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IEEE International Conference on Big Data

作者： Lopamudra Praharaj Deepti Gupta Maanak Gupta Dept. of Computer Science Tennessee Tech University Cookeville Tennessee USA Dept. of Computer Information Systems Texas A&M University - Central Texas TX USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The agriculture sector is increasingly adopting innovative technologies to meet the growing food demands of the global population. To optimize resource utilization and minimize crop losses, farmers are joining cooperatives to share their data and resources among member farms. However, while farmers benefit from this data sharing and interconnection, it exposes them to cybersecurity threats and privacy concerns. A cyberattack on one farm can have widespread consequences, affecting the targeted farm as well as all member farms within a cooperative. For instance, a de-authentication attack prevents sensors from connecting to the network, obstructing the farming application from receiving real-time data. This disruption obstructs decision-making for all member farms that rely on this data from an attacked farm. Further, farmers have adopted digital twin (DT) technology that facilitates a virtual farm replica that encompasses vital aspects of farming, such as crop cultivation, soil composition and weather conditions. However, it’s critical to acknowledge that attackers can target these digital twins (DTs), potentially disrupting real physical farm *** this research, we address existing gaps by proposing a novel and secure architecture for Cooperative Smart Farming (CSF). First, we highlight the role of edge-based DTs in enhancing the efficiency and resilience of agricultural operations. To validate this, we develop a test environment for CSF, implementing various cyberattacks on both the DTs and their physical counterparts using different attack vectors. We collect two smart farming network datasets to identify potential threats. After identifying these threats, we focus on preventing the transmission of malicious data from compromised farms to the central cloud server. To achieve this, we propose a CNN-Transformer-based network anomaly detection model, specifically designed for deployment at the edge. As a proof of concept, we implement this model and evaluate

关键词： Smart agriculture Web services Image edge detection Crops Soil Vectors Digital twins Servers Cyberattack Farming

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Traffic Sign Detection and Recognition Using YOLOv5 and Its Versions 1

Traffic Sign Detection and Recognition Using YOLOv5 and Its ...

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1st IEEE International Conference on Cognitive Mobility, CogMob 2022

作者： Lebumfacil, Aaron Joaquin Abu, Patricia Angela Ateneo De Manila University Ateneo Laboratory for Intelligent Visual Environments Dept. of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9781665476317

A Traffic Sign Detection and Recognition (TSDR) System, which helps navigate vehicles through computer vision and human-machine communication, has to perform quickly as vehicles using them travel at high speeds. During this study, a speedy one-stage detector such as YOLOv5, a deep learning model, was chosen to dive into. This study explores creating a TSDR model by comparing four different versions of YOLOv5, namely YOLOv5 Nano, Small, Medium, and Large. This study was accomplished by first creating a new traffic sign dataset. The four versions of the YOLOv5 algorithm were then trained with a 75-25 train validation split, and 24 models were created. Afterwards, the models were tested on a test set, and their metrics were tallied. Results showed that YOLOv5 Medium and Large offer a 10% increase in accuracy performance when compared to YOLOv5 Small, but due to the slower detection speed of YOLOv5 Large, the YOLOv5 Medium models are a better fit when it comes to the detection of traffic signs when prepared by a relatively small dataset. This study provides an overview of the performance of the different YOLOv5 versions in traffic sign detection and recognition that aims to contribute to the improvement of traffic sign detectors. © 2022 IEEE.

关键词： computer vision

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Lightweight Networks for COVID-19 Detection from Chest X-Ray Images inside a Low-Tier Android Device

Lightweight Networks for COVID-19 Detection from Chest X-Ray...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Bacad, Dave Jammin A. Abu, Patricia Angela R. Ateneo de Manila University Ateneo Laboratory for Intelligent Visual Environment Dept. of Information Systems and Computer Science Quezon City Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

