4 research outputs found
A comparative experimental design and performance analysis of Snort-based Intrusion Detection System in practical computer networks
As one of the most reliable technologies, network intrusion detection system (NIDS) allows the monitoring of incoming and outgoing traffic to identify unauthorised usage and mishandling of attackers in computer network systems. To this extent, this paper investigates the experimental performance of Snort-based NIDS (S-NIDS) in a practical network with the latest technology in various network scenarios including high data speed and/or heavy traffic and/or large packet size. An effective testbed is designed based on Snort using different muti-core processors, e.g., i5 and i7, with different operating systems, e.g., Windows 7, Windows Server and Linux. Furthermore, considering an enterprise network consisting of multiple virtual local area networks (VLANs), a centralised parallel S-NIDS (CPS-NIDS) is proposed with the support of a centralised database server to deal with high data speed and heavy traffic. Experimental evaluation is carried out for each network configuration to evaluate the performance of the S-NIDS in different network scenarios as well as validating the effectiveness of the proposed CPS-NIDS. In particular, by analysing packet analysis efficiency, an improved performance of up to 10% is shown to be achieved with Linux over other operating systems, while up to 8% of improved performance can be achieved with i7 over i5 processors
V-ROOM: a virtual meeting system with intelligent structured summarisation
With the growth of virtual organisations and multinational companies, virtual collaboration tasks are becoming more important for employees. This paper describes the development of a virtual meeting system, called V-ROOM. An exploration of facilities required in such a system has been conducted. The findings highlighted that intelligent systems are needed, especially since information that individuals have to know and process, is vast. The survey results showed that meeting summarisation is one of the most important new features that should be added to virtual meeting systems for enterprises. This paper highlights the innovative methods employed in V-ROOM to produce relevant meeting summaries. V- ROOM's approach is compared to other methods from the literature and it is shown how the use of meta-data provided by parts of the V-ROOM system can improve the quality of summaries produced
Air pollution forecasts: An overview
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies
Semantic framework for regulatory compliance support
Regulatory Compliance Management (RCM) is a management process, which an organization
implements to conform to regulatory guidelines. Some processes that contribute towards
automating RCM are: (i) extraction of meaningful entities from the regulatory text and (ii)
mapping regulatory guidelines with organisational processes. These processes help in updating
the RCM with changes in regulatory guidelines. The update process is still manual since there
are comparatively less research in this direction. The Semantic Web technologies are potential
candidates in order to make the update process automatic. There are stand-alone frameworks
that use Semantic Web technologies such as Information Extraction, Ontology Population,
Similarities and Ontology Mapping. However, integration of these innovative approaches in
the semantic compliance management has not been explored yet. Considering these two
processes as crucial constituents, the aim of this thesis is to automate the processes of RCM. It
proposes a framework called, RegCMantic.
The proposed framework is designed and developed in two main phases. The first part of the
framework extracts the regulatory entities from regulatory guidelines. The extraction of
meaningful entities from the regulatory guidelines helps in relating the regulatory guidelines
with organisational processes. The proposed framework identifies the document-components
and extracts the entities from the document-components. The framework extracts important
regulatory entities using four components: (i) parser, (ii) definition terms, (iii) ontological
concepts and (iv) rules. The parsers break down a sentence into useful segments. The
extraction is carried out by using the definition terms, ontological concepts and the rules in the
segments. The entities extracted are the core-entities such as subject, action and obligation, and
the aux-entities such as time, place, purpose, procedure and condition.
The second part of the framework relates the regulatory guidelines with organisational
processes. The proposed framework uses a mapping algorithm, which considers three types of
Abstract
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entities in the regulatory-domain and two types of entities in the process-domains. In the
regulatory-domain, the considered entities are regulation-topic, core-entities and aux-entities.
Whereas, in the process-domain, the considered entities are subject and action. Using these
entities, it computes aggregation of three types of similarity scores: topic-score, core-score and
aux-score. The aggregate similarity score determines whether a regulatory guideline is related
to an organisational process.
The RegCMantic framework is validated through the development of a prototype system. The
prototype system implements a case study, which involves regulatory guidelines governing the
Pharmaceutical industries in the UK. The evaluation of the results from the case-study has
shown improved accuracy in extraction of the regulatory entities and relating regulatory
guidelines with organisational processes. This research has contributed in extracting
meaningful entities from regulatory guidelines, which are provided in unstructured text and
mapping the regulatory guidelines with organisational processes semantically