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International Conference on computer and information Technology (ICCIT)

作者： Mst. Nishita Aktar Md. Nazmus Sakib Afzal Hossain Ahsan Ullah Kazi Hassan Robin Kh. Mustafizur Rahman Md Tuhin Reza Ali Ameen Md Refat Hossain Debashis Kundu Computer Science & Engineering World University of Bangladesh Dhaka Bangladesh Information Sciences and Manegement Lincoln University Petaling Jaya Malaysia College of Business Irvine Westcliff University California USA Dept. Management Information Systems American International University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

An empirical analysis is conducted to reduce the false positive alerts in Security information and Event Management (SIEM) systems. Through the use of recurrent Neural Networks (RNN), research aims to increase alert detection accuracy and reduce false positive rates. In order to reduce false positives in SIEM systems. this study applies a novel combination of well-established techniques, such as Random Forest classifiers, Echo State Networks (ESN), and Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. Although a lot of research has been done on these approaches in other fields, they haven’t been tested in the context of SIEM systems.using the particular dataset that this study employed, which came from Kaggle. Investigation demonstrates that the ESN-Random Forest combo yields superior outcomes, achieving high precision, a 99.5% detection accuracy, and a considerable decrease in false positive *** limits in introducing the primary dataset, which is forced to depend only on publicly available *** may not adequately portray the nuances and complexity of real Security information and Event Management (SIEM) systems.

关键词： Accuracy Echo state networks Forestry Feature extraction Complexity theory information technology computer security Random forests

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Study of the Shock Loads Effect on the Operation of Multi-Motor Electric Drives of a Rolling Mill with a Second-order Elastic Coupling 5

Study of the Shock Loads Effect on the Operation of Multi-Mo...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Tytiuk, Valerii Kalinin, Alexey Druzhinin, Valeriy Kuznetsov, Vitalii Chornyi, Oleksii Busher, Victor Kryvyi Rih National University Department of Electromechanics Kryvyi Rih Ukraine Abylkas Saginov Karaganda Technical University Npjsc Department of Informative Computer Systems Karaganda Kazakhstan Karaganda Industrial University Npjsc Department of Power Engineering Temirtau Kazakhstan Ukrainian State University of Science and Technologies Department of the Electrical Engineering Dnipro Ukraine National University Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi Kremenchuk Ukraine National University "odessa Maritime Academy" Dept. of Electrical Engineering and Electronics Odessa Ukraine

ISBN: (纸本)9798350359787

This paper presents a study for optimizing a hot rolling mill. The paper is devoted to the study of ways to reduce the effect of shock loads of synchronous electric drives of the roughing mill group on the relative lengthening of the metal strip in the inter-stand gap of the rolling stands of the finishing mill group. The elasticity of the metal strip in the inter-stand gap of the rolling mill has a significant impact on the movement of intercoupled multi-motor electric drives of the finishing mill group. Another crucial route of exposure that influences the motion of the electric drives within the finishing mill group involves the electromagnetic connection between the drives in the roughing and finishing mill groups. The impact of the electromagnetically connected circuits is evident through the effect of shock loads from synchronous electric drives on the angular velocity of the direct current electric drives within the finishing mill group. The characteristics of the electromagnetic communication circuit are established by the power grid arrangement and equipment specifications. To assess the quality of work of electric drives of the finishing mill group, it is proposed to use the relative lengthening of the metal strip in the inter-stand gap, which is an important indirect indicator of the quality of rolling. The mathematical model of a hot rolling mill that takes into account both the elasticity of the metal strip that connects the DC electric drives of the finishing group, and the electromagnetic coupling circuit that takes into account the impact loads of synchronous electric drives of the roughing group on the example of the mill 1700 of ArcelorMittal Temirtau JSC was proposed in this paper. Using the mathematical model developed in Simulink software using the methods of planning the experiment, regression models were obtained to establish the dependence of the parameters of overexcitation of a synchronous motor on the magnitude of the voltage sag in the no

关键词： Electric loads

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Repairing with Zero Skip Cost

Repairing with Zero Skip Cost

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IEEE International Symposium on information Theory

作者： Yeow Meng Chee Son Hoang Dau Tuvi Etzion Han Mao Kiah Yuan Luo Wenqin Zhang Dept. of Industrial Systems Engineering and Management National University of Singapore School of Computing Technologies RMIT University Melbourne VIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio... 详细信息

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

关键词： Measurement Costs Codes Maintenance engineering

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

arXiv

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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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LIDS: Lightweight Dynamic Scheduling Technique for 6G-enabled Massive LoRa based IoT systems

LIDS: Lightweight Dynamic Scheduling Technique for 6G-enable...

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IEEE Globecom Workshops

作者： Akshitha Kumbam Nikumani Choudhury Moustafa M. Nasralla Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781665459761

The sixth-generation (6G) wireless communication networks are envisioned to deliver improved Quality of Services (QoS) such as data-rate, latency, localization, etc., compared to 5G services. This demand will mainly arise due to the massive Internet of Things (IoT) connections among billions of embedded devices. It is worth noting that these IoT devices are constrained in nature in terms of memory, computation and most importantly power. Low-power wireless computing necessitates a link-layer technology that considers the aforementioned IoT device requirements such as low-power, low data-rate, long-range and energy efficient reliable communications. Long Range Radio (LoRa) technology has recently become widely popular to facilitate low-power, long-range communication, while other similar technologies struggle to achieve long-range transmissions. The LoRA standard defines several PHY and MAC sub-layer specifications for Low-Power Wide Area Networks (LPWAN) that can be used to build several IoT applications. With 6G communications, an IoT application is expected to consist of hundreds of devices connected at a time, and this will need a proper scheduling mechanism among these devices for optimal performance. The LoRa standard does not define any scheduling mechanism and leaves it to the research community to explore. In this paper, we propose a distributed scheduling mechanism under the supervision of a common LoRa gateway. The proposed mechanism focuses on a Lightweight DynamicScheduling (LIDS) scheme to minimize loss in data transmissions through collisions. The proposed mechanism optimizes the power consumption to achieve the desired level of QoS requirements such as throughput and latency.

