2022 IEEE International Conference on Computing (ICOCO)最新文献_第4页

Performance Evaluation of Self-Organising Map Model in Organising the Unstructured Data 自组织映射模型在组织非结构化数据中的性能评价

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10032038

C. C. You, Joi San Tan, Seng Poh Lim, Seng Chee Lim, Chen Kang Lee

{"title":"Performance Evaluation of Self-Organising Map Model in Organising the Unstructured Data","authors":"C. C. You, Joi San Tan, Seng Poh Lim, Seng Chee Lim, Chen Kang Lee","doi":"10.1109/ICOCO56118.2022.10032038","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10032038","url":null,"abstract":"Surface reconstruction becomes a difficult task in reverse engineering when the data obtained during the data acquisition process is unstructured. The unstructured data do not contain the connectivity information required to represent the surface correctly with the least error. Hence, it should be organised to obtain the connectivity information. Various types of Self-Organising Map (SOM) models are utilised in the previous works to organise the unstructured data and represent the surface. However, the performance of the SOM models is affected when different topologies are involved in the organising process. Therefore, the purposes of this experiment are to evaluate the performance of the SOM models with different topologies and to determine the limitation of the various SOM models. The SOM models involved are 2-D SOM, 3-D SOM, Cube Kohonen (CK) SOM, and Spherical SOM (SSOM). Three 3-D unstructured closed surface data sets are applied in this experiment to evaluate the models. The experimental results show that the CKSOM and SSOM models can represent the closed surface correctly with a medium speed. Overall, the CKSOM model performs better than the SSOM model as its grid size can be tuned and it achieved 9 out of 9 minimum error in presenting the surface.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"162 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129151080","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

Rice Price Prediction in Malaysia 马来西亚大米价格预测

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031931

Nor Haziqah Mohd Yusri, Nur Amalina binti Shafie, N. A. M. Ghani

引用次数: 0

Construct an Intelligent Querying System in Education based on Ontology Integration 构建基于本体集成的教育智能化查询系统

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031735

D. Truong, H. Nguyen, Sang Vu, Vuong T. Pham, Diem Nguyen

引用次数: 1

Prediction of Infected Devices Using the Quantification Theory Type 3 Based on MITRE ATT&CK Technique 基于MITRE攻击& ck技术的量化理论3型感染设备预测

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031822

Yosuke Katano, Yukihiro Kozai, Satoshi Okada, Takuho Mitsunaga

{"title":"Prediction of Infected Devices Using the Quantification Theory Type 3 Based on MITRE ATT&CK Technique","authors":"Yosuke Katano, Yukihiro Kozai, Satoshi Okada, Takuho Mitsunaga","doi":"10.1109/ICOCO56118.2022.10031822","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031822","url":null,"abstract":"Reports of cyber attacks are increasing every year. Although many companies, groups, and organizations have taken various measures against cyber attacks, such as security education and attack detection systems. However, it is still practically challenging to prevent security incidents completely and proactively. In addition, attackers continue to attack internally after their initial intrusion. In other words, it is essential to prevent the attacker’s intrusion and quickly identify and stop the damage after the intrusion. However, it takes time and effort to quickly identify the infection status from a large number of logs. The purpose of this research is to identify the infection status of an organization quickly. We hypothesized that the behavior of the initially infected device and the secondary one by lateral movement would be similar. To put it differently, we thought it was possible to detect laterally moved devices based on the similarity between an initially infected device and a secondary one. In this research, we propose a method to find a device secondarily infected by lateral movement. We determine the similarity between the initially infected device and the secondary one by embodying the device’s behavior in terms of MITRE ATT&CK’s Technique. Our experiment results show a substantial similarity between the initially infected device and the secondary one by lateral movement.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125771717","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

ML Detection Method for Malicious Operation in Hybrid Zero Trust Architecture 混合零信任架构下恶意操作的机器学习检测方法

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031702

Koshi Ishide, Satoshi Okada, Mariko Fujimoto, Takuho Mitsunaga

{"title":"ML Detection Method for Malicious Operation in Hybrid Zero Trust Architecture","authors":"Koshi Ishide, Satoshi Okada, Mariko Fujimoto, Takuho Mitsunaga","doi":"10.1109/ICOCO56118.2022.10031702","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031702","url":null,"abstract":"Recently, remote work has become popular due to the widespread of infectious diseases. Many organizations and companies have turned to a Virtual Private Network (VPN) in an attempt to provide secure remote access to their on-premises infrastructure. However, intensive access to such VPN devices places a heavy burden on network performance, and there is also a high risk of cyber-attacks targeting them. Therefore, the demand for zero trust architecture without using VPN devices is increasing these days. However, it takes much time for organizations to introduce a zero trust architecture. Furthermore, it is difficult for some organizations to implement the so-called “ideal zero trust environment” because of some security problems and confidential information management. Thus, it is expected that a hybrid environment in which a zero trust architecture and a conventional on-premises environment coexist is introduced at first in many organizations. In this environment, access logs for each service are distributed in both cloud and on-premise servers. Thus, conventional log-based anomaly detection methods will not work well. In this paper, we propose a method for detecting unauthorized access to such a hybrid environment using machine learning and verify its effectiveness in a virtual environment. As a result, we detect abnormal behavior with high accuracy. Furthermore, based on the experimental results, we discuss how logs should be collected and what kind of log information is useful for anomaly detection in hybrid environments.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123421020","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: 1

Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest 提高类不平衡检测和分类性能:组合样本和随机森林的新潜力

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031922

A. Zakaria, A. Selamat, Lim Kok Cheng, O. Krejcar

引用次数: 1

2022 IEEE Conference on Computing (ICOCO) 2022年IEEE计算大会(ICOCO)

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/icoco56118.2022.10031974

引用次数: 0

Logistic Regression Model for Measuring Perception on Open and Distance Learning (ODL) during COVID-19 Pandemic based on Impeding Factors among Students 基于阻碍因素的新型冠状病毒大流行期间学生开放远程学习感知的Logistic回归模型

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031675

Al Mamun Mizan, Farah Aliah Nur Mansor, N. A. M. Ghani, A. Kamarudin, Mahayaudin M. Mansor, Siti Afiqah Muhamad Jamil, N. Ibrahim

{"title":"Logistic Regression Model for Measuring Perception on Open and Distance Learning (ODL) during COVID-19 Pandemic based on Impeding Factors among Students","authors":"Al Mamun Mizan, Farah Aliah Nur Mansor, N. A. M. Ghani, A. Kamarudin, Mahayaudin M. Mansor, Siti Afiqah Muhamad Jamil, N. Ibrahim","doi":"10.1109/ICOCO56118.2022.10031675","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031675","url":null,"abstract":"Authorities have suggested emergency remote instruction to guarantee that students are not left idle during the pandemic due to the sudden closing of educational facilities. Then for the time being, traditional methods (face-to-face) have been replaced by Open and Distance Learning (ODL). Face-to-face learning was preferred by the majority of students over online learning since students were not able transit to online learning and lacked inspiration. Hence, this study focuses on perception towards ODL during COVID-19 among statistics’ students at FSKM UiTM Shah Alam based on some impeding factors such as social issue, lecturer issue, accessibility issue, academic issue, generic skills and learner intentions. The aim of this study is to investigate the perception of statistics’ students on ODL based on impeding factors and to identify the significant impeding factors effect on statistics students’ perception on ODL. There are 160 observations that are used in this study. The methods that are being used in this study are descriptive analysis and logistic regression. Overall, from the result obtained, students’ perception on ODL are approximately to agree for social issue, academic issue and learner intentions variables. Meanwhile, the significance impeding factors in this study are social issue and learner intentions. This study may help higher education institution to improve and make a better strategy to improve the existing teaching method that have been applied by all lecturers.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133922133","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

Classification of Lung Nodule CT Images Using GAN Variants and CNN 基于GAN变体和CNN的肺结节CT图像分类

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031756

Muhammad Syabil Azman, Farli Rossi, N. Zulkarnain, S. S. Mokri, Ashrani Aizzuddin Abd. Rahni, Nurul Fatihah Ali

{"title":"Classification of Lung Nodule CT Images Using GAN Variants and CNN","authors":"Muhammad Syabil Azman, Farli Rossi, N. Zulkarnain, S. S. Mokri, Ashrani Aizzuddin Abd. Rahni, Nurul Fatihah Ali","doi":"10.1109/ICOCO56118.2022.10031756","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031756","url":null,"abstract":"Global Cancer Statistics 2020 states that there are 2.2 million lung cancer cases worldwide with 1.8 million deaths. At present, deep learning based CAD system for lung nodules classification has been extensively explored. However, this approach requires a great size of images which becomes an issue for medical images. Thus, Generative Advesarial Network (GAN) is introduced to ease this limitation by creating synthetic images. In this study, four GAN architectures namely Deep Convolutional (DCGAN), Deep Regret Analytic GAN (DRAGAN), Wasserstein GAN (WGAN) and Wasserstein GAN with Gradient Penalty (WGANGP) are used in generating synthetic medical images which are then used to classify the lung lesions into benign and malignant via ShuffleNet. The classification is assessed based on pecificity, accuracy, sensitivity, and values of AUC-ROC. Experimental results show that DRAGAN achieved the lowest Fréchet Inception Distance (FID) score of 137.48 of the new generated datasets followed by the WGAN-GP (158.86), WGAN (176.86) and DCGAN (172.56). However, due to the lack of diversity in datasets of DRAGAN, instead WGAN-GP ShuffleNet performed the best in the classification task achieving 98.87% of accuracy, 98.36% of specificity, 99.34% of sensitivity and highest AUC among others at 99.96%. Overall, both high quality and well diversed synthetic images are equally important for the lung nodules classification problem.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132765697","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

Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification 基于机器学习的物联网设备识别中流量表示对准确率下降影响的初步研究

2022 IEEE International Conference on Computing (ICOCO) Pub Date : 2022-11-14 DOI: 10.1109/ICOCO56118.2022.10031725

Nik Aqil, Firdaus Afifi, Faiz Zaki, N. B. Anuar

{"title":"Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification","authors":"Nik Aqil, Firdaus Afifi, Faiz Zaki, N. B. Anuar","doi":"10.1109/ICOCO56118.2022.10031725","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031725","url":null,"abstract":"The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"925 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113981878","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