2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)最新文献_第3页

A Recent Assessment for the Ransomware Attacks Against the Internet of Medical Things (IoMT): A Review 针对医疗物联网(IoMT)勒索软件攻击的最新评估:综述

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237161

Tamara Nusairat, M. Saudi, Azuan Ahmad

{"title":"A Recent Assessment for the Ransomware Attacks Against the Internet of Medical Things (IoMT): A Review","authors":"Tamara Nusairat, M. Saudi, Azuan Ahmad","doi":"10.1109/ICCSCE58721.2023.10237161","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237161","url":null,"abstract":"The magnitude, complexity, and diversity of cyber threats against the Internet of Medical Things have increased over the past several years, making it challenging to implement effective defence strategies against these cyber-attacks, especially against ransomware. Balancing security requirements and rapid innovation and adoption with the Internet of Medical Things is challenging in healthcare industries. Hence, this article presents the identified features in ransomware architecture that triggered the ransomware attacks against the Internet of Medical Things. The experiment was conducted in a controlled lab environment, with open-source tools and by using hybrid analysis. As a result, thirteen (13) related features that include initial access, persistence, execution, defence evasion, credential access, discovery, impact, command and control, privilege escalation, lateral movement, collection, network communication and MD5 have been identified by analyzing vulnerabilities, potential attack routes, and the effects of a successful security breach against the IoMT. In the future, this work can be used as a reference and guidance for other researchers with the same interest.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114891627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Optimizing Network Classification Performance by Geometric Transformations on Delayed Enhancement Cardiac Magnetic Resonance Imaging 基于几何变换优化延迟增强心脏磁共振成像网络分类性能

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237089

D. S. A. Damit, S. N. Sulaiman, Muhammad Khusairi Osman, N. Karim, Belinda Chong Chiew Meng, M. F. Abdullah

{"title":"Optimizing Network Classification Performance by Geometric Transformations on Delayed Enhancement Cardiac Magnetic Resonance Imaging","authors":"D. S. A. Damit, S. N. Sulaiman, Muhammad Khusairi Osman, N. Karim, Belinda Chong Chiew Meng, M. F. Abdullah","doi":"10.1109/ICCSCE58721.2023.10237089","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237089","url":null,"abstract":"Delayed enhancement cardiac magnetic resonance imaging is crucial in identifying and monitoring heart disease. Since Deep Convolutional Neural networks have been found to perform very well in different computer-assisted activities, the use of these automated methods appears to have potential for reducing the workload of radiologists and improving workflow efficiency. Nevertheless, these networks rely significantly on big data to avoid biases and accurately learn the feature conditions. To address this issue, the use of data augmentation techniques has been suggested. In this work, we develop an automated deep-learning method to assist radiologists in classifying the left ventricle segment in cardiac MRI images by using pre-trained convolutional neural networks. Four popular network architectures, namely GoogLeNet, SqueezeNet, ResNet-50 and ShuffleNet were compared, and the abilities of these networks to perform the task were examined on augmented data using geometric transformation. All network models were trained and tested on 80% and 20% of the images, respectively, using five-fold cross-validation. On the augmented dataset and the same training network parameter, ResNet50 architecture achieves the highest performance with an average accuracy of 97.78% and F1-score of 0.9776. All networks’ performances differ slightly from one another. The finding shows that the target class, which is the LV segment, performs exceptionally well after the geometric transformation augmentation technique has been applied.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125678871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empowering Small and Medium Enterprises with Data Analytics for Enhanced Competitiveness 为中小型企业提供数据分析，提升竞争力

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237151

Aissa Mosbah, Musab A. M. Ali, N. Tahir

{"title":"Empowering Small and Medium Enterprises with Data Analytics for Enhanced Competitiveness","authors":"Aissa Mosbah, Musab A. M. Ali, N. Tahir","doi":"10.1109/ICCSCE58721.2023.10237151","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237151","url":null,"abstract":"In today’s competitive landscape, Small and Medium-sized Enterprises (SMEs) are increasingly embracing business data analytics to gain valuable insights and make informed decisions. However, the true essence of big data lies not only in its volume but also in the analysis process and the derived insights that aid managers in making effective business choices. Despite this, limited knowledge exists regarding SMEs’ utilization of data analytics and its potential in facilitating informed decision-making. Therefore, this paper aims to elucidate the concept of business data analytics within the context of SMEs and outline the essential requirements for its effective implementation, enabling SMEs to leverage data analytics for enhanced competitiveness. It is proposed that the foundation of a successful data analytics system for SMEs should encompass four key elements: data, people, technology, and process. The degree to which data analytics contributes to a firm’s competitiveness is largely influenced by four factors: data quality, well-defined objectives, the caliber of analytic tools and techniques, and analytical skills. Data generates information and insights, which in turn foster knowledge that ultimately leads to wisdom. However, due to limited resources, SMEs may be less inclined to engage in advanced predictive and prescriptive analytics compared to larger firms.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131083934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pre-controller Synthesis for Runtime Controller Synthesis 用于运行时控制器合成的预控制器合成