关键词： 6G mobile communication Wireless communication Power demand Quality of service Throughput Propagation losses Internet of Things

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Feature Distraction Based Backdoor Defense for Federated Trained Intrusion Detection System

Feature Distraction Based Backdoor Defense for Federated Tra...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yu-Wen Chen Bo-Hsu Ke Yen-Xin Wang Shih-Heng Lin Ming-Han Tsai Bo-Zhong Chen Jian-Jhih Kuo Ren-Hung Hwang Computer Systems Technology New York City College of Technology Brooklyn New York USA Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan College of Artificial Intelligence National Yang-Ming Chiao Tung University Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) incorporated with Federated Learning (FL) facilitates collaborative model training to secure user privacy and enhance data diversity, emerging as a promising solution to defend malicious flows. Yet, FL is vulnerable to trigger backdoor attacks. Existing methods have limited ability on IDS with FL for detecting or repairing a converged attacked model as they either require access to the training process or are dedicated to specific applications. To tackle the limitations, we propose a novel Features Distraction Defense Framework (FDDF) that does not interfere with the model training and requires only a few additional model inferences to protect users from the aforesaid attack. The experiment results show that FDDF can recover the accuracy effectively and preserve the accuracy of normal models.

关键词： Training Data privacy Accuracy Costs Federated learning Computational modeling Intrusion detection Feature extraction Data models Global communication

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Using the recurrence plots as indicators for the recognition of Parkinson’s disease through phonemes assessment

Using the recurrence plots as indicators for the recognition...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vasileios Skaramagkas Anastasia Pentari Dimitrios I. Fotiadis Manolis Tsiknakis Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Institute of Computer Science Foundation for Research and Technology Hellas (FORTH) Heraklion Crete Greece Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology (FORTH) Ioannina Greece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece

Parkinson’s disease (PD) is considered to be the second most common neurodegenerative disease which affects the patients’ life throughout the years. As a consequence, its early diagnosis is of major importance for the improvement of life quality, implying that the severe symptoms can be delayed through appropriate clinical intervention and treatment. Among the most important premature symptoms of PD are the voice impairments of articulation, phonation and prosody. The objective of this study is to investigate whether the voice’s dynamic behavior can be used as possible indicator for PD. Thus in this work, we employ the recurrence plots (RPs) which derive from the analysis of the three modulated vowels /a/, /e/ and /o/, which belong to the PC-GITA dataset, and are fed as input images to a 3-channel Convolutional Neural Network-based (CNN) architecture, which, finally, differentiates the 50 PD patients from 50 healthy subjects. The experimental results obtained provide evidence that the RP-based approach is a promising tool for the recognition of PD patients through the analysis of voice recordings, with a classification accuracy achieved equal to 87%.

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data systems

Towards Eco-Sustainability and Green Analytics Model to Meas...

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International Conference on information Management (ICIM)

作者： Yusmadi Yah Jusoh Rusli Abdullah Rozi Nor Haizan Nor Jamaiah Hj. Yahaya Shireen Muhammad Aishwharya Raani Arunachalam Dept. of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Communication Visual Art and Computing Universiti Selangor (UNISEL) Selangor Malaysia

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS.

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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A Hybrid Deep Learning Network for Long-Term Travel Time Prediction in Freeways 26

A Hybrid Deep Learning Network for Long-Term Travel Time Pre...

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26th International Conference on technologies and Applications of Artificial Intelligence, TAAI 2021

作者： Ho, Ming-Chu Chen, Yu-Cing Hung, Chih-Chieh Wu, Hsien-Chu National Chung Hsing University Dept. of Management Information Systems Taiwan National Chung Hsing University Dept of Foreign Language and Literature Taiwan National Chin-Yi University of Technology Dept of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665408257

Travel time plays a vital role in people's daily lives. It can help them not merely avoid traffic congestion but save time as well. When people need to drive to different cities by taking highways, travel time become more and more important now that they can check it to arrange better routes. Moreover, because COVID-19 are epidemic across Taiwan, people prefer to drive rather than taking public transportation. Therefore, accurate predictions of travel time is of great significance. In order to obtain precise predictions and correspond to situations in real life, we divide data into long and short sequences and create three types of dataset, including the whole year, only national holidays, and non-holidays. Additionally, on account of the interactive influence of time in different segments of the freeway, we exploit data to predict next-hour travel time instead of next 5 minutes. We introduce a deep learning model which hybrids tendency from XGBoost and recency embeddings from a fully-connected neural network, respectively. It can capture crucial features of both long and short sequences and observe implicit correlations between XGBoost and a fully-connected neural network. Extensive experiments on the dataset illustrate that our model achieves eminent performances and outperforms other state-of-the-art models. © 2021 IEEE.

关键词： Travel time

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