2023 32nd International Conference on Computer Communications and Networks (ICCCN)最新文献_第7页

Location Verification of Crowd-Sourced Sensors 众包传感器的位置验证

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230111

Christopher Kitras, Carter Pollan, Kyle Myers, Camille Wirthlin Tischner, Philip Lundrigan

引用次数: 0

Machine Learning Based Intelligent Routing for VDTNs 基于机器学习的VDTNs智能路由

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230185

Shiyi Liu, Haiying Shen, Brian L. Smith, Volker Fessmann

{"title":"Machine Learning Based Intelligent Routing for VDTNs","authors":"Shiyi Liu, Haiying Shen, Brian L. Smith, Volker Fessmann","doi":"10.1109/ICCCN58024.2023.10230185","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230185","url":null,"abstract":"Delay Tolerant Networks (DTNs) are wireless mobile networks resilient to disruptions, significant latency, and incomplete paths between source and destination (S-D). Vehicular Delay Tolerant Network (VDTNs) is a subclass of DTNs. Most existing routing algorithms for VDTNs rely on predetermined rules that are inflexible to changes in node features (such as location, contact history, and node relationships). Moreover, given the extremely dynamic network architecture of the VDTN, the routing protocols must be flexible to the varying node features. Thus, the purpose of this work is to develop an intelligent routing system capable of determining the optimal routing method given a pair of S-D nodes. In this study, a novel self-adaptive routing method for VDTNs based on machine learning (ML) is proposed. Using real-time features of S-D nodes, the proposed method selects the optimal routing method. Using the Opportunistic Network Environment (ONE) simulator based on a real-world taxi trace, we evaluated the performance of our ML-based self-adaptive routing method employing various ML models. Evaluation results show that our method achieves up to 34.34% greater success rate and up to 23.75% lower average message delivery delay than existing methods.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114729834","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

Outdoor Millimeter-Wave Picocell Placement using Drone-based Surveying and Machine Learning 使用无人机测量和机器学习的室外毫米波皮cell放置

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230163

Ian McDowell, Rahul Bulusu, Hem Regmi, Sanjib Sur

{"title":"Outdoor Millimeter-Wave Picocell Placement using Drone-based Surveying and Machine Learning","authors":"Ian McDowell, Rahul Bulusu, Hem Regmi, Sanjib Sur","doi":"10.1109/ICCCN58024.2023.10230163","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230163","url":null,"abstract":"Millimeter-Wave (mmWave) networks rely on carefully placed small base stations called “picocells” for optimal network performance. However, the process of conducting site surveys to identify suitable picocell locations is both expensive and time-consuming. The current low-cost approaches for indoor surveying are often unsuitable for outdoor environments due to the presence of various environmental factors. To address this issue, we present Theia, a drone-based system that predicts outdoor mmWave Signal Reflection Profiles (SRPs) and facilitates picocell placement for optimal network coverage. The drone platform integrates optical systems and a mmWave transceiver to collect depth images and mmWave SRPs of the environment. These datasets are fed into a machine learning model that maps the depth data to SRPs, allowing SRPs to be predicted at previously unseen parts of the environment. Theia then leverages these predictions to identify optimal picocell locations that maximize network coverage and minimize link outages. We evaluate Theia in three large-scale outdoor environments and demonstrate that the proposed design can generalize the deployment method with a little refinement of the model.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130565960","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

A Deep Learning Approach for ECG Authentication on Implantable Medical Devices 植入式医疗设备心电认证的深度学习方法

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230198

Mustafa Mahjeed, Geethapriya Thamilarasu, Nicole Johnson, Christian Alfonso

引用次数: 0

ICCCN 2023 Program Overview ICCCN 2023计划概述

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/icccn58024.2023.10230119

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Depicting Instant Messaging Encrypted Traffic Characteristics through an Empirical Study 通过实证研究描述即时通讯加密流量特征

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230093

Zolboo Erdenebaatar, Riyad Alshammari, B. Nandy, N. Seddigh, Marwa Elsayed, N. Zincir-Heywood

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Copyright Page 版权页

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/icccn58024.2023.10230092

引用次数: 0

Building Identification Using Smartphone Sensors and a Map 使用智能手机传感器和地图进行建筑识别

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230153

Jihoon Lee, Kyungmin Go, Myungchul Kim

{"title":"Building Identification Using Smartphone Sensors and a Map","authors":"Jihoon Lee, Kyungmin Go, Myungchul Kim","doi":"10.1109/ICCCN58024.2023.10230153","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230153","url":null,"abstract":"Building identification refers to the recognition of the identity of a building when the building is sighted. With proper identification, additional useful information can be gained, such as the geographical information of the building and facility information within the building based on the recognized identity. However, existing studies related to building identification require additional information such as building images in advance, or have constraints on the types of possible input images. Therefore, we propose an approach that undertakes building identification using a building boundary map and smartphone sensors. Our approach measures the position of the user and the orientation of the user's view using sensors embedded in a smartphone. We find the building sighted by the user by reducing the area of buildings that can exist in the user's orientation in a step-by-step manner. During the validation of our approach, it identified the buildings with accuracy of up to 83.3% despite the inaccuracy of the building boundary map used and the error inherent in the user's position and orientation based on the smartphone.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123057861","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

Message from the Executive Chair 执行主席的话

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/icccn58024.2023.10230157

引用次数: 0

Ransomware Classification Using Machine Learning 使用机器学习的勒索软件分类

2023 32nd International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2023-07-01 DOI: 10.1109/ICCCN58024.2023.10230176

N. Majd, Torsha Mazumdar

{"title":"Ransomware Classification Using Machine Learning","authors":"N. Majd, Torsha Mazumdar","doi":"10.1109/ICCCN58024.2023.10230176","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230176","url":null,"abstract":"The rise of ransomware has emerged as a pressing concern for the technology industry, demanding prompt action to prevent monetary and ethical exploitation. Therefore, an accurate approach is imperative to identify and thwart such attacks effectively. Most of the prior ransomware detection techniques either are signature-based, which are inefficient to identify new ransomware, or utilize a dynamic analysis, which are complicated and computationally expensive. This paper proposes a feature selection-based framework along with different machine learning and deep learning algorithms that can effectively detect ransomware based on features extracted from the files. We performed various experiments beginning with filter, wrapper and embedded methods of feature selection and then applied Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Extreme Gradient Boost (XGB) and Multi-layer Perceptron (MLP) on a ransomware dataset that contains the features and label from files. The experimental results demonstrate that RF and MLP classifiers with ANOVA filter method of feature selection outperform other methods in terms of accuracy, precision, and recall.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123924373","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