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

{"title":"SecureGAN: Secure Three-Party GAN Training","authors":"Sijia Cao, Han Zhang, Yuhang Wang, Jie Lin, Fanyu Kong, Leyun Yu","doi":"10.1109/ICCCN58024.2023.10230199","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230199","url":null,"abstract":"Generating Adversarial Network (GAN) is a prominent unsupervised learning method that utilizes two competing neural networks to generate realistic data, which has been widely employed in image synthesis and data augmentation. Outsourcing GAN training to cloud servers can significantly reduce the computation load on local devices. Furthermore, in outsourcing settings, training data can be gathered from multiple users, leading to larger amounts of data and, as a result, improved training accuracy. However, outsourcing is associated with privacy risks, as training data often contains sensitive information. To address this problem, we propose SecureGAN, a privacy-preserving framework for GAN that aims to protect the privacy of the training input and output. We implement secure protocols based on replicated secret sharing technology to protect the privacy of the linear and nonlinear layers. We conduct experiments using the MP-SPDZ framework, and the results demonstrate the effectiveness of the proposed protocols.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"10 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":"123897458","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}

{"title":"ICCCN 2023 Technical Program","authors":"","doi":"10.1109/icccn58024.2023.10230102","DOIUrl":"https://doi.org/10.1109/icccn58024.2023.10230102","url":null,"abstract":"","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"2 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":"129328766","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}

{"title":"Spam SMS Classification Using Machine Learning","authors":"N. Majd, Mandar Shivaji Hanchate","doi":"10.1109/ICCCN58024.2023.10230203","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230203","url":null,"abstract":"Over the past few years, the use of emails and text messages has drastically increased. Short Message Service (SMS) on cellphone providers and related apps, like Whatsapp, is one of the best and fastest ways to communicate among users. SMSs are used and sent globally for personal and business purposes. However, alongside safe SMSs, the users may receive fraudulent Spam SMSs, which could cause security issues and inconvenient for the users. Numerous Spam messages are being sent daily for both personal and professional benefits. Accurately identifying Spam SMS is a challenge. The objective of this research is to build a model utilizing machine learning and deep learning to understand the semantics of SMSs and classify them to either Spam or non-Spam (Ham). We used a pre-trained BERT model and combined it with several machine learning and deep learning models. The results indicated that BERT+SVC and BERT+BiLSTM performed the best with 99.10% and 99.19% accuracies respectively on the test dataset.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"22 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":"127211727","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}

{"title":"Improved Conflict-Based Search for the Virtual Network Embedding Problem","authors":"Y. Zheng, Srivatsan Ravi, Erik Kline, Lincoln Thurlow, Sven Koenig, T. K. S. Kumar","doi":"10.1109/ICCCN58024.2023.10230188","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230188","url":null,"abstract":"Virtualization is the mechanism of creating virtual representations of physical resources. It is now integrated into almost every facet of computing and is pervasive on the Internet: ranging from data center services and cloud computing services to services on our phones. The common goal for virtualization providers is to ensure that the physical resources are managed efficiently and effectively. This goal induces the Virtual Network Embedding (VNE) problem: the task of properly allocating the physical resources of a network to satisfy virtual requests for resources under various constraints while ensuring the quality of service and maximizing resource utilization. The VNE problem captures many resource allocation tasks arising in computer systems and computer networks. In this paper, we present Improved VNE-CBS (iVNE-CBS) as an efficient and effective algorithm for solving the VNE problem. iVNE-CBS builds on Conflict-Based Search (CBS), a heuristic search framework borrowed from the Multi-Agent Path Finding literature. We show that iVNECBS significantly outperforms popular baseline VNE algorithms: it scales to networks with several hundreds of vertices and thousands of edges, while also producing better-quality solutions.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"76 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":"126212062","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}