The efforts to inoculate majority of the population have been slower than expected and this is especially true for lower income countries. This problem has caused a lot of worries and further accentuates the importance of timely and effective mass testing considering the emergence of newer variants. The RT-PCR is still the gold standard diagnostic test for COVID-19 detection, but its limitations has led researchers and scientists to explore supplementary screening methods. One effective tool to consider is Chest X-Ray (CXR) imaging and combining it with deep learning has piqued attention from the artificial intelligence (AI) community. To further contribute to this research area, this work focuses on creating, evaluating, and comparing lightweight and mobile-phone-suitable COVID-detecting models. These transfer learning models together with their corresponding dynamic-range quantized versions are first tested according to their classification performance. Afterwards, the models are pushed in a low-tier phone to measure their resource consumption and inference timings. Results show that the utilization of EfficientNetB0 and MobileNetV3 (Small & Large) architectures for transfer learning without any quantization can produce at least 91 % overall average accuracy for 3-class classification scheme. For systems requiring more efficient models, using the quantized versions of the transfer learning models particularly with EfficientNetB0 and MobileNetV3Large as foundation can render at most 0.79 % accuracy loss but still show more than 95% f1-scores for the COVID-19 class. © 2022 IEEE.

关键词： COVID-19

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A Novel Technique to Parameterize Congestion Control in 6TiSCH IIoT Networks

arXiv

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arXiv 2024年

作者： Chakraborty, Kushal Dutta, Aritra Kumar Hussain, Mohammad Avesh Mohiuddin, Syed Raafay Choudhury, Nikumani Matam, Rakesh Mukherjee, Mithun Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad Pilani India Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Nanjing University of Information Science and Technology Nanjing China

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads. © 2024, CC BY.

关键词： Internet of things

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Impact of Dataset Balancing on Machine Learning-Based Intrusion Detection systems 10

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Yusri, Muhammad Iqbal Habaebi, Mohamed Hadi Gunawan, Teddy Surya Mansor, Hasmah Kartiwi, Mira Nur, Levy Olivia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350365283

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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FedDist-POIRec: Federated Distillation for Point-Of-Interest Recommendation in Human Mobility Prediction 4

FedDist-POIRec: Federated Distillation for Point-Of-Interest...

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4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Rezaei, Esmatollah Seyedmohammadi, S. Jamal Abouei, Jamshid Plataniotis, Konstantinos N. Yazd University Dept. Of Electrical Engineering Yazd Iran Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Edward S. Rogers Sr. Department Of Electrical And Computer Engineering Toronto Canada

ISBN: (纸本)9798350315790

Next Point-of-Interest (POI) recommendation plays a vital role in human mobility prediction within Location-based Social Networks (LSBN), assisting individuals to decide on their next destination. However, traditional centralized systems rely on large datasets of previous POI check-ins, which raises privacy and computation concerns. In this regard, Federated Learning (FL) has emerged as a promising solution, enabling distributed training of machine learning models while preserving user privacy. In this paper, we propose a novel FL-based POI recommendation system, FedDist-POIRec, that utilizes Federated Distillation (FD) in a semi-synchronous framework to promote privacy, recommendation efficiency, and cope with resource heterogeneity by considering heterogeneous model architectures among users. Additionally, to alleviate the sparsity of user checkin lists, we employ a innovative knowledge aggregation technique, where the knowledge of each user is aggregated with that of users who share similar preferences. Simulation results demonstrate that FedDist-POIRec outperforms the baseline methods in terms of recommendation efficiency and communication cost, while keeping the computation cost at an acceptable level. © 2024 IEEE.

关键词： Distillation

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Change Bit-dept. Procedure for Data Measured Preprocessing before Compression

Change Bit-Depth Procedure for Data Measured Preprocessing b...

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2022 IEEE International Multi-Conference on Engineering, computer and information Sciences, SIBIRCON 2022

作者： Levenets, Alexey V. Bondarenko, Vladislav V. Bahenov, Ruslan I. Belozerov, Oleg I. Belozerova, Svetlana I. Bogatenkov, Sergey A. Pacific National University Dept. Automation and System Engineering Khabarovsk Russia Sholom-Aleichem Priamursky State University Dept. Information Systems and Mathematics Birobidzhan Russia Far Eastern State Transport University Dept. Computer Technology and Computer Graphics Khabarovsk Russia South Ural State University Dept. Information Technology in Economics Chelyabinsk Russia

ISBN: (纸本)9781665464802

The paper proposes to change the bit-dept. of the original data sequence as a preprocessing of the measuring data, which is carried out before the compression procedure. In this case the digit capacity conversion is performed without accounting for the primary data sample boundaries. The simulation was carried out in the Matlab software environment. The findings of the authors' research are presented. They prove to be a promising approach for some types of the studied data at least and several digit capacity values. The analysis of autocorrelation function values showing the data correlation revealed by the conversion is used as an assessment of perspective of the active conversion. In this article the authors review the values of secondary maximums of autocorrelation function. Their occurrence shows the demonstration of the implied correlative correspondence. Such correlations can potentially allow the converted data to be compressed more effectively than the original data using the known compression methods. © 2022 IEEE.

