2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)最新文献_第4页

Collaborative Robot Learning For Indoor Environment 面向室内环境的协同机器人学习

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193717

Lucas Foissey, El-hacen Diallo, K. A. Agha

引用次数: 0

Exploring Efficient Implementation of Delay-based PUF Design on FPGA 基于延迟的PUF设计在FPGA上的高效实现研究

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193305

Shailesh Rajput, Jaya Dofe, K. Sood

{"title":"Exploring Efficient Implementation of Delay-based PUF Design on FPGA","authors":"Shailesh Rajput, Jaya Dofe, K. Sood","doi":"10.1109/ICCNS58795.2023.10193305","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193305","url":null,"abstract":"The Internet of Things (IoT) presents unique challenges for security due to resource limitations. Physically unclonable function (PUF) technology offers a promising solution for cost-effective hardware identification and authentication. PUFs leverage process variations in integrated circuits to produce unique and unreplicable responses. They generate a significant number of challenge-response pairs (CRPs) for authentication. In IoT applications, Arbiter PUF (APUF) and its variants are considered the most appropriate lightweight security primitives. However, implementing a high-quality version of APUF on Field Programmable Gate Arrays (FPGA) has been challenging. This work proposes a practical implementation of APUF and XOR PUF on FPGA, improving flexibility and simplifying implementation. Our experimental analysis shows that APUF and XOR PUF demonstrate a remarkable reliability rate of 98%. Additionally, the distribution of 1s and 0s in the response bit is almost uniform, indicating a randomness of 46.81%. Furthermore, we conduct machine learning modeling attacks to assess the efficacy of the APUF and XOR PUF implementation.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025989","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

DNN Feature Map Gram Matrices for Mitigating White-Box Attacks on Image Classification 缓解图像分类白盒攻击的DNN特征映射图矩阵

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193579

Nathaniel R. Dean, D. Sarkar

{"title":"DNN Feature Map Gram Matrices for Mitigating White-Box Attacks on Image Classification","authors":"Nathaniel R. Dean, D. Sarkar","doi":"10.1109/ICCNS58795.2023.10193579","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193579","url":null,"abstract":"Deep convolutional neural networks accurately classify a diverse range of natural images, but may be easily deceived when designed, imperceptible perturbations are embedded in the images. In this paper, we design a multi-pronged training, input transformation, and image ensemble system that is attack agnostic and not easily estimated. Our system incorporates two novel features. The first is a transformation layer that iteratively filters the input image’s feature maps based on their Gram matrix representational differences to those of randomly sampled training instances from each class, which creates an ensemble of filtered input image copies. The second is a classification system that treats this filtered ensemble as a voting committee that focuses on only those predictions that changed from the original prediction on the unfiltered input image. Our evaluations on the CIFAR10 dataset show our system improves the robustness of an undefended network against a variety of bounded and unbounded white-box attacks under different distance metrics, while sacrificing little accuracy on clean images. Against adaptive full-knowledge attackers creating end-to-end attacks, our system successfully augments the existing robustness of adversarially trained networks, for which our methods are most effectively applied.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127568417","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

Hypercube-based caching: a multidimensional semantic architecture for efficient information mediation 基于超立方体的缓存:用于高效信息中介的多维语义体系结构

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193446

Ouafa Ajarroud, Lahbib Ajallouda, A. Zellou, A. Idri

引用次数: 0

Federated Learning Support for Cybersecurity: Fundamentals, Applications, and Opportunities 网络安全的联