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Intelligent Expert systems Application for Structuring Educational Materials to Enhance the Quality of Learning Outcomes 13

Intelligent Expert Systems Application for Structuring Educa...

引用

13th IEEE International conference on Electronics and Information Technologies, ELIT 2023

作者： Ivanyshyn, Alla Zahorodniuk, Tetiana Maliarenko, Valentyna Sus, Bohdan Zagorodnyuk, Sergiy Bauzha, Oleksandr Boyko, Oksana Department of Information Measuring Technologies Lviv Polytechnic National University Lviv Ukraine Preparatory Department Taras Shevchenko National University of Kyiv Kyiv Ukraine Institute of Biology and Medicine Taras Shevchenko National University of Kyiv Kyiv Ukraine Institute of High Technologies Taras Shevchenko National University of Kyiv Kyiv Ukraine Faculty of Radio Physics Electronics and Computer Systems Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Medical Informatics Danylo Halytsky Lviv National Medical University Lviv Ukraine

ISBN: (纸本)9798350383096

Application of intelligent expert systems for structuring and automation of laboratory activities in the educational process has been analysed. It is shown that combination of teaching materials with cross-references and transitions by the means of expert systems allows educators to make optimal decisions on time, set priorities and prepare more effective laboratory classes. The implementation of expert systems for classification sections of academic disciplines in both engineering and technical sciences and humanities has been discussed. An algorithm for making decisions by an expert system, where the main emphasis is given to laboratory tasks and work with laboratory equipment, is proposed. It is shown how to develop a new expert system which can help university educators prepare remote laboratory work, and also to use of virtual simulators or effective practical training. The proposed expert systems can be used for classifying thematic sections of a wide range of disciplines, including natural sciences, engineering, and humanities. They can be used for preparing new courses and training new educators in different areas. Thus expert systems are described as high-scale software units without restrictions on the depth of the question tree and the number of logical branches of the classifier. © 2023 IEEE.

关键词： Expert systems

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IoT Device Authentication via RAM Trace Analysis: A Representation Learning Framework

IoT Device Authentication via RAM Trace Analysis: A Represen...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing Nebraska United States

ISBN: (纸本)9798350351255

Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a pervasive proliferation of connected devices, ranging from high-power computers to low-power sensors. Yet, the complex IoT architecture poses numerous vulnerabilities, demanding robust security measures. Existing firmware attestation techniques often encounter obstacles due to proprietary constraints, necessitating access to the device's authentic firmware. To address this challenge, this paper proposes a novel software-based attestation framework that utilizes RAM traces from IoT devices for remote verification. By employing deep learning models trained in a representation learning paradigm, our framework empowers the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, our approach achieves an impressive 100% detection rate for critical attacks on IoT devices with a false positive rate of 10-3. Remarkably, our framework preserves device availability and maintains low authentication latency, highlighting its efficacy and practicality for securing IoT ecosystems. © 2024 IEEE.

关键词： Firmware

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Adaptive dynamic programming control algorithm and simulation software design 3

Adaptive dynamic programming control algorithm and simulatio...

引用

2022 3rd International conference on computer Information and Big Data Applications, CIBDA 2022

作者： Ma, Runmin Xia, Xiaonan School of Computer Sciences and Engineering Northeastern University Shenyang110169 China Department of Automation College of Information Engineering Yangzhou University Yangzhou225127 China

ISBN: (纸本)9783800758760

In this paper, an event-triggered adaptive dynamic programming tracking control scheme is investigated for a class of nonlinear systems with unmodeled dynamics based on dynamic surface control method. Due to considering the ummodeled dynamics, the feedback control consists of two controllers. One is used to compensate the unmodeled dynamics and system functions, and the other is used to optimize the system performance. Based on the control scheme, a parameter setting and real-time simulation system is developed by using Matlab GUI and M language. The system provides the functions of solving the optimal controller, tuning the controller parameters and real-time simulation of the control system. Through this software, the design parameters can be set repeatedly online and the control results can be observed in real time. The main results of algorithm design and simulation software are given in this paper. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Controllers

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Detect and Mitigate Cyberattacks Using SIEM 16

Detect and Mitigate Cyberattacks Using SIEM

引用

16th International conference on Developments in esystems engineering, DeSE 2023

作者： Younus, Zeyad Safaa Alanezi, Mafaz Univ. of Mosul College of Computer Science and Mathematics Dept. of Software Mosul Iraq Univ. of Mosul College of Computer Science and Mathematics Dept. of Computer Science Mosul Iraq Univ. of Mosul Ict Research Unit Computer Center Mosul Iraq

