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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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AAF-SCM: An Authenticated Framework for Supply Chain Management

AAF-SCM: An Authenticated Framework for Supply Chain Managem...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Roy, Prince Kumar, Rajneesh Morshed, Md Neaz Fairleigh Dickinson University Dept. of Applied Computer Science United States Dept. of Computer Science & Engineering India

ISBN: (纸本)9798331539948

The process of a supply chain involves a series of activities that distribute, assemble, and manage products, transferring them from the supplier to the end consumer. It operates as a complex, interconnected network regulated by supply and demand. Security in supply chain management is a crucial aspect of its overall security measures, focusing primarily on maintaining essential end-to-end protection. Without this, several attacks can be used to manipulate data and valuable information from the entire chain of networks. This paper explains how encrypted intermediates can secure the entire SCM by validating each intermediate using public-private key encryption. We also demonstrate and assess how the proposed AAF-SCM model can aid in tracing security breaches or unauthorized data tampering. We also validate this model against nefarious attacks like Fabrication, Assertion, Deletion, and Modification. Finally, we propose opportunities to develop innovative systems that enhance security through intermediary steps. © 2024 IEEE.

关键词： Supply chain management

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： D'Aniello, Giuseppe Falcone, Roberto Gaeta, Matteo Rehman, Zia Ur Fortino, Giancarlo University of Salerno Dept. Information and Electrical Engineering and Applied Mathematics SA Fisciano Italy University of Calabria Dept. Computer Engineering Modeling Electronics and Systems Cs Rende Italy

ISBN: (纸本)9781665410205

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset. © 2024 IEEE.

关键词： Prediction models

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Combating Cyber Attacks by Malware Classification Using Deep Neural Networks

Combating Cyber Attacks by Malware Classification Using Deep...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Bennet, Deepthi Tabitha Samantha Bennet, Preethi Anitha Gracy, J. Sharmila, P. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9798350390049

The detection and classification of malware pose significant challenges in cybersecurity due to the ever-evolving nature of malicious threats. In this study, we propose a novel approach for classifying malware using deep neural networks (DNNs). Our dataset comprises of 25 distinct classes of malware samples. After developing and testing 4 DNNs, including ResNet50, InceptionResNetV2, VGG16, DenseNet169, a robust and efficient model capable of accurately identifying and categorizing malware instances is built using DenseNet169. The process involves feature extraction from byteplot *** proposed malware detection model uses DenseNet169 as it has shown very high accuracy (95%), precision, recall and F1-score for the *** research contributes to advancing the state-of-the-art in malware classification, providing insights into the efficacy of deep neural networks for combating sophisticated cyber threats in modern computing environments. © 2024 IEEE.

关键词： Cyber attacks

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Building Cloud Base Cyber Range by Designing an Efficient Container Distribution API Using Docker 16

Building Cloud Base Cyber Range by Designing an Efficient Co...

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16th IIAI International Congress on Advanced applied Informatics, IIAI-AAI 2024

作者： Shin, Sanggyu Tokai University Dept. of Applied Computer Engineering Hiratsuka-shi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377903

This study proposes the Cyber Range environment built in a virtual space on the Cloud via a local web browser. The training contents are developed using Docker, which has excellent portability, and attack and defense exercise scenarios are produced as containers. This study (1) develops a Cloud platform that can be connected to the cloud-based training environment via a web browser. (2) enable users to interactively attack and defend exercises in a virtual space on the Cloud in an internet environment. Finally, (3) The curriculum is structured based on micro-service to be configured flexibly in the open environment. © 2024 IEEE.

关键词： Cloud platforms

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Construction and Examination of an Emotional Speech Synthesis System Using a CNN Image Classification Model 16

Construction and Examination of an Emotional Speech Synthesi...

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16th IIAI International Congress on Advanced applied Informatics, IIAI-AAI 2024

作者： Shin, Sanggyu Tokai University Dept. of Applied Computer Engineering Hiratsuka-shi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377903

In this study, the proposal system investigates the possibility of simplifying the emotional speech synthesis system by embedding styles built with a feature extractor based on a CNN image classification model. To simplify the emotional speech synthesis system, the proposal system attempted to add emotion to Tacotron2 speech synthesis by creating a style embedding using a feature extractor based on the CNN image classification model. © 2024 IEEE.