{"title":"Towards Multi-Person Gesture Recognition using Commodity Wi-Fi","authors":"Xiaozhuang Liu, Zhenxing Niu, Wenye Wang","doi":"10.1109/ICCCN58024.2023.10230095","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230095","url":null,"abstract":"Comparing the recognition of human gestures using cameras, radar, or LiDAR, a WiFi-based gesture recognition system has distinct advantages, such as being low-cost, being device-free, and having much less privacy leakage. Recently, there have been advancements in WiFi-based gesture recognition, but most of the research has primarily focused on single-person gesture recognition. However, in real-world scenarios like e-learning, it is common for multiple individuals to engage in different actions simultaneously. To this end, this paper focuses on multi-person gesture recognition, which presents two major challenges, that is, the recognition accuracy due to the WiFi signal interference, and the processing time for real-time applications. Multi-person gesture recognition is more challenging than single-person scenario due to the interference caused by the superposition of WiFi signals induced by multiple moving individuals. In this paper, we define a concept of super-gesture and propose a WiFi-based Super-Gesture recognition (WiSG) method. Through the decomposition of the super-gesture's DFS spectrogram by Multi-Motion Trajectory algorithm, we extract modified signals of each person. Moreover, a novel feature called Field Motion Velocity is proposed by fully exploiting the advantages of our multiple transmitter-receiver WiFi sensing system. The proposed feature is not significantly affected by domains such as position, orientation, and other factors irrelevant to gestures. As a result, our approach can effectively recognize gestures across different domains. Evaluation results show that the cross-domain recognition accuracy of our WiSG can achieve up to 89% in multi-person scenario. Moreover, our approach can reduce processing time by 20 times against Widar3.0, which satisfies the requirements of most real-time applications.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"28 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":"122051478","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}

{"title":"Protection of Network Security Selector Secrecy in Outsourced Network Testing","authors":"Sultan Alasmari, Weichao Wang, Aidong Lu, Yu Wang","doi":"10.1109/ICCCN58024.2023.10230113","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230113","url":null,"abstract":"With the emergence and fast development of cloud computing and outsourced services, more and more companies start to use managed security service providers (MSSP) as their security service team. This approach can save the budget on maintaining its own security teams and depend on professional security persons to protect the company infrastructures and intellectual property. However, this approach also gives the MSSP opportunities to honor only a part of the security service level agreement. To prevent this from happening, researchers propose to use outsourced network testing to verify the execution of the security policies. During this procedure, the end customer has to design network testing traffic and provide it to the testers. Since the testing traffic is designed based on the security rules and selectors, external testers could derive the customer network security setup, and conduct subsequent attacks based on the learned knowledge. To protect the network security configuration secrecy in outsourced testing, in this paper we propose different methods to hide the accurate information. For Regex-based security selectors, we propose to introduce fake testing traffic to confuse the testers. For exact match and range based selectors, we propose to use NAT VM to hide the accurate information. We conduct simulation to show the protection effectiveness under different scenarios. We also discuss the advantages of our approaches and the potential challenges.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"53 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":"123565743","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}

{"title":"Location Inference under Temporal Correlation","authors":"Yukun Dong, Yidan Hu, A. Aseeri, Depeng Li, Rui Zhang","doi":"10.1109/ICCCN58024.2023.10230099","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230099","url":null,"abstract":"Location Based Services (LBSs) have become increasingly popular in the past decade, allowing mobile users to access location-dependent information and services. To protect user privacy while using LBSs, various Location Privacy Protection Mechanisms (LPPMs) have been proposed that obfuscate users' true locations through random perturbation. However, adversaries can still exploit the temporal correlation between a user's locations in multiple LBS queries to improve inference accuracy. In this paper, we introduce a novel location inference attack that strikes a good balance between inference accuracy and computational complexity by effectively exploiting temporal correlation. Simulation studies using synthetic and real datasets confirm the advantages of our proposed attack.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"2 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":"126506636","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}

