2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第6页

Empirical Measurement of Performance Maintenance of Gradient Boosted Decision Tree Models for Malware Detection 恶意软件检测中梯度增强决策树模型性能维护的实证度量

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415220

Colin Galen, Robert Steele

{"title":"Empirical Measurement of Performance Maintenance of Gradient Boosted Decision Tree Models for Malware Detection","authors":"Colin Galen, Robert Steele","doi":"10.1109/ICAIIC51459.2021.9415220","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415220","url":null,"abstract":"Important for effective, real-world machine learning (ML) or artificial intelligence (AI)-based malware detection systems is that models demonstrate both high discriminative performance at time of training and also demonstrate a high level of performance maintenance over time subsequent to training. That is, it is desirable that the models have a slow rate of performance decline over time as they encounter previously unseen malware threats. The study of malware detection model empirical performance maintenance on real-world data sets has not been widely addressed despite significant work on ML-based malware detection in general. In this work, we evaluate performance maintenance characteristics of models using a large, one million instance malware-goodware dataset spanning executables collected over one year in duration. Based on the outperformance of gradient boosted decision tree-based models, we investigate this category of model further and demonstrate models with performance and performance maintenance superior to that demonstrated in the previous ML-based malware detection literature. Given the large size of the dataset of real-world executables utilized, the insights into model performance maintenance may have valuable implications for real-world ML-based malware detection systems.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130289618","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: 4

Extensive Huffman-tree-based Neural Network for the Imbalanced Dataset and Its Application in Accent Recognition 基于扩展huffman -tree的非平衡数据集神经网络及其在口音识别中的应用

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415243

Jeremy Merrill, Yu Liang, Dalei Wu

引用次数: 0

Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors 最小-最大反向传播神经网络预测电子期刊访客

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415197

A. Wibawa, Zahra Nabila Izdihar, Agung Bella Putra Utama, Leonel Hernandez, Haviluddin

{"title":"Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors","authors":"A. Wibawa, Zahra Nabila Izdihar, Agung Bella Putra Utama, Leonel Hernandez, Haviluddin","doi":"10.1109/ICAIIC51459.2021.9415197","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415197","url":null,"abstract":"Electronic journal (e-journal) management comprises several aspects, specifically pageviews, sessions, visitors, and new visitors. Sessions or the number of unique visitors from a journal page is an essential indicator of a journal's outcome. Therefore, it is necessary to forecast the number of unique visitors to determine the strategy for developing a journal. Thus, it is expected to be able to accelerate the journal accreditation system in the future. In this study, this paper predicts the number of unique visitors to the journal by developing a time series forecasting model. Forecasting was done by applying the Backpropagation. The method has the advantage of being able to adapt to changes that occur in the input and output values. There are three time series data input models for this research, specifically three days, seven days and 14 days. The accuracy of forecasting results was measured using the MAPE evaluation of several forecasting models and BPNN architecture. The results show that the best forecasting is using forecasting model 1 and architecture 2-5-1 with an accuracy value of 69.9%. Thus, the performance of the Neural Network in this study is relatively good.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124685554","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: 10

A Design of Learning based Human Positioning Algorithm using Images 基于图像学习的人体定位算法设计

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415237

Tae-Wan Kim, Dong Myung Lee

引用次数: 0

Optimal Energy Management Strategy for ESS with Day Ahead Energy Prediction 具有日前能量预测的ESS最优能量管理策略

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415283

Md. Morshed Alam, Md. Faisal Ahmed, I. Jahan, Y. Jang

引用次数: 3

Intrusion Measurement and Detection in LAN Using Protocol-Wise Associative Memory 基于协议关联内存的局域网入侵测量与检测

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415195

Yuwei Sun, H. Ochiai, H. Esaki

引用次数: 0

A study on the selection of future AI+X promising fields and the direction to strengthen competitiveness 未来AI+X前景领域的选择及增强竞争力方向研究

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415181

Jee-Sun Oh, Moon-Koo Kim, D. Lee

{"title":"A study on the selection of future AI+X promising fields and the direction to strengthen competitiveness","authors":"Jee-Sun Oh, Moon-Koo Kim, D. Lee","doi":"10.1109/ICAIIC51459.2021.9415181","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415181","url":null,"abstract":"the rapid development of artificial intelligence technology has an important impact across many industries and societies. AI is useful in a variety of industries such as machine translation, medical image analysis, and healthcare fields such as telemedicine, manufacturing and services, transportation, and energy. On the other hand, negative impacts such as job and labor substitution, human alienation, potential security threats, and malicious use of artificial intelligence technology are also predicted. The field where the possibility of rapid application of AI technology is possible was the highest in the medical field with 42.5%. In addition, in terms of the usability of AI technology, performance creation, and Korea's competitiveness, the medical field was the highest. The service sector ranked second in applicability, usability, performance creation, and competitiveness. The transportation sector ranked third in applicability, usability, performance creation, and competitiveness. The first factor necessary to create future AI+X domestic competitiveness is the improvement of laws and systems. The second is to establish a code of ethics and the third is to strengthen regulations. Others include reinforcing utilization in society, building infrastructure, global cooperation, fostering venture companies, and expanding technology investment.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116563036","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

Design and implementation of a reference model between services and AI-assisted network 服务与人工智能辅助网络之间参考模型的设计与实现

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415274

Hideki Yamamoto, N. Kondo, Tetsu Joh, T. Warabino, Yusuke Suzuki, Genichi Mori, M. Jibiki

引用次数: 1

DNN based WiFi positioning in 3GPP indoor office environment 基于DNN的3GPP室内办公环境WiFi定位

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415207

S. Oh, J. Kim

{"title":"DNN based WiFi positioning in 3GPP indoor office environment","authors":"S. Oh, J. Kim","doi":"10.1109/ICAIIC51459.2021.9415207","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415207","url":null,"abstract":"As the development of the 4th industry begins, LBS(Location Based Service) technology is drawing attention. AI(Artificial Intelligence), IoT(Internet of Things), and Big Data, which are major technologies of the 4th industry, can be effectively applied to these LBS technologies. In addition, in order to provide LBS technology to users in an indoor environment, the positioning results must be provided in real time. Therefore, in this paper, we propose a scheme for providing real-time user positioning results based on AI technology. The proposed scheme is based on Wi-Fi(Wireless Fidelity) communication, and applies DNN(Deep Neural Network), one of the AI technologies, for location positioning in the indoor office environment proposed by 3GPP(The 3rd Generation Partnership Project). In order to perform the user’s location positioning, the DNN model learns the RSSI(Received Signal Strength Indicator) value of a specific location collected in the offline step and the corresponding location with one label. After that, in the online step, the location of the actual user is estimated based on the learned model. It can be seen that the proposed scheme achieves higher performance than the existing scheme in terms of processing time for performing positioning through simulation. This can be considered in order for the scheme to achieve real-time location positioning later.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128151790","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

Deep Learning Based Hybrid Multiple Access Consisting of SCMA and OFDMA Using User Position Information 基于用户位置信息的SCMA和OFDMA混合多址深度学习

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415180

Yuta Kumagai, Naoya Gonda, Yukiko Shimbo, Hirofumi Suganuma, F. Maehara

引用次数: 1