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International Conference on Embedded and Ubiquitous Computing, EUC 2004

作者： Buiati, Fabio Puttini, Ricardo De Sousa, Rafael Barenco Abbas, C.J. García Villalba, L.J. University of Brasília Department of Electrical Engineering Brasília70910-900 Brazil Department of Computer Systems and Programming Complutense University of Madrid–UCM Juan del Rosal 8 DespachoMadrid431 28040 Spain

ISBN: (纸本)354022906X

In this paper we propose a secure autoconfiguration model for Manet. Our design is based in a distributed and self-organization certification service, which provides node identification and authentication for the autoconfiguration protocol. We have defined some modifications in the Dynamic Configuration and Distribution Protocol (DCDP) in order to extend the protocol functionalities to include security-aware node identification and authentication services. The overall security is also enforced with intrusion detection techniques. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Intrusion detection

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Formal specification of virtual process topologies

Formal specification of virtual process topologies

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IEEE International Conference on Cluster Computing

作者： K. Kazemi C. McDonald Programming Languages and Systems Group Department of Computer Science University of Western Australia Nedlands WA Australia

A lack of adequate and flexible topology support in the popular message passing systems such as Parallel Virtual Machine was a major factor in the development of our Virtual Process Topology Environment. This parallel programming environment provides high level abstractions for interprocess communication, relieving the application developer of the cumbersome task of mapping logical neighbours to their task identifiers within message passing systems. The novel approach of separating topological specification from the APIs provided extreme flexibility to the developers of the applications using regular topologies. We believed that the task of supporting process topologies could be made even easier and, in this paper present our new method which uses recurrence relations to define topologies. Within the new environment, the recurrence relationships can be passed to the topology server which then is used in the generation of the topological specification.

关键词： Formal specifications Message passing Libraries Network topology Workstations Supercomputers Computer languages Computer science Read only memory Network servers

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Method of Activity of Intelligent Agent for Semantic Analysis of Software Requirements

Method of Activity of Intelligent Agent for Semantic Analysi...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems

作者： Olga Pavlova Tetiana Hovorushchenko Artem Boyarchuk System Programming Department of Khmelnytsky National University Computer Engineering & System Programming Department of Khmelnytsky National University Lecturer of Computer Systems Networks and Cybersecurity Department of National Aerospace University "Kharkiv Aviation Institute"

ISBN: (纸本)9781728140704

The paper deals with the development of the method of activity and the realization of the intelligent agent (IA) for semantic analysis of the natural language software requirements. This IA performs the analysis of the requirements, determines the number and percentage of missing indicators for determining the metrics of complexity and quality of software, displays which indicators are missing for each metric, and also forms the real ontology of the domain "Software Engineering" (part "Quality and complexity of software. Metric analysis"). During an experiment, which is conducted with the help of the realized IA, the semantic analysis of requirements for the Internet of Things (IoT)-system was performed. Such analysis provides an increase of the volume and ensures of the sufficiency of metric information in the software requirements, which, in turn, provides an improvement in the quality of the software of IoT-system.

关键词： Natural language software requirements Semantic analysis Intelligent agent (IA) Ontology-based intelligent agent (OBIA) Internet of things (IoT)-system software requirements Intelligent agents Semantics Metronidazole activity INFORMATION ASSURANCE Metric computer software

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Software Anomaly Detection Method Based on Artificial Neural Network

Software Anomaly Detection Method Based on Artificial Neural...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

A growing range of data sets have been created in recent years; these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. As a result, network security in data transmission is becoming a more important area of study. One well-known method for safeguarding computer systems is the deployment of intrusion detection systems. This study proposes an artificial intelligence based method for data analysis-based anomaly detection. Methods based on machine learning and rules are mixed together. The right rules are created via a genetic algorithm. Relevant features are extracted using principal component analysis with the goal of enhancing performance. The KDD Cup 1999 dataset is used to empirically validate the suggested procedure, satisfying the criterion of using appropriate data. Using the well-known benchmark dataset, the suggested approach is used to identify and examine four different kinds of attacks: Neptune, Ipsweep, Pod, and Teardrop. During the machine learning phase, the data is categorized into categories of attacks and normal behavior after the features set during the training phase are tested. For the purpose of data analysis, the input data is divided into training and testing sets for an artificial neural network. The first 80% of the data are used to train the neural network, and the remaining 20% are used for testing. The estimated accuracy improves with the number of epochs and is higher for training data and lower for validation test data, according to experimental results. Consequently, the trained model can be retained and used to detect discrepancies. The learnt model is used to the system to compute new input parameters that are not used during training or validation.

关键词： Training Accuracy Predictive models Network security Feature extraction Data structures Data models Software Anomaly detection Principal component analysis

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Healthcare Data Analytics Based on Machine Learning

Healthcare Data Analytics Based on Machine Learning

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they enable the identification and creation of models of complicated datasets and the interactions between them. This, in turn, enables the successful prediction of future outcomes associated with a specific sickness type. An artificial intelligence-based approach to healthcare data analytics is presented, which leverages data to build a desired model and solve a particular issue. The suggested approach for healthcare data analytics uses a random forest and feedforward artificial neural network with two hidden layers as its basis to get the best model.