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237143

Yuki Arioka, Takuto Yamauchi, Kenji Tei

{"title":"Pre-controller Synthesis for Runtime Controller Synthesis","authors":"Yuki Arioka, Takuto Yamauchi, Kenji Tei","doi":"10.1109/ICCSCE58721.2023.10237143","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237143","url":null,"abstract":"Self-adaptive systems that operate to satisfy functional requirements in a changing environment are realized by reasoning with runtime models. Existing research has proposed such runtime modeling techniques, for example, reflecting changes in the environment in an environment model and synthesizing new behavior on the basis of the updated environment and requirement models by discrete controller synthesis. However, discrete controller synthesis increases the computation time exponentially as the model size of the environment and requirements increases, which poses a challenge for its application to resynthesizing controllers at runtime. In this paper, we propose a pre-computation controller synthesis that reduces the computation time by omitting a part of the synthesis process of the discrete controller synthesis performed when the environment changes. In the evaluation, we use a concrete system example to evaluate the usefulness of the pre-controller synthesis. As a result, we succeeded in reducing the execution time by up to 99.9% compared to the execution time of conventional controller synthesis.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130972594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OptimRSEG: An Optimized Semantic Road Segmentation Model 优化的语义道路分割模型

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237094

A. Huong, Kimgaik Tay, X. Ngu, W. Mahmud, N. Jumadi

{"title":"OptimRSEG: An Optimized Semantic Road Segmentation Model","authors":"A. Huong, Kimgaik Tay, X. Ngu, W. Mahmud, N. Jumadi","doi":"10.1109/ICCSCE58721.2023.10237094","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237094","url":null,"abstract":"The traditional methods used in road detection for autonomous vehicle applications depend largely on lane marking detection. The techniques can be compromised by shadows and vehicles, occluding the important features crucial to lane detection. This problem is even more prevalent in the case of unstructured roads without markings or borders. This study demonstrated a Particle Swarm optimization (PSO) optimized lightweight semantic segmentation model that made use of AlexNet architecture as its backbone for detecting urban roads, both with and without markings, and under different occlusion conditions. The PSO method is used to search for the best hyperparameters setting to optimize the model learning process using a small dataset for the two-class problem (lane vs. background). Our results showed that the proposed OptimRSEG model produced considerably good performance metrics results of 0.85, 0.91, and 0.923 in the evaluated Intersection of Union (IU), Dice Similarity Coefficient (DSC), and prediction accuracy, respectively. The use of augmentation to enrich the dataset improves these results slightly by around 1-7 %, confirming the effectiveness of the optimization strategy. This system performs acceptably well, even on road images without lane markings or unique markings or partially occluded, with a fast-computing time of 20 ms each frame.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"6 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133454761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Saving Wireless Bidirectional People Counter with Notification and Data-Storing Systems 节能无线双向计数器与通知和数据存储系统

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237092

Marc Joshua Rivera, D. D. C. Maceda, Andrew G. Bitancor, Leonardo C. Sawal, Rose Ann R. Blanqueza, Natalie Joy dJ. Dazo, Hannah Faye L. Sabillo

{"title":"Energy-Saving Wireless Bidirectional People Counter with Notification and Data-Storing Systems","authors":"Marc Joshua Rivera, D. D. C. Maceda, Andrew G. Bitancor, Leonardo C. Sawal, Rose Ann R. Blanqueza, Natalie Joy dJ. Dazo, Hannah Faye L. Sabillo","doi":"10.1109/ICCSCE58721.2023.10237092","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237092","url":null,"abstract":"In 2020, the Philippines succumbed to the pandemic brought by COVID-19 virus. Businesses were forced to shut down, and lockdown protocols, like social distancing and limited capacity, were implemented. Despite the closing of recreational establishments, electricity consumption in households spiked since people were mandated to stay at home and have a virtual setup for employees and students. With these in mind, the researchers conducted a study on the development of Energy-Saving Wireless Bidirectional People Counter with Notification and Data Storing Systems to aid people in making energy-saving efforts, implementing a maximum capacity of establishments, and providing information to business owners about their foot traffic. The prototype utilized ultrasonic sensors for detecting the number of people entering and leaving an area, light dependent resistor for detecting ample amount of light energy present in an area, LED lights and speakers for notification and alarm systems, relay modules for controlling the connected bulb and fan, and Arduino Uno for controlling the other components of the prototype. Through the data from the prototype, the bulb and fan were controlled; when there was human presence in an area, the bulb and fan were on and, otherwise, off. The prototype was 100% accurate in controlling the LED lights, alarm, bulb, and fan. On the other hand, a success rate of 86.27% and an error of 13.72% regarding ultrasonic sensors were recorded. Overall, the prototype saved energy, and a return on investment was theoretically possible if the controlled loads were 36-Watt bulbs and 48-Watt fans or higher.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114247469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stepwise Comparison for Minimizing Controller Makespan 最小化控制器最大时间跨度的逐步比较