{"title":"An Improved Fire and Smoke Detection Method Based on YOLOv7","authors":"Jie Lian, Xinyu Pan, Jing-lin Guo","doi":"10.1109/ICCCN58024.2023.10230135","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230135","url":null,"abstract":"Fires can cause extensive damage to human life and property, highlighting the importance of accurate flame and smoke detection systems. However, current methods for detecting flame and smoke struggle to balance the demands of real-time processing and prediction accuracy, limiting their applicability in fire warning and detecting systems. In this paper, we propose a novel approach based on the YOLOv7 architecture for efficient and accurate detection of fire and smoke in images. Our approach incorporates partial convolutional layers into the E-ELAN module of the YOLOv7 network, enabling faster and more precise identification of fire and smoke. Furthermore, we introduce the Focal-EIoU loss function to address the issue of fluctuating model loss caused by low-quality samples. We validate our approach on a real-world dataset and report significant improvements in detection accuracy, with a mean average precision of 78.5 and an FPS increase to 63. These results demonstrate the effectiveness of our approach in enhancing the capability of fire and smoke detection systems.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"45 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":"131866443","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}

{"title":"DeepHealth: Geospatial and ML-Based Approach to Identify Health Disparities and Determinants for Improving Pandemic Health Care","authors":"Jinwei Liu, Rui Gong, Long Cheng, Richard A. Aló","doi":"10.1109/ICCCN58024.2023.10230101","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230101","url":null,"abstract":"The COVID-19 pandemic has exacerbated existing health disparities, and its impact has fallen disproportionately on disadvantaged and vulnerable communities. Racial and ethnic minorities such as Black Americans who are at a particular disadvantage are more likely to be the potential target of COVID-19 infection and are dying at alarmingly high rates. Despite a promising solution of the COVID-19 vaccination offers hope, equitable access to COVID-19 vaccines remains a challenge in the US, which has compounded the existing disparities in cases, hospitalizations, and deaths among racial and ethnic minority groups. The deep and pervasive history of medical racism in the US has led to the vaccine hesitancy in racial and ethnic minorities, and thereby caused the disparities. Although some studies examine determinants of health disparities (e.g., social health determinants), there is a shortage of studies examining the social, structural and constructural health determinants, either alone or in tandem with other determinants. Little research paid attention to leveraging geographic information to trace the social, structural and constructural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepHealth, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the social, structural, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities based on multiple COVID-19 datasets and examine the social, structural, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in incidence and mortality rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. This research provides new strategies for health disparity identification and determinant tracking with a goal of mitigating health disparities and improving pandemic health care. The research suggests that policymakers should give attention to initiatives that will protect the health of populations (i.e., an upstream approach to reducing health disparities) rather than solely focusing only on providing health and social services.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"51 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":"129718037","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}

{"title":"Automatic Synthesis of Network Security Services: A First Step","authors":"Lei Xu, Yangyong Zhang, Phakpoom Chinprutthiwong, G. Gu","doi":"10.1109/ICCCN58024.2023.10230115","DOIUrl":"https://doi.org/10.1109/ICCCN58024.2023.10230115","url":null,"abstract":"In the network security life cycle, security needs are initialized by network operators and typically documented in natural languages, and later implemented and deployed in developed/acquired security appliances, typically written in a programming language by third-party developers. However, oftentimes, those security appliances/programs may not quite match the urgent and fast-evolving security needs since the whole developing/deployment procedure is very time-consuming. In this paper, we propose a novel framework, AUTOSEC, to aid network operators in building up or rapid prototyping operational network security services directly from high-level service needs as automatically as possible. AUTOSEC helps bridge the huge gap from human intents in natural language descriptions to the deliverable network security services. More specifically, AUTOSEC utilizes Natural Language Processing (NLP) techniques to infer security intents from natural language descriptions, and then performs Interactive Synthesis to assist users to validate and refine parsed intents if necessary. AUTOSEC further lever-ages Software-Defined Networking (SDN) and Network Function Virtualization (NFV) techniques to automatically compose and instantiate security services in terms of refined security intents. In the evaluation, we demonstrate the early success of AUTOSEC with security policy descriptions collected from various data sources including research papers, appliance descriptions, real-world security standards, and human-written policies.","PeriodicalId":132030,"journal":{"name":"2023 32nd International Conference on Computer Communications and Networks (ICCCN)","volume":"56 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":"131229089","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}