ISBN: (纸本)9798350381344

Cybersecurity is crucial to protect network resources and reduce the risk of cyberattacks like denial of service (DOS) and file modification. Security tools like intrusion detection and prevention systems (IDS and IPS), which work independently on network endpoints, struggle to distinguish between normal and anomalous network traffic in real-time, making cyberattack detection and prevention challenging. As a result, security information and event management (SIEM), which offers centralized network management, is utilized to gather and analyze logs from various endpoints on networks in order to identify threats in real time. This paper introduces a method integrating Wazuh and IDS tool capabilities to improve DOS detection and mitigation, and Wazuh file integrity monitoring (FIM) capabilities for real-time file modification detection. Wazuh monitors and evaluates network resource status centrally, while Snort and Suricata tools are used to monitor the network traffic on the monitored endpoint. After that, logs are sent to the Wazuh server for analysis and alerting. In addition, Wazuh is used to detect modifications to critical files, enabling early detection of malicious changes. The effectiveness of the suggested method is tested using a variety of DOS tools that are produced in real-time and by modifying important files. The experimental findings demonstrate that our method is effective in detecting and mitigating cyberattacks. © 2023 IEEE.

关键词： Cybersecurity

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Performance Comparison of Different Digital Image Filters Used for Biomedical Signals 8th

Performance Comparison of Different Digital Image Filters U...

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8th International conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Sudagani Jyothi Muthu Krishnammal, P. School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819757855

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. MATLAB is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like mean, median, weighted average, Gaussian, and bilateral in Python to verify their performance in terms of mean square error (MSE) and similarity index (SI), a suitable filter can be selected for biomedical applications by comparing their performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Multi-Population Genetic Algorithm Based EFSM Regression Test Data Generation 23

Multi-Population Genetic Algorithm Based EFSM Regression Tes...

引用

23rd IEEE International conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Li, Jiahao Zhang, Bo Fan, Zeyu Wang, Yichen School of Reliability and Systems Engineering Beihang University Beijing China Tianjin University Computer Science and Technology Division of Intelligence and Computing China

ISBN: (纸本)9798350359398

Extended Finite State Machine(EFSM) has a wide range of application systems and protocols in control modeling. Genetic algorithms are commonly used in EFSM test data generation due to their good performance. Studies have been done to generate regression test sequences to meet the requirements based on changes in EFSM, there is a large amount of historical test data to choose from when using genetic algorithms to generate test data for this part of the test sequence, and there is no research on how to fully utilize the existing test data to generate regression test data for the EFSM. In this paper, a multi-population genetic algorithm is designed to generate test data to meet the requirements. Firstly, some test data that can cover the regression test sequences are selected from the historical test data, and for the rest of the test sequences, a mathematical model for the simultaneous optimization of multiple test sequences is constructed, secondly, in order to make full use of the historical test data, the relationship between the existing test data and the target test sequences is investigated, and the initial population is constructed based on the similarity of the test sequences and the FSCS-ART algorithm, which makes full use of the The initial population is constructed based on the similarity of test sequences and the FSCS-ART algorithm, which makes full use of the existing test data and ensures the diversity of the initial population. Finally, the adaptive crossover operator and variation operator were designed and the fitness function was optimized. The experimental results show the feasibility and efficiency of this method in test data generation. © 2023 IEEE.

关键词： software testing

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Malware Analysis Types & Techniques : A Survey 5

Malware Analysis Types & Techniques : A Survey

引用

5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Arora, Priya Gupta, Rashmi Malik, Nidhi Kumar, Anil Amity University Haryana Gurugram India The North Cap University Haryana Gurugram India Poornima Institute of Engineering and Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

Malware is like a digital troublemaker. It's a type of software that sneaks into computers and gadgets with harmful objectives. It can cause problems by stealing in- formation, damaging files, or making devices act strangely without the owner knowing. Just like a virus can make a person sick, malware can make a computer sick and harm its proper functioning. Malware analysis plays an essential role in cybersecurity by enabling the understanding, detection, and mitigation of malicious software threats. Through careful examination of malware's behavior, code, and techniques, analysts un- cover vulnerabilities, identify attack vectors, and develop effective countermeasures. This process enhances the ability to safeguard digital ecosystems, boost networks, and protect sensitive data, thereby preventing cyberattacks and preserving the integrity of both individual systems and broader technological infrastructures. This research paper primarily centers on the topic of malware, exploring its various forms. It delves into two generations of malware: the first generation, which includes viruses, worms, and spyware, among others;and the second generation, encompassing encrypted, oligomorphic, polymorphic, and metamorphic malware. By examining these different categories, researchers will be able to achieve a deeper intelligence of the evolving landscape of malicious software and its potential impacts. This paper also delves into the fundamental concepts of malware analysis, encompassing static and dynamic and hybrid approaches. It explores various detection techniques like signature-based detection, heuristic analysis, behavioral analysis, and machine learning techniques. By explaining each technique's strengths and limitations, this paper aids researchers, practitioners, and cybersecurity professionals in selecting suitable approaches based on the malware's characteristics and the analysis objectives. © 2023 ACM.

