific users, by observing violations of known policies. The framework studies mobile application executables to better inform users of the risks associated with using certain applications. The policy capture process involves an iterative user feedback process that captures policy modifications required to mediate observed violations. Precision of policy is used to determine convergence of the policy capture process. Policy rules in the system are written using Semantic Web technologies and the Platys ontology to define a hierarchical notion of context. Policy rule antecedents are comprised of context elements derived using the Platys ontology employing a query engine, an inference mechanism and mobile sensors. We performed a user study that proves the feasibility of using our violation driven policy capture process to gather user-specific policy modifications.

We contribute to the static and dynamic study of mobile applications by defining “application behavior” as a possible way of understanding mobile applications and creating access control policies for them. Our user study also shows that unlike our behavior-based policy, a “deny by default” mechanism hampers usability of access control systems. We also show that inclusion of crowd-sourced policies leads to further reduction in user burden and need for engagement while capturing context-based access control policy. We enrich knowledge about mobile “application behavior” and expose this knowledge through the Mobipedia knowledge-base. We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth, low energy beacons and Nearby Messaging services from Google.

Committee: Drs. Anupam Joshi (chair), Tim Finin (co-chair), Tim Oates, Nilanjan Banerjee, Arkady Zaslavsky, (CSIRO), Dipanjan Chakraborty (Shopperts)


PhD defense: Deep Representation of Lyrical Style and Semantics for Music Recommendation

July 16th, 2017

Dissertation Defense

Deep Representation of Lyrical Style and Semantics for Music Recommendation

Abhay L. Kashyap

11:00-1:00 Thursday, 20 July 2017, ITE 346

In the age of music streaming, the need for effective recommendations is important for music discovery and a personalized user experience. Collaborative filtering based recommenders suffer from popularity bias and cold-start which is commonly mitigated by content features. For music, research in content based methods have mainly been focused in the acoustic domain while lyrical content has received little attention. Lyrics contain information about a song’s topic and sentiment that cannot be easily extracted from the audio. This is especially important for lyrics-centric genres like Rap, which was the most streamed genre in 2016. The goal of this dissertation is to explore and evaluate different lyrical content features that could be useful for content, context and emotion based models for music recommendation systems.

With Rap as the primary use case, this dissertation focuses on featurizing two main aspects of lyrics; its artistic style of composition and its semantic content. For lyrical style, a suite of high level rhyme density features are extracted in addition to literary features like the use of figurative language, profanity and vocabulary strength. In contrast to these engineered features, Convolutional Neural Networks (CNN) are used to automatically learn rhyme patterns and other relevant features. For semantics, lyrics are represented using both traditional IR techniques and the more recent neural embedding methods.

These lyrical features are evaluated for artist identification and compared with artist and song similarity measures from a real-world collaborative filtering based recommendation system from Last.fm. It is shown that both rhyme and literary features serve as strong indicators to characterize artists with feature learning methods like CNNs achieving comparable results. For artist and song similarity, a strong relationship was observed between these features and the way users consume music while neural embedding methods significantly outperformed LSA. Finally, this work is accompanied by a web-application, Rapalytics.com, that is dedicated to visualizing all these lyrical features and has been featured on a number of media outlets, most notably, Vox, attn: and Metro.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates, Cynthia Matuszek and Pranam Kolari (Walmart Labs)