2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第8页

Iot Network Behavioral Fingerprint Inference With Limited Network Traces For Cyber Investigation 基于有限网络痕迹的物联网网络行为指纹推理

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415273

Jonathan Pan

{"title":"Iot Network Behavioral Fingerprint Inference With Limited Network Traces For Cyber Investigation","authors":"Jonathan Pan","doi":"10.1109/ICAIIC51459.2021.9415273","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415273","url":null,"abstract":"The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in cyber and physical realms. In order to manage such destructive effects, incident responders and cyber investigators require the capabilities to find these rogue IoT, contain them quickly and protect other legitimate IoTs from attacks. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT’s network behavioral fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there are limited network activity traces. This research proposes a novel model construct that learns to infer the network behavioral fingerprint of specific IoT based on limited network activity traces using a One-Class Time Series Meta-learner called DeepNetPrint. Our research demonstrated our model to perform comparative well to supervised machine learning model trained with lots of network activity traces to identify IoT devices.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"4 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":"133789164","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: 3

Design of MIMO C-OOK using Matched filter for Optical Camera Communication System 光学摄像机通信系统中使用匹配滤波器的MIMO C-OOK设计

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415204

Huy Nguyen, Y. Jang

引用次数: 9

Volumetric Human Reconstruction from a Single Depth Map 基于单一深度图的人体体积重建

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415231

Ju-Mi Kang, J. Yoon, Minho Lee, Jewoo Kim, Min-Gyu Park

引用次数: 0

ML-Based Localizing and Driving Direction Estimation System for Vehicular Networks 基于ml的车辆网络定位与行驶方向估计系统

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415282

Abduladhim Ashtaiwi

{"title":"ML-Based Localizing and Driving Direction Estimation System for Vehicular Networks","authors":"Abduladhim Ashtaiwi","doi":"10.1109/ICAIIC51459.2021.9415282","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415282","url":null,"abstract":"Road traffic injuries continuously claim the lives of millions of people besides millions of disabled people every year and causing tremendous economic loss. Numerous safety-critical applications, for instance, pre-crash sensing, blind spot warning, emergency braking, lane changing, cooperative collision avoiding, curve speed warning, etc, are proposed in Vehicular Ad Hoc Networks (VANETs) to mitigate road traffic injuries. Both, this vehicle’s position and the position of other road participants in the vicinity is a critical condition for the success of safety-critical applications. Many technologies and techniques are proposed, but no one technology is yet able to satisfy the accuracy, integrity, continuity, and availability needed by safety-critical application. Hence, integrating effective positioning or localizing techniques into one system is the solution. This work proposes a Localizing and Driving Direction Estimation System (LDDES) that estimates the position and driving direction of other vehicles in the vicinity. LDDES created using Machine Learning (ML), VANETs, and Multiple Input Multiple Output (MIMO) capabilities. The performance evaluation of LDDES shows that LDDES estimates the location and driving direction of other vehicles with an accuracy percentage of 90 %. LDDES can be integrated with the Global Position System (GPS) and other positioning techniques to enhance the vehicular positioning requirements needed by many safety-critical applications.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"270 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":"115998290","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

Recognizing human activity using deep learning with WiFi CSI and filtering 使用WiFi CSI和过滤的深度学习来识别人类活动

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415247

Sang-Chul Kim, Yong-Hwan Kim

引用次数: 0

A Chemical Monitoring and Prediction System in Semiconductor Manufacturing Process Using Bigdata and AI Techniques 基于大数据和人工智能技术的半导体制造过程化学监测与预测系统

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415241

Hyung-Min Cho, Kyung-Hee Lee, Peter Shim, A. Park

引用次数: 0

Battery Management using LSTM for Manhole Underground System 基于LSTM的井下人孔系统电池管理

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415285

Himawan Nurcahyanto, Aji Teguh Prihatno, Y. Jang

引用次数: 1

Fast Temporal Information Retrieval In Videos With Visual Memory 具有视觉记忆的视频的快速时间信息检索

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415226

Jungkyoo Shin, Jinyoung Moon

{"title":"Fast Temporal Information Retrieval In Videos With Visual Memory","authors":"Jungkyoo Shin, Jinyoung Moon","doi":"10.1109/ICAIIC51459.2021.9415226","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415226","url":null,"abstract":"Due to recent increases in video usage, there have been many studies about processing and managing information within huge volumes of videos. Existing methods for video retrieval aim to retrieve only similar frames related to a query image and compare all frames to the query image, which is costly in run-time and memory usage. To resolve these limitations, we propose a fast retrieval method for precise temporal information with visual memory. Our model compresses an input video into a compressed visual memory and applies an attention-based layer to obtain the probability of a given query image’s existence. To the best of our knowledge, we are the first to attempt video retrieval for temporal information using visual memory. To show the efficiency and effectiveness of our model, we conducted experiments for temporal information retrieval on 60-second videos from TV shows and dramas. Our model could effectively compress a video to visual memory with space-savings of 93.6% and 99.1% compared to frame features and original video, respectively. Using the compressed visual memory, our method retrieved temporal information at 250K fps, which is 28x and 4,164x faster than retrieval methods using frame features and frames, respectively.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"27 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":"126533425","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

Juris2vec: Building Word Embeddings from Philippine Jurisprudence 从菲律宾法理学中构建词嵌入

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415251

Elmer C. Peramo, C. Cheng, M. Cordel

引用次数: 2

A Time-aware Data Clustering Approach to Predictive Maintenance of a Pharmaceutical Industrial Plant 医药工业设备预测性维护的时间感知数据聚类方法

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415206

Gabriele Calzavara, Eleonora Oliosi, G. Ferrari

{"title":"A Time-aware Data Clustering Approach to Predictive Maintenance of a Pharmaceutical Industrial Plant","authors":"Gabriele Calzavara, Eleonora Oliosi, G. Ferrari","doi":"10.1109/ICAIIC51459.2021.9415206","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415206","url":null,"abstract":"Predictive maintenance is one of the most active fields of study for Industry 4.0, as it is expected to significantly decrease the maintenance costs of the equipment. Often, it is not possible to accurately predict the deterioration of a component, as the reliability of predictive models strongly depends on the available sensory data and on the specific characteristics of the monitored component. In this paper, we present a clustering-based approach with the aim of predicting the time-aware evolution of the health status of a machine component in a pharmaceutical plant. The developed strategy allows to obtain a time segmentation of the component’s operational points, which are then clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). In particular, this approach has the advantage of being general and making use of a limited amount of features extracted from a single sensor signal. The proposed approach becomes attractive when the quantity of single sensory collected data is not sufficient to build a physical model capable of identifying changes in the system status.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"385 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":"124924605","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