Khalid Latif

Semantic security against web application attacks

In this paper, we propose a method of detecting and classifying web application attacks. In contrast to current signature-based security methods, our solution is an ontology based technique. It specifies web application attacks by using semantic rules, the context of consequence and the specifications of application protocols. The system is capable of detecting sophisticated attacks effectively and efficiently by analyzing the specified portion of a user request where attacks are possible. Semantic rules help to capture the context of the application, possible attacks and the protocol that was used. These rules also allow inference to run over the ontological models in order to detect, the often complex polymorphic variations of web application attacks. The ontological model was developed using Description Logic that was based on the Web Ontology Language (OWL). The inference rules are Horn Logic statements and are implemented using the Apache JENA framework. The system is therefore platform and technology independent. Prior to the evaluation of the system the knowledge model was validated by using OntoClean to remove inconsistency, incompleteness and redundancy in the specification of ontological concepts. The experimental results show that the detection capability and performance of our system is significantly better than existing state of the art solutions. The system successfully detects web application attacks whilst generating few false positives. The examples that are presented demonstrate that a semantic approach can be used to effectively detect zero day and more sophisticated attacks in a real-world environment.

Change management in evolving web ontologies

Knowledge constantly grows in scientific discourse and is revised over time by different stakeholders, either collaboratively or through institutionalized efforts. The body of knowledge gets structured and refined as the Communities of Practice concerned with a field of knowledge develop a deeper understanding of the issues. As a result, the knowledge model moves from a loosely clustered terminology to a semi-formal or even formal ontology. Change history management in such evolving knowledge models is an important and challenging task. Different techniques have been introduced in the research literature to solve the issue. A comprehensive solution must address various multi-faceted issues, such as ontology recovery, visualization of change effects, and keeping the evolving ontology in a consistent state. More so because the semantics of changes and evolution behavior of the ontology are hard to comprehend. This paper introduces a change history management framework for evolving ontologies; developed over the last couple of years. It is a comprehensive and methodological framework for managing issues related to change management in evolving ontologies, such as versioning, provenance, consistency, recovery, change representation and visualization. The Change history log is central to our framework and is supported by a semantically rich and formally sound change representation scheme known as change history ontology. Changes are captured and then stored in the log in conformance with the change history ontology. The log entries are later used to revert ontology to a previous consistent state, and to visualize the effects of change on ontology during its evolution. The framework is implemented to work as a plug-in for ontology repositories, such as Joseki and ontology editors, such as Protege. The change detection accuracy of the proposed system Change Tracer has been compared with that of Changes Tab, Version Log Generator in Protege; Change Detection, and Change Capturing of NeOn Toolkit. The proposed system has shown better accuracy against the existing systems. A comprehensive evaluation of the methodology was designed to validate the recovery operations. The accuracy of Roll-Back and Roll-Forward algorithms was conducted using different versions of SWETO Ontology, CIDOC CRM Ontology, OMV Ontology, and SWRC Ontology. Experimental results and comparison with other approaches shows that the change management process of the proposed system is accurate, consistent, and comprehensive in its coverage.

Ontology based semantic information retrieval

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries. The main drawback of the existing semantic-based information retrieval techniques is that none of them considers the context of the concept(s). We propose a semantic information retrieval framework to improve the precision of search results. In this paper, thematic similarity approach is employed for information retrieval in order to capture the context of particular concept(s). We store metadata information of source(s) in the form of RDF triples. We search userpsilas queries in the existing metadata by matching RDF triples instead of keywords. The results of the experiments performed on our framework showed improvements in precision and recall compared to the existing semantic-based information retrieval techniques.

Ontology Evolution: A Survey and Future Challenges

Ontology used in many Information Systems and Knowledge Sharing Systems to represent the domain knowledge. As use of ontology increased significantly in recent years that gives importance to proper maintenance of ontology. Ontology change management is a multifaceted and complex task incorporating research areas like; ontology engineering, evolution, versioning, merging, integration, and maintenance. Ontology evolves from one state to another state in response to the changes requested. Crucial task is how to accommodate the new changes while preserving its consistency. This paper provides state of the art analysis of existing approaches covering ontology evolution, and their critical analysis. Pending/Unsolved challenges that need to be address in order to get the process done automatically are also discussed.

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.

Managing Change History in Web Ontologies

Ontologies changes in the sense of constantly growing in scientific discourse and being revised over time by different people. It refers to the fact that groups of professionals over time (e.g. in a longer term project) develop a common understanding based on joint interpretation of a shared terminology. The terminology will get structured and refined as the Communities of Practice concerned with a field of knowledge develop a deeper understanding of issues, thus moving from a loosely clustered terminology to a semi-formal or even formal ontology.Keeping trail of these changes in semantically rich and formally sound mechanism, on one hand, has pragmatic advantages for providing the undo and redo facility and to recover to a previous state of ontology. On the other hand, it also contributes in understanding the change process and identifying the patterns of changes in the ontologies. This research aims at developing semantic structure for comprehensively storing ontology changes, and the application of the stored changes. For change history management in evolving ontologies, we have modeled an ontology for change history.

