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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dep.ndencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

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Information Technology (ACIT)

作者： Rabia Emhamed Al Mamlook Ahmad Nasayreh Hasan Gharaibeh Mohammad Aljaidi Qais Al-Na'amneh Dalia Alzu'bi Hanin Bzizi Abdelrahman Abdeltawab Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada Dep of Biomedical Science Western Michigan University Kalamazoo MI USA Faculty of pharmacy Bani Sweif university Bani Suwayf Egypt

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology.

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Knowledge Representation of Asset Information and Performance in OT Environments

Knowledge Representation of Asset Information and Performanc...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter Institute of Computer Engineering TU Wien Vienna Austria Institute of Computer Technology TU Wien Dep. of Integrated Sensor Systems Danube University Krems Austria

To make a management decision for the Operational Technology (OT) environment, obtaining OT asset information from a plant is essential. Key Performance Indicators (KPIs) can play a crucial role in evaluating the performance of the manufacturing process, minimizing production costs, assessing process effectiveness, and investigating various settings to improve performance, operations, and quality in OT. This paper aims to provide a detailed examination of how Asset Information Mining Sources (AIMSs) support the process for acquiring both static and runtime OT asset information. Once we have obtained accurate OT asset information, we can calculate the KPIs and incorporate them into a knowledge representation using an ontology-based model. This model can assist the management team in making informed decisions and provide a roadmap for exploring further automation possibilities.

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A Model Based Framework for Testing Safety and Security in Operational Technology Environments

arXiv

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arXiv 2023年

作者： Bhole, Mukund Kastner, Wolfgang Sauter, Thilo Institute of Computer Engineering TU Wien Vienna Austria Institute of Computer Technology TU Wien Dep. of Integrated Sensor Systems Danube University Krems Austria

Today’s industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the operational technology level to compromise their safety functions. To identify these concerns, we propose a model-based testing approach which we consider a promising way to analyze the safety and security behavior of a system under test providing means to protect its components and to increase the quality and efficiency of the overall system. The structure of the underlying framework is divided into four parts, according to the critical factors in testing of operational technology environments. As a first step, this paper describes the ingredients of the envisioned framework. A system model allows to overview possible attack surfaces, while the foundations of testing and the recommendation of mitigation strategies will be based on process-specific safety and security standard procedures with the combination of existing vulnerability databases. © 2023, CC BY.

关键词： Model checking

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Tracking of Multiple Spawning Targets with Heterogeneous Sensors for Seabed-To-Space Situational Awareness

Tracking of Multiple Spawning Targets with Heterogeneous Sen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Domenico Gaglione Leonardo M. Millefiori Paolo Braca Peter Willett Moe Z. Win NATO STO-CMRE La Spezia Italy Electrical and Computer Engineering Dep. University of Connecticut Storrs CT USA Laboratory for Information and Decision Systems MIT Cambridge MA USA

Seabed-to-space situational awareness (S3A) aims to organize, fuse, and synthesize the massive volume of information collected from heterogeneous sensors, i.e., underwater, terrestrial, and space-based sensors, and therefrom extract knowledge thence available to defence operators, enabling informed decision-making. Heterogeneous sensors provide observations of targets in different domains (e.g., air, water surface, undersea), and with different modalities, perspectives, latencies, and update rates. They complement each other, and an effective approach is needed to combine the data they generate. This paper introduces a comprehensive framework for multi-target tracking based on the sum-product algorithm (SPA) that models the different characteristics of the sensors and handles the appearance of targets in a complex multi-domain environment both through spontaneous births and through spawning from existing targets. The efficacy of this approach is demonstrated through a simulated maritime scenario informed by real-world observation streams.

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Ka-Band Phased Antenna Array Concept for High-EIRP Satellite Connections

Ka-Band Phased Antenna Array Concept for High-EIRP Satellite...

