2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)最新文献_第2页

Neural Network-Based Prediction for Lateral Acceleration of Vehicles 基于神经网络的车辆横向加速度预测

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914270

János Kontos, B. Kránicz, Ágnes Vathy-Fogarassy

引用次数: 2

Comparative Analysis of Deep Learning Models for Network Intrusion Detection Systems 网络入侵检测系统深度学习模型的比较分析

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914128

Brenton Budler, Ritesh Ajoodha

{"title":"Comparative Analysis of Deep Learning Models for Network Intrusion Detection Systems","authors":"Brenton Budler, Ritesh Ajoodha","doi":"10.1109/CITDS54976.2022.9914128","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914128","url":null,"abstract":"Detecting network intrusions is an imperative part of the modern cybersecurity landscape. Over the years, researchers have leveraged the ability of Machine Learning to identify and prevent network attacks. Recently there has been an increased interest in the applicability of Deep Learning in the network intrusion detection domain. However, Network Intrusion Detection Systems developed using Deep Learning approaches are being evaluated using the outdated KDD Cup 99 and NSLKDD datasets which are not representative of real-world network traffic. Recent comparisons of these approaches on the more modern CSE-CIC-IDS2018 dataset, fail to address the severe class imbalance in the dataset which leads to significantly biased results. By addressing this class imbalance and performing an experimental evaluation of a Deep Neural Network, Convolutional Neural Network and Long Short-Term Memory Network on the balanced dataset, this research provides deeper insights into the performance of these models in classifying modern network traffic data. The Deep Neural Network demonstrated the best classification performance with the highest accuracy (84.312%) and Fl-Score (83.799%) as well as the lowest False Alarm Rate (2.615%).","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122081106","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

Multimodal Feature Extraction for Memes Sentiment Classification 模因情感分类的多模态特征提取

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914260

Sofiane Ouaari, Tsegaye Misikir Tashu, Tomáš Horváth

引用次数: 3

A multi-round bilinear-map-based secure password hashing scheme 基于多轮双线性映射的安全密码哈希方案

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914189

Csaád Bertók, Andrea Huszti, Tamás Kádek, Zsanett Jámbor

引用次数: 0

Analysis of a typical cell in the uplink cellular network model using stochastic simulation 用随机仿真方法分析上行蜂窝网络中的典型蜂窝模型

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914210

Taisiia Morozova, I. Kaj

引用次数: 2

Smart watch activity recognition using plot image analysis 基于情节图像分析的智能手表活动识别

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914230

A. Alexan, Anca Alexan, S. Oniga

引用次数: 0

Anomaly Detection Using Hybrid Learning for Industrial IoT 基于混合学习的工业物联网异常检测

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914338

Atallo Kassaw Takele, B. Villányi

{"title":"Anomaly Detection Using Hybrid Learning for Industrial IoT","authors":"Atallo Kassaw Takele, B. Villányi","doi":"10.1109/CITDS54976.2022.9914338","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914338","url":null,"abstract":"The industrial internet of things (IIoT) enhances industrial and manufacturing operations by using smart sensors and actuators. However, it is hampered due to the energy efficiency requirements, real time performance requirements in a dynamic environment, and maintaining the security of applications. Security is a serious issue nowadays and is mostly caused by abnormal traffic of some nodes. For detecting those abnormalities, there are two basic machine learning approaches, namely Federated and Centralized Learning. Centralized Learning has better performance, but it has a privacy issue since edge devices send data to the server. On the other hand, Federate Learning obviates privacy issues, but it has less performance due to the resource limitation of edge devices. In this study, a typical hybrid learning based abnormality detection framework has been proposed in which edge devices undertake Federated Learning with a limited number of datasets and the edge server will use the periodically collected aggregated data from edge devices. For security reasons, edge devices share their data after a certain period of time when the time value of the data has declined. We have used Long Short Term Memory (LSTM) Autoencoders with two different datasets (a smaller for edge devices and a larger for the edge server) for the demonstration. The experimental result shows that the size of the dataset affects the predicting performance and resource utilization in an anomaly detection model.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116076830","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

Using a virtual reality headset in the simulation of the control room of a nuclear power plant 利用虚拟现实耳机在核电站控制室进行模拟

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914190

B. Szabó

引用次数: 1

Testing MPT-GRE Multipath Solution in Vehicular Network V2I Communication 车载网络V2I通信中MPT-GRE多路径方案的测试

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914181

S. Szilágyi, László Kovács

引用次数: 0

Torch: Software Package For The Search Of Linear Binary Codes Torch:用于搜索线性二进制码的软件包

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914052

Carolin Hannusch, Sándor Roland Major

引用次数: 1