关键词： Data compression

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Systematic Literature Review of Security Control Assessment Challenges 12

Systematic Literature Review of Security Control Assessment ...

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12th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2022

作者： Abu Othman, Noor Ashitah Norman, Azah Anir Mat Kiah, Miss Laiha Universiti Malaya Faculty of Comp. Science & Information Technology Kuala Lumpur Malaysia Universiti Teknologi MARA Faculty of Computer & Math. Sciences Melaka Malaysia Universiti Malaya Faculty of Computer Science & Information Technology Dept. of Information Systems Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science & Information Technology Dept. of Computer System &Technology Kuala Lumpur Malaysia

ISBN: (数字)9781665483391

ISBN: (纸本)9781665483391

The advancement of cybersecurity has called for active effective information security management. Security control (SC) assessment must be empowered to ensure that security implementation is effective and provides expected protection. Lack of comprehensive literature analysis on SC assessment compared to risk and threats assessments are concerning. This research aims to systematically review the trends of SC assessment by identifying, categorizing and analyzing the challenges and available solutions of SC assessment. 34 articles were qualitatively selected with a definite contribution in SC assessment. These articles were reviewed using thematic analysis according to Theme 1: Assessment Challenges, and Theme 2: Proposed Solution. Findings from each theme are systematically categorized to answer research questions. The results of this review are significant in identifying the issues and areas of improvement for future research and can serve as the baseline for SC assessment characteristics. © 2022 IEEE.

关键词： Cybersecurity

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Highlighting of Hydrographic Objects on Satellite Images for the Development of High-quality Digital Relief Models of Floodplain Areas as a Basis for Modeling the Hydrological Regime

Highlighting of Hydrographic Objects on Satellite Images for...

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International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anna Tertychnaya Alexander Khoperskov Dept. Information Systems and Computer Modeling Volgograd State University Volgograd Russia

Mathematical modeling of surface water dynamics allows us to make forecasts of the hydrological regime of a territory for a wide variety of hydrological, environmental, and geophysical applications. The such simulations quality is largely determined by the relevance of the topographic data used to construct digital terrain models. The important task is the identifying of the boundaries of numerous reservoirs in a floodplain area. The distinctive feature such area is the complex structure of small channels, vegetation, and wetlands. We develop the method for detecting coastlines using multi-channel satellite data which includes two infrared bands (4 and 5 bands), such as Landsat ETM+. Software was tested whith the Northern part of the Volga-Akhtuba Floodplain (Russia) and one of the region in the Amazon basin. It differs significantly in their natural characteristics. During the software testing we determined the conditions that provides accurate detection of borders of water bodies.

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Problem of Improving the Environmental and Socio-Economic System of the Volga-Akhtuba Floodplain: Position Examination of Large Dam in the Volga River Based on Computational Fluid Dynamics

Problem of Improving the Environmental and Socio-Economic Sy...

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International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anna Yu. Klikunova Ekaterina O. Agafonnikova Alexander V. Khoperskov Dept. Information Systems and Computer Modeling Volgograd State University Volgograd Russia

The system of hydroelectric power stations on the Volga River, built in the 20th century, completely changed the hydrological regime of the Volga River. The current over-regulation of the water flow is the cause of the accelerating degradation of the Volga-Akhtuba floodplain. Modern analysis of water availability indicates a critical point associated with the spring filling of the Akhtuba binnacle. Various projects suggest a solution to this problem. This study is aims at identifying the most optimal location of a large dam below the Volzhsky hydroelectric power stations. We use a new model of hydraulic resistance based on the combined effect of both the friction force in the Manning model and the turbulent viscosity. Our study involves varying of the dam’s location in a numerical shallow water model. We calculate the hydrological relationship between the release of water through the Volzhsky dam and the source of the Akhtuba, as a target function for analyzing the effectiveness of the project to construction such a dam.

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