Mapping evolution of dynamic web ontologies

Information on the web and web services that are revised by stakeholders is growing incredibly. The presentation of this information has shifted from a representational model of web information with loosely clustered terminology to semi-formal terminology and even to formal ontology. Mediation (i.e., mapping) is required for systems and services to share information. Mappings are established between ontologies in order to resolve terminological and conceptual incompatibilities. Due to new discoveries in the field of information sharing, the body of knowledge has become more structured and refined. The domain ontologies that represent bodies of knowledge need to be able to accommodate new information. This allows for the ontology to evolve from one consistent state to another. Changes in resources cause existing mappings between ontologies to be unreliable and stale. This highlights the need for mapping evolution (regeneration) as it would eliminate the discrepancies from the existing mappings. In order to re-establish the mappings between dynamic ontologies, the existing systems require a complete mapping process to be restructured, and this process is time consuming. This paper proposes a mapping reconciliation approach between the updated ontologies that has been found to take less time to process compared to the time of existing systems when only the changed resources are considered and also eliminates the staleness of the existing mappings. The proposed approach employs the change history of ontology in order to store the ontology change information, which helps to drastically reduce the reconciliation time of the mappings between dynamic ontologies. A comprehensive evaluation of the performance of the proposed system on standard data sets has been conducted. The experimental results of the proposed system in comparison with six existing mapping systems are provided in this paper using 13 different data sets, which support our claims.

Formal reliability analysis of a typical FHIR standard based e-Health system using PRISM

Fast Health Interoperable Resources (FHIR) is the recently proposed standard from HL7. Its distinguishing features include the user friendly implementation, support of built-in terminologies and for widely-used web standards. Given the safety-critical nature of FHIR, the rigorous analysis of e-health systems using the FHIR is a dire need since they are prone to failures. As a first step towards this direction, we propose to use probabilistic model checking, i.e., a formal probabilistic analysis approach, to assess the reliability of a typical e-health system used in hospitals based on the FHIR standard. In particular, we use the PRISM model checker to analyze the Markov Decision Process (MDP) and Continuous Time Markov Chain (CTMC) models to assess the failure probabilities of the overall system.

Process interoperability in healthcare systems with dynamic semantic web services

Healthcare systems are very complex due to extreme heterogeneity in their data and processes. Researchers and practitioner need to make systems interoperable and integrate for the benefit of all the stakeholders including hospitals, clinicians, medical support staff, and patients. The broader goal of interoperability can only be achieved when standards are practiced.Two different healthcare systems can earn HL7 conformance and compliance but at the same time can be incompatible for interoperability because of varying implementation of HL7 interaction model. This is mainly because workflows in healthcare systems are very complex. Interoperability on one hand requires flexible mechanism for the mapping of business processes to a standard, HL7 in our example. On the other hand it requires deeper understanding of the standard interaction model and gaps created by their incompatible implementations. In this paper we propose a novel technique of dynamically creating semantic web services as overlay on top of the existing services. We used Web Service Modeling Framework as an underlying architecture for HL7 process artifacts implementation as semantic web services. These semantic services are mapped to our proposed interaction ontology. Integrated reasoning mechanism provides necessary execution semantics for more effective and seamless end-to-end communication.The prototype we tested on different processes from the laboratory domain at a local diagnostic laboratory with uninterrupted process flow. The scenario of Result Query Placer interaction flow and its associated process artifacts are executed for the proof of concept.The proposed solution complements the existing data interoperability in HL7 and leads to semantic process interoperability. The achievement of semantic interoperability results in timely delivery of healthcare services to patients saving precious lives.

Achieving interoperability among healthcare standards: building semantic mappings at models level

Resolving heterogeneities between data and processes paves the way for interoperability between different heterogeneous systems. Healthcare standards provide the base for interoperability between different Electronic Health Record (EHR) system. The problems related to data interoperability arise when two EHR systems are complaint to heterogeneous healthcare standards and want to communicate with each other. To achieve semantic data interoperability, there is need to resolve data level heterogeneity. In this paper, we propose system that enable high level of accuracy of mapping between heterogeneous healthcare standards model. The broader goal of data interoperability is achieved when these heterogeneities are resolved through ontology matching and generation of accurate mapping file, that helps in clinical message conversion from one standard to another. To justify claim we investigate HL7 and openEHR standards ontological mappings. We will discuss transformation of HL7 and openEHR models at high level and instance transformation at the realization level. The proposed approach provides accurate mappings that enables timely health information sharing among different healthcare systems to provide better healthcare to patients.