关键词： Learning systems

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Method of Signal Detection for NOMA systems Based on Convolutional Neural Network

Method of Signal Detection for NOMA Systems Based on Convolu...

引用

International conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhao, Tong Zhao, Shipeng School of Computer and Software Dalian Neusoft University of Information Liaoning Dalian116026 China School of Information Science and Technology Dalian Maritime University Liaoning Dalian116026 China

ISBN: (纸本)9789819639564

The employment of non-orthogonal multiple access (NOMA) technology represents an innovative approach in enhancing spectrum efficiency and bolstering user access capabilities, which acts as a pivotal technology within 5G and future maritime intelligent transport applications. With numbers of users increasing significantly, complexity of implementation increases dramatically if traditional serial interference cancellation (SIC) technology is used for signal detection. Aiming at error propagation problem in the decoding process of the traditional SIC receiver, a novel downlink dual-user NOMA signal detection scheme based on convolutional neural network (CNN) is proposed in this paper. The CNN-based scheme can directly process the NOMA signals received at the receiver without relying on the traditional SIC receiver. Through the training and optimization of the neural network model, the accurate detection of NOMA signals has been achieved, and the technical advantages of this scheme in improving system transmission rate and reducing error propagation have been evaluated. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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An Intelligent-Safe Network Traffic Distribution System in Cluster Mesh-Formations of Thermal-Technology Complexes 1

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2nd International conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2022

作者： Lazarev, Alexey Vereykina, Elizabeth Sinyavskiy, Viktor National Research University "Moscow Power Engineering Institute" in Smolensk 1 Energy Passage Smolensk214013 Russia National Research University "Moscow Power Engineering Institute" 14 Krasnokazarmennaya Moscow111250 Russia

ISBN: (数字)9783031313530

ISBN: (纸本)9783031313523

Nowadays, means of communication development exchange affects the vast majority of industries in the world, in particular, data exchange in thermal technology systems. The rapid development of communications makes it possible to solve a number of automated exchange problems. However, insufficient development of intellectual separation cluster formations means and flexible traffic redirection leaves the safe management problem of technological complexes relevant. The proposed solution to the highlighted problem is the software, based on a universal flexible topology of network segments, the usage of which, in the aggregate with an intelligent model for predicting network traffic loads in thermal technology complexes, would allow to implement a secure remote network interaction and critical information exchange to ensure stable operation of complex technical equipment. An essential feature of this system is the secure access to the required information due to direct p2p exchange between clients using a secure tunnel. Among other features of the system there is the encryption data approach using dynamic binary comparison in combination with methods of generating one-time pairs without an active internet connection to initiate a local p2p connection is highlighted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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Granular Network Traffic Classification on Mobile Application Service for Better Parental Control 2

Granular Network Traffic Classification on Mobile Applicatio...

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2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dhivvya, J.P. Aswini Dath, Guru Chinta, Mahesh Kumar Karki, Ankit Amrita Vishwa Vidyapeetham Amrita School of Computing Center for Wireless Networks and Applications Department of Computer Science and Engineering Kollam Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Kollam Amritapuri India

ISBN: (纸本)9798350360523

The widespread use of mobile applications among children had its limitations in excessive screen usage in entertainment. Traditional parental control techniques mostly depend on blocking applications based on their name or categories and are ineffective at identifying potentially new dangerous applications. This study addresses this issue by introducing a paradigm shift for protecting kids' digital experiences through granular network traffic classifications. This system classifies mobile app network traffic into various groups by implementing machine learning or deep learning models. Machine learning models like linear regression, Adaboost Classifier, Support Vector Classification (SVC), Ridge Classifier and deep learning models like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) were used to identify and classify mobile applications. Initially, the deep learning RNN model results had 80% accuracy in the classification of mobile apps, and the machine learning logistic SVC and Adaboost Classifier had 72% accuracy. After adding new applications to the existing dataset, the accuracy of the RNN model has remained the same 80% accuracy. Meanwhile, the machine learning model accuracy reduced to 70%, proving the flexibility of the system. This work can assist parents in monitoring their kids to use mobile applications more for education. This work has a huge scope for information technology management of social change using hybrid learning models in software-defined networking in local networks for the administrators to empower their users to wisely use the internet. © 2024 IEEE.

关键词： Learning systems

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