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237145

Yuki Shimizu, Takanori Hirano, Takuto Yamauchi, Kenji Tei

引用次数: 0

Fusing Frontal Face Recognition Using Multi View Cameras 基于多视角相机的正面人脸识别融合

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237090

M. A. Rashidan, S. N. Sidek, M. M. Al-Samman

{"title":"Fusing Frontal Face Recognition Using Multi View Cameras","authors":"M. A. Rashidan, S. N. Sidek, M. M. Al-Samman","doi":"10.1109/ICCSCE58721.2023.10237090","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237090","url":null,"abstract":"The recognition of faces in videos has recently gained considerable attention, but the recognition process executed on a single camera has limitations, especially when dealing with uncooperative subjects, changes in body posture, or self-occlusion. These challenges are particularly relevant in the context of studying facial analysis in children with Autism Spectrum Disorder (ASD). Therefore, the use of multiple cameras in a face recognition system is proposed to overcome these limitations. Facial image realignment was employed in the automatic face recognition process. To achieve this, the Kanade-Lucas-Tomasi (KLT) algorithm was used to track facial features, and the RANSAC algorithm was utilized to estimate the homography transformation for realigning the multi-view input images. To assess and compare the similarity of the fused image, the normalized cross-correlation (NCC) was employed. The resulting fused image was obtained based on the extracted pose of the face. The results demonstrate the efficacy of the method, achieving an accuracy of 94.5% for typically developed children and 87.3% for ASD children.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116701390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security Analysis of DNP3 Protocol in SCADA System SCADA系统中DNP3协议的安全性分析

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237142

Q. Qassim, Musab A. M. Ali, N. Tahir

{"title":"Security Analysis of DNP3 Protocol in SCADA System","authors":"Q. Qassim, Musab A. M. Ali, N. Tahir","doi":"10.1109/ICCSCE58721.2023.10237142","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237142","url":null,"abstract":"The safeguarding of Supervisory Control and Data Acquisition (SCADA) systems and their cyber-security have been the subject of extensive research for many years, owing to the severe ramifications that can result from their breach or compromise. SCADA is a system that facilitates the monitoring and control of physical infrastructures in industrial processes from a centralized control station. The utilization of this technology is prevalent in crucial infrastructure domains, such as the electricity, oil, and gas industries, encompassing production and distribution. The compromise of data integrity in SCADA systems through cyber-attacks, specifically the unauthorized manipulation of sensors or control signals, has the potential to disrupt the operation of critical national infrastructure significantly. Therefore, this research is intended to investigate the limitations and drawbacks of one of the commonly used communication protocols in SCADA, namely; DNP3, investigate the cyber-attacks exploiting the weaknesses of this protocol, and put forward a recommendation to protect the SCADA networks and prevent devastating consequences.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132760153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Effectiveness of GRU Model with SIELU Activation Function for Water Quality Classification 带SIELU激活函数的GRU模型在水质分类中的有效性探讨

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI: 10.1109/ICCSCE58721.2023.10237140

Abdullahi Abdi Abubakar Hassan, Nordiana Rahim, R. Ghazali, N. Murli

{"title":"Exploring the Effectiveness of GRU Model with SIELU Activation Function for Water Quality Classification","authors":"Abdullahi Abdi Abubakar Hassan, Nordiana Rahim, R. Ghazali, N. Murli","doi":"10.1109/ICCSCE58721.2023.10237140","DOIUrl":"https://doi.org/10.1109/ICCSCE58721.2023.10237140","url":null,"abstract":"Accurate water quality classification is crucial for effective environmental monitoring and resource management. This research investigates the efficacy of the Gaussian Error Linear Unit with Sigmoid (SIELU) activation function in a Gated Recurrent Unit (GRU) model for improved water quality classification accuracy. Specifically focusing on Hong Kong rivers, this study justifies the dataset selection due to its consistent historical data, minimal missing values, and diverse parameters encompassing classifications of good, fair, and poor. By leveraging the unique properties of the SIELU activation function, the proposed SIELU-GRU model aims to enhance the GRU’s performance and generalization capabilities in the context of Hong Kong water quality classification. The research includes rigorous experimentation and analysis, comparing the proposed model with the existing approach, the Gaussian Error Linear Unit (GELU) activation function. Results highlight the superior accuracy achieved by a small margin by the model that includes GELU activation function, indicating its potential for improving environmental monitoring systems, aiding decision-making processes, and facilitating resource management, compared to the model that includes SIELU activation function. This study contributes to advancements in water quality classification methodologies, ultimately benefiting the sustainability and well-being of water ecosystems in the world.","PeriodicalId":287947,"journal":{"name":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"78 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132789492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0