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European Conference on Antennas and Propagation, EuCAP

作者： Stefano Moscato Alessandro Fonte Steven Caicedo Mejillones Alessandro DiCarlofelice Emidio DiGiampaolo Piero Tognolatti Emilio Arnieri Giandomenico Amendola Luigi Boccia Matteo Oldoni Research and Development Dep. SIAE MICROELETTRONICA Cologno Monzese MI Italy Dep. of Industrial and Information Engineering and Economics Univ. of L'Aquila L'Aquila Italy Dep. of Computer Eng. Modeling Electronics and Systems Univ. of Calabria Rende CS Italy Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

This work deals with an innovative architecture of a phased antenna array operating in the Ka band. It envisages the adoption of custom designed PA together with 64 hollow metallic horns as radiating elements. The proposed architecture is designed to work in the 25.5 to 27 GHz frequency span with a target EIRP higher than 70 dBm, even considering the maximum beam steering configuration of $\pm 20^{\circ}$ . The proposed concept highlights the flexibility in targeting different power levels, antenna gain and steering capabilities to finally address a large number of use-cases. The exploitation of non-uniform array is also taken into account to prevent grating lobes and increasing the steering capabilities of the phased array.

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Extractive summarization for explainable sentiment analysis using transformers 2

Extractive summarization for explainable sentiment analysis ...

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Joint 2nd International Workshop on Deep Learning Meets Ontologies and Natural Language Processing and 6th International Workshop on Explainable Sentiment Mining and Emotion Detection, DeepOntoNLP and X-SENTIMENT 2021

作者： Bacco, Luca Cimino, Andrea Dell'Orletta, Felice Merone, Mario Università Campus Bio-Medico di Roma Unit of Computer Systems and Bioinformatics Dep. of Engineering ItaliaNLP Lab Webmonks S.r.l.

In recent years, the paradigm of eXplainable Artificial Intelligence (XAI) systems has gained wide research interest and beyond. The Natural Language Processing (NLP) community is also approaching this new way of understanding AI applications: building a suite of models that provide an explanation for the decision, without affecting performance. This is certainly not an easy task, considering the wide use of very poorly interpretable models such as Transformers, which in recent years are found to be almost ubiquitous in the NLP literature because of the great strides they have allowed. Here we propose two different methodologies to exploit the performance of these models in a task of sentiment analysis and, in the meantime, to generate a summary that serves as an explanation of the decision taken by the system. To compare the classification performance of the two methodologies, we used the IMDB dataset while, to assess the explainability performance, we annotated some samples of this dataset to retrieve human extractive summaries, benchmarking them with the summaries generated by the systems. © 2021 CEUR-WS. All rights reserved.

关键词： Sentiment analysis

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Speech Emotion Recognition Using Deep Learning Techniques and Augmented Features

Speech Emotion Recognition Using Deep Learning Techniques an...

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International Conference on Electrical engineering, computer Science and Informatics (EECSI)

作者： Shahed Mohammadi Niloufar Hemati Ali Hashemi Haniye Zandiye Computer Science & Systems Engineering Dep. Ayandegan University Mazandaran Iran Islamic Azad University Central Tehran Branch Tehran Iran Qom University of Technology Qom Iran

This paper presents a study on speech emotion recognition using deep learning techniques. The focus is on utilizing the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS) datasets. The proposed approach involves preprocessing the audio signals by converting them into a sequence of numbers and normalizing them using max normalization. Furthermore, the signals are augmented using three methods: noise with a 0.015 noise rate, pitch adjustment, and a combination of noise with a 0.03 noise rate and pitch adjustment. The extraction of audio features includes a combination of two features, namely Mel Frequency Cepstrum Coefficients (MFCC) and Root Mean Square (RMS) energy, which provide distinct information from each other. These features are then used to train a CNN for emotion classification. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing emotions from speech signals. The proposed approach achieves an accuracy of 93.6% on the RAVDESS dataset and 99.9% on the TESS dataset. These results highlight the potential of the proposed approach in practical applications such as speech therapy and human-computer interaction.

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond 23

Fed-Haul: A Federated Learning Dual Band Point Multi-Point B...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Chehri, Abdellah Chaibi, Hasna Mettiti, Abderrahmane Saadane, Rachid Jeon, Gwanggil Dep. of Math. and Computer Science Royal Military College of Canada Kingston Canada Supmti Genius Laboratory Hassan Rabat Morocco It Rabat Center Faculty of Sciences Mohammed v University Rabat Morocco SIRC-LAGES-EHTP Hassania School of Public Works Casalanca Morocco Department of Embedded Systems Engineering Incheon National University Korea Republic of

ISBN: (纸本)9798400700194

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices - the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices - and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdep.ndency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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