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Generating formal specifications for security-critical applications - A model-driven approach

Generating formal specifications for security-critical appli...

引用

International Workshop on Software engineering for Secure Systems (SESS)

作者： Nina Moebius Kurt Stenzel Wolfgang Reif Department of Software Engineering and Programming Languages Institute of Computer Science University of Augsburg Augsburg Germany

ISBN: (纸本)9781424437252

The SecureMDD approach aims to generate both, a formal specification for verification and executable code, from UML diagrams. The UML models define the static as well as dynamic components of the system under development. This model-driven approach is focused on security-critical applications that are based on cryptographic protocols, especially Java Card applications. In this paper we describe the generation of the formal specification from the UML model which is then used as input for our interactive verification system KIV. The formal specification is based on abstract state machines and algebraic specifications. It allows to formulate and to prove application-specific security properties.

关键词： Formal specifications Object oriented modeling Unified modeling language Cryptographic protocols Java Security Application software Software engineering computer languages computer science

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection computer Network Intrusion Using Deep Neural Networks

Detection Computer Network Intrusion Using Deep Neural Netwo...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Vadym Poltoratskyi Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to develop a model for detecting intrusions into computer networks based on the Transformer model using tabular input data. In this work, the UNSW-NB15 dataset is used as the source data. This dataset contains information about normal network behaviour as well as during synthetic intrusions. Models for intrusion detection in computer networks based on machine learning methods were investigated: Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, Random Forest. A method of converting tabular data into images was developed, which made it possible to build intrusion detection models based on Vision Transformer and Vision Transformer for small-size datasets on modern Transformer architecture. The research results showed that developed model based on Vision Transformer and Vision Transformer for small-size datasets improves the quality of identification, and eliminates the need for a preprocessing step such as dimensionality reduction.

关键词： Support vector machines computer vision Computational modeling Intrusion detection Artificial neural networks Forestry Transformers Data models computer networks Regression tree analysis

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IoT Cyberattack Detection Approach Based on Energy Consumption Analysis

IoT Cyberattack Detection Approach Based on Energy Consumpti...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Kira Bobrovnikova Oleg Savenko Sergii Lysenko Ivan Hurman Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine

ISBN: (纸本)9798350333053

Today, the Internet of Things has been widely used and is becoming an increasingly vital part in various areas of human activity, which helps to effectively solve various real-life tasks and problems. The infrastructure of the Internet of Things includes a lot of heterogeneous devices with limited capacities, which makes it difficult to control them in order to detect cyberattacks. The increase in the number of attacks on the infrastructure of the Internet of Things indicates the relevance of developing new solutions for detecting attacks on the networks of the Internet of Things. One of the possible approaches to identify attacks is to analyze the physical parameters associated with IoT devices that describe the behavior of devices on the network, such as power consumption. Power analysis allows you to detect power-hungry attacks such as crypto mining and denial-of-service attacks. The paper proposes a new method for cyberattack detection in the infrastructure of the Internet of Things that is based on the analysis of energy consumption. In order to improve the accuracy of detecting cyberattacks, the approach also uses the analysis of the actions of the IoT software. The proposed approach makes it possible to detect attacks such as DoS/DDoS with high efficiency with a level of detection up to 99.95%.

关键词： Energy consumption Power demand Software Behavioral sciences Internet of Things Object recognition Data mining

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Heterogeneous Ensemble Classifier in Medical Decision Support Systems

Heterogeneous Ensemble Classifier in Medical Decision Suppor...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Anatol y Povoroznyuk Oksana Povoroznyuk Anna Filatova Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two approaches to formulating a diagnostic conclusion: a probabilistic one based on the analysis of the training sample, and expert information on the structure of symptom complexes. The choice of prototype matching method as a probabilistic component is justified. Formalization of expert information on the structure of symptom complexes was carried out by representing symptom complexes of diseases with numerical intervals of linguistic variables. Options for taking into account expert assessments about the structure of symptom complexes in an ensemble classifier are considered. Test verification of the developed classifier was done on real medical data and confirmed the effectiveness of its work.

关键词： Decision support systems Training Analytical models Prototypes Linguistics Probabilistic logic Medical diagnostic imaging Diseases

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Spam Detection With Fasttext Based Features

Spam Detection With Fasttext Based Features

引用

Innovations in Intelligent Systems and Applications Conference

作者： Talha Karadeniz Gül Tokdemir H. Hakan Maraş Department of Software Engineering Çankaya University Ankara Turkey Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Programming Çankaya University Ankara Turkey

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative; mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification.

关键词： Technological innovation Accuracy Unsolicited e-mail Computational modeling Feature extraction Vectors Intelligent systems Standards Postal services

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Product of interval-valued fuzzy graphs and degree

Product of interval-valued fuzzy graphs and degree

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作者： Rashmanlou, Hossein Pal, Madhumangal Borzooei, Rajab Ali Mofidnakhaei, F. Sarkar, Biswajit Department of Computer Science University College of Adib Sari Iran Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Mathematics Shahid Beheshti University Tehran Iran Department of Physics Sari Branch Islamic Azad University Sari Iran Department of Industrial and Management Engineering Hanyang University Erica Campus Ansan Korea Republic of

Theoretical concepts of graphs are highly utilized by computer science applications. Especially in research areas of computer science such as data mining, image segmentation, clustering, image capturing and networking. The interval-valued fuzzy graphs are more flexible and compatible than fuzzy graphs due to the fact that they have many applications in networks. In this paper, at first we define three new operations on interval-valued fuzzy graphs namely strong product, tensor product and lexicographic product. Likewise, we study about the degree of a vertex in interval-valued fuzzy graphs which are obtained from two given interval-valued fuzzy graphs using the operations Cartesian product, composition, tensor and strong product of two interval-valued fuzzy graphs. These operations are highly utilized by computer science, geometry, algebra, number theory and operation research. In addition to the existing operations these properties will also be helpful to study large interval-valued fuzzy graph as a combination of small, interval-valued fuzzy graphs and to derive its properties from those of the smaller ones. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Image segmentation

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Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem 8

Performance Evaluation of Metaheuristic Optimization Techniq...

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8th International Conference on Information Technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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Statistical Analysis of Capacity Loss for Stored Batteries 15

Statistical Analysis of Capacity Loss for Stored Batteries

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Constantinescu, Luminita-Mirela Enache, Bogdan-Adrian Dogaru, Violeta-Georgiana Voicila, Teodor Iulian Vilciu, Irina Vasile, Silvia-Carmen University of Pitesti Electronics Computer and Electrical Engineering Department Pitesti Romania University Politehnica of Bucharest Measurements Electrical Apparatus and Static Converters Department Bucharest Romania University Politehnica of Bucharest Electrical Engineering Faculty Bucharest Romania Bucharest Transelectrica S.A Programming and Planning Department for the Teritorial Dispatch Center Bucharest Romania

ISBN: (纸本)9798350321388

Lithium-ion battery production is generally geared towards current demand from the main sectors: electric vehicles, consumer electronics, and energy storage. These sectors typically require the batteries to be as fresh as possible to ensure maximum lifespan and performance. Still, sometimes manufacturers work with buffer stocks and deliver batteries from different production series. In this context, the present paper examines stored batteries' capacity loss, employing an exhaustive statistical study. This study aims to establish if the capacity loss is statistically significant and can lead to battery pack unbalances. © 2023 IEEE.

关键词： Battery Pack

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Authentication and autoconfiguration for MANET nodes

Authentication and autoconfiguration for MANET nodes

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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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