关键词： Analytical models Data analysis Computational modeling Medical services Artificial neural networks Learning (artificial intelligence) Data models Telecommunication computing Informatics Random forests

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Kinesthetics eXtreme: an external infrastructure for monitoring distributed legacy systems

Kinesthetics eXtreme: an external infrastructure for monitor...

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International Workshop on Active Middleware Services

作者： G. Kaiser J. Parekh P. Gross G. Valetto Department of Computer Science Programming Systems Laboratory Columbia University USA Telecom Italia Laboratory Italy

Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is widely believed to be a promising solution to ever-increasing system complexity and the spiraling costs of human system management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build ''systems of systems'' that draw from a gamut of new and legacy components involving disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand or modify the code, and in many cases even when it is impossible to recompile. We present a meta-architecture implemented as active middleware infrastructure to explicitly add autonomic services via an attached feedback loop that provides continual monitoring and, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, as well as the full infrastructure, for use with a large variety of legacy, new systems, and systems of systems. We summarize several experiments spanning multiple domains.

关键词： Computerized monitoring Computer networks Application software Middleware Costs Humans Feedback loop Software maintenance Hardware Conferences

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The constraint language for lambda structures

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Journal of Logic, Language and Information 2001年第4期10卷 457-485页

作者： Egg, Markus Koller, Alexander Niehren, Joachim Department of Computational Linguistics University of the Saarland 66041 Saarbrücken P.O. Box 15 11 50 Germany Programming Systems Lab. University of the Saarland 66041 Saarbrücken P.O. Box 15 11 50 Germany

This paper presents the Constraint Language for Lambda Structures (CLLS), a first-order language for semantic underspecification that conservatively extends dominance constraints. It is interpreted over lambda structures, tree-like structures that encode λ-terms. Based on CLLS, we present an underspecified, uniform analysis of scope, ellipsis, anaphora, and their interactions. CLLS solves a variable capturing problem that is omnipresent in scope underspecification and can be processed efficiently. © 2001 Kluwer Academic Publishers.

关键词： Anaphora Ellipsis Scope ambiguity Tree descriptions Underspecification

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Application of Machine Learning Techniques for Software Anomaly Detection

Application of Machine Learning Techniques for Software Anom...

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International Conference on Applied Mathematics & Computer Science (ICAMCS)

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network security has grown in importance as a research topic. The usage of intrusion detection systems is a well-known strategy for safeguarding computer networks. This paper proposes an anomaly detection method that blends rule-based and machine-learning-based methods. In order to construct the appropriate rules, a genetic algorithm is utilized. Principal component analysis is used to extract the relevant features aimed to improve the performance. The suggested method is validated experimentally using the KDD Cup 1999 dataset, which meets the requirement of using appropriate data. The proposed method is applied to detect and analyze four types of attacks in a well-known benchmark dataset: Neptune, Ipsweep, Pod, and Teardrop, utilizing Support Vector Machine, Decision Tree, and Naive Bayes algorithms. After testing the characteristics specified in the training phase, the data is classified into attack categories and normal behavior during the machine learning phase.

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Traffic and road sign recognition using deep convolutional neural network

Traffic and road sign recognition using deep convolutional n...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Kuchkorov, Temurbek Khamzaev, Jamshid Allamuratova, Zamira Ochilov, Temur Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Department of Computer Systems Tashkent Uzbekistan Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Joint Faculty TUIT-BSUIR Department of Information-computer Technologies and Programming Tashkent Uzbekistan

ISBN: (纸本)9781665432580

Automatic road sign recognition is one of the most important steps to help a driver prevent accidents. In this paper, the deep convolutional neural network (Deep CNN) was used for the autonomous traffic and road signs detection and recognition system. The proposed system works in real-time recognition of the image of road signs. The contribution of this article is that it consists of a newly developed datasets for 42 different road signs using combination of existing datasets and collected local road signs. The images were taken from different angles and included other parameters and conditions. More than 30K images were collected to create the dataset, which is named Uzbekistan traffic and road signs (UZ-TRS-2021). The CNN architecture was used with various parameters to achieve the best recognition speed. Experimental results show that the proposed CNN architecture achieved 98% accuracy. © 2021 IEEE.

关键词： Convolutional neural networks

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A filtered tuning method for a GPC controller

A filtered tuning method for a GPC controller

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IET Irish Signals and systems Conference (ISSC 2010)

作者： M.H. Schwarz C.S. Cox J. Börcsök Department of Computer architecture and System programming Safety Computer Technology University of Kassel Germany Control Systems Centre University of Sunderland UK Department of Computer architecture and System programming University of Kassel Germany

This paper presents a new tuning method based on model parameters identified in closed-loop. For classical controllers such as PI(D) controllers a large number of simple tuning methods for various application areas exist. However, when it comes to designing a generalised predictive controller (GPC) four parameters have to be specified. To choose those parameters is not a trivial task since they are not directly related to control or regulation performance. The presented tuning method exploits model-parameters to select suitable controller parameters. Additionally, a Rhinehart filter is incorporated in the design to decrease the impact of noise, therefore, a fifth parameter has to be optimised. The proposed method has been tested in simulation and on a real system.

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