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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Gavrylenko, Svitlana Vladislav, Zozulia Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors. © 2023 IEEE.

关键词： Classification (of information)

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Runtime Verification of Autonomous Systems Utilizing Digital Twins as a Service 5

Runtime Verification of Autonomous Systems Utilizing Digital...

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5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2024

作者： Kristensen, Morten Haahr Bonizzi, Alberto Gomes, Claudio Hansen, Simon Thrane Isasa, Carlos Iven, Hannes Kamburjan, Eduard Larsen, Peter Gorm Leucker, Martin Talasila, Prasad Tang, Valdemar Trojgard Tonetta, Stefano Vosteen, Lars B. Wright, Thomas Aarhus University Department of Electrical and Computer Engineering Denmark Fondazione Bruno Kessler Italy University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Luxembourg Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany University of Oslo Department of Informatics Norway

ISBN: (纸本)9798350389760

Autonomous Systems (AS) enable systems to adapt to drastic and unprecedented environmental changes, a capability that can be enhanced through the utilization of Digital Twins (DTs). However, the additional capabilities of AS come at the cost of explainability, as the expanding adaptation space complicates the reasoning about the system's behavior. For certain types of systems, it is crucial to ensure that specific properties are upheld despite the system's autonomous behavior. To facilitate the monitoring of these properties, we propose the use of Runtime Verification (RV). This tutorial demonstrates the integration of RV tools into the Digital Twins as a Service (DTaaS) platform to monitor and verify the behavior of AS in real-time. By exploring various methods to incorporate RV tools within a DT context, the tutorial aims to advance the application of RV technologies in autonomic computing and self-adaptive system design. Specifically, we demonstrate how the behavior of a self-configuring DT can be verified utilizing RV. This is accomplished through the DTaaS platform, which supports seamless deployment of DT-based AS. © 2024 IEEE.

关键词： Adaptive systems

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Application-based spam detection with machine learning algorithms 27

Application-based spam detection with machine learning algor...

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27th International Conference on Information Society and University Studies, IVUS 2022

作者： Erbey, Ali Barışçı, Necaattin Uşak University Distance Education Vocational School Department of Computer Programming Uşak Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

We investigated the process of unsupervised generative learning and the structure of informative generative representations of images of handwritten digits (MNIST dataset). Learning models with the architecture of sparse convolutional autoencoder with constraints to produce low-dimensional representations achieved successful generative learning demonstrated by high accuracy of generation of images. A well-defined, continuous and connected structure of generative representations was observed and described. Structured informative representations of unsupervised generative models can be an effective platform for investigation of origins of intelligent behaviors in artificial and biological learning systems. © 2022 Copyright for this paper by its authors.

关键词： Neural networks

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Social engineering Attacks Detection Approach 13

Social Engineering Attacks Detection Approach

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Models and algorithms for determining the probability of occurrence of a critical combination of events for three-element minimum sections in intelligent decision support systems 4

Models and algorithms for determining the probability of occ...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Kolomiitsev, Oleksii Osiievskyi, Serhii Zakharchenko, Iryna Rybalchenko, Alina Tretiak, Viacheslav Heiko, Hennadii Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Mathematics and Software of ACS Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

A method for determining the probability of occurrence of a critical combination of events for the three-element minimum cross-sections of attributes of the characteristics of the software quality model of intelligent... 详细信息

ISBN: (纸本)9798350395532

关键词： Decision support systems

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Academic Fraud Detection in Online Exams with DNN's Multilayer Model

Academic Fraud Detection in Online Exams with DNN's Multilay...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Bahaddin Erdem Murat Karabatak Department of Computer Programming Bitlis Eren University Bitlis Turkey Department of Software Engineering Firat University Elaziğ Turkey

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who “cheated” from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty.

关键词： Deep learning Analytical models Digital forensics Predictive models Nonhomogeneous media Data models Educational courses Fraud Security

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Spam Detection With Fasttext Based Features

Spam Detection With Fasttext Based Features

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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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Optimistic Versioning for Conflict-tolerant Collaborative Blended Modeling 10

Optimistic Versioning for Conflict-tolerant Collaborative Bl...

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STAF 2022 Workshop: 10th International Workshop on Bidirectional Transformations, BX 2022, 2nd International Workshop on Foundations and Practice of Visual Modeling, FPVM 2022 and 2nd International Workshop on MDE for Smart IoT Systems, MeSS 2022

作者： Exelmans, Joeri Pietron, Jakob Raschke, Alexander Vangheluwe, Hans Tichy, Matthias Department of Computer Science University of Antwerp - Flanders Make Antwerp Belgium Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

Optimistic versioning is a key component in supporting collaborative workflows. Text-based versioning has been widely adopted for versioning code, but in model-driven engineering, dealing with visual concrete syntaxes, new methods are required. In the case of blended modeling, a mixture of both textual and visual syntaxes, concurrently editable and synchronizable, introduces additional challenges. We propose a type of operation-based versioning to record not only user edits, but also bi-directional change propagations between concrete and abstract syntax. This way we can support blended modeling with layout continuity, and flexible handling of missing information (e. g., layout information) when rendering changes from abstract to concrete syntax. In addition, the proposed versioning approach enables collaborative conflict resolution by allowing partial conflict resolution, thus deferring a final resolution. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Abstracting

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