Archive for September, 2015
September 29th, 2015, by Tim Finin, posted in NLP, Ontologies, RDF, Semantic Web
Clare Grasso, Anupam Joshi and ELior Siegel, Beyond NER: Towards Semantics in Clinical Text, Biomedical Data Mining, Modeling, and Semantic Integration (BDM2I); co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA.
While clinical text NLP systems have become very effective in recognizing named entities in clinical text and mapping them to standardized terminologies in the normalization process, there remains a gap in the ability of extractors to combine entities together into a complete semantic representation of medical concepts that contain multiple attributes each of which has its own set of allowed named entities or values. Furthermore, additional domain knowledge may be required to determine the semantics of particular tokens in the text that take on special meanings in relation to this concept. This research proposes an approach that provides ontological mappings of the surface forms of medical concepts that are of the UMLS semantic class signs/symptoms. The mappings are used to extract and encode the constituent set of named entities into interoperable semantic structures that can be linked to other structured and unstructured data for reuse in research and analysis.
September 26th, 2015, by Tim Finin, posted in cybersecurity, Machine Learning, Privacy, Security
Is your personal data at risk?
App analytics to the rescue
10:30am Monday, 28 September 28 2015, ITE346
According to Virustotal, a prominent virus and malware tool, the Google Play Store has a few thousand apps from major malware families. Given such a revelation, access control systems for mobile data management, have reached a state of critical importance. We propose the development of a system which would help us detect the pathways using which user’s data is being stolen from their mobile devices. We use a multi layered approach which includes app meta data analysis, understanding code patterns and detecting and eventually controlling dynamic data flow when such an app is installed on a mobile device. In this presentation we focus on the first part of our work and discuss the merits and flaws of our unsupervised learning mechanism to detect possible malicious behavior from apps in the Google Play Store.
September 12th, 2015, by Tim Finin, posted in Security, Semantic Web
In the 14-09-2015 ebiquity meeting, Ankur Padia will talk about his recent work aimed at providing access control for an RDF triple store.
Attribute-based Fine Grained Access Control for Triple Stores
Ankur Padia, UMBC
The maturation of semantic web standards and associated web-based data representations like schema.org have made RDF a popular model for representing graph data and semi-structured knowledge. However, most existing SPARQL endpoint supports simple access control mechanism preventing its use for many applications. To protect the data stored in RDF stores, we describe a framework to support attribute-based fine grained access control and explore its feasibility. We implemented a prototype of the system and used it to carry out an initial analysis on the relation between access control policies, query execution time, and size of the RDF dataset.
For more information, see: Ankur Padia Tim Finin and Anupam Joshi, Attribute-based Fine Grained Access Control for Triple Stores, 3rd Society, Privacy and the Semantic Web – Policy and Technology workshop (PrivOn 2015), 14th Int. Semantic Web Conf., Oct. 2015.
September 1st, 2015, by Tim Finin, posted in cybersecurity, Machine Learning
Wenjia Li, Anupam Joshi and Tim Finin, SVM-CASE: An SVM-based Context Aware Security Framework for Vehicular Ad-hoc Networks, IEEE 82nd Vehicular Technology Conf., Boston, Sept. 2015.
Vehicular Ad-hoc Networks (VANETs) are known to be very susceptible to various malicious attacks. To detect and mitigate these malicious attacks, many security mechanisms have been studied for VANETs. In this paper, we propose a context aware security framework for VANETs that uses the Support Vector Machine (SVM) algorithm to automatically determine the boundary between malicious nodes and normal ones. Compared to the existing security solutions for VANETs, The proposed framework is more resilient to context changes that are common in VANETs, such as those due to malicious nodes altering their attack patterns over time or rapid changes in environmental factors, such as the motion speed and transmission range. We compare our framework to existing approaches and present evaluation results obtained from simulation studies.