关键词： Speech analysis

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Robust Line-Convex Polygon Intersection Computation in E2 using Projective Space Representation

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Machine Graphics and Vision 2023年第3-4期32卷 3-16页

作者： Skala, Vaclav Dept. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

This paper describes modified robust algorithms for a line clipping by a convex polygon in E2 and a convex polyhedron in E3. The proposed algorithm is based on the Cyrus-Beck algorithm and uses homogeneous coordinates to increase the robustness of computation. The algorithm enables computation fully in the projective space using the homogeneous coordinates and the line can be given in the projective space, in general. If the result can remain in projective space, no division operation is needed. It supports the use of vector-vector operations, SSE/AVX instructions, and GPU. © 2023 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： computer graphics

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

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22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Influence of Electric Vehicle Charging on the Grid Access Impedance from 20 kHz to 500 kHz

Influence of Electric Vehicle Charging on the Grid Access Im...

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2023 International Conference on Smart Energy Systems and Technologies, SEST 2023

作者： Gonzalez-Ramos, Jon Angulo, Itziar Fernandez, Igor Gallarreta, Alexander Arrinda, Amaia De La Vega, David Dept. of Communications Engineering Bilbao Spain Dept. of Applied Mathematics Bilbao Spain

ISBN: (纸本)9798350397901

This paper aims to analyze the potential effect of the Electric Vehicle charging process (EVCP) on the mean and sub-cycle grid access impedance from 20 kHz to 500 kHz. The characterization is carried out in a controlled Low Voltage (LV) grid and covers the still uncharacterized frequency band assigned to Narrowband Power Line Communications (NB-PLC) up to 500 kHz. The results show that the measured impedances, regardless of whether an EV is charging in the network or not, are considerably lower than the reference impedances defined in CISPR 16-1-2 and IEC 61000-4-7 standards. Moreover, the influence on the grid impedance of the distance between the measurement point and the Point of Connection (POC) where the EV Charging Stations (EVCSs) are installed has been evaluated, concluding, first, that the highest impedance variations do not necessarily occur at the electrical point where the EV is connected and, second, that the influence of a certain EVCP depends not only on the EVCP itself, but also on the impedance in the default situation (when no EV is connected). Finally, a time-dependent behavior within the fundamental period of the mains (20 ms) has been observed at specific frequencies both in the absence of EVs and when each EV is charging. Maximum differences within the cycle of 3.3 Ω and 0.9 rad in the modulus and phase, respectively, have been reported. © 2023 IEEE.

关键词： Electric vehicles

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Situation Identification using Context Space Theory and Decision Tree

Situation Identification using Context Space Theory and Deci...

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Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2023

作者： D’aniello, Giuseppe Gaeta, Matteo Dept. of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano84084 Italy

Context Space Theory (CST) is a geometrical approach used to represent contexts and situations in situation-aware computing applications. In this theory, situations are represented in a multidimensional space, where each dimension corresponds to an interesting feature of the context. The primary advantage of CST lies in its capacity to effortlessly integrate multiple factors, creating a meaningful representation of situations that can be observed and manipulated by experts. Moreover, it empowers experts to customize the situation space to align with their knowledge and understanding of the situation. However, when applied to real-world scenarios, modeling complex situation spaces can be time-consuming and labor-intensive. This is due to the manual effort required in defining contribution functions for each context feature, as well as determining weights and thresholds to identify the situation space. To address this challenge, the paper proposes a hybrid approach that combines decision trees with the CST, thereby expediting the definition of situation spaces. Decision trees are employed to automatically identify an initial definition of the contribution functions and weights, reducing the workload on human experts. To demonstrate the efficacy of this approach, the paper showcases a case study focused on the management of the Covid-19 pandemic situation in Italy. © 2024, EasyChair. All rights reserved.

关键词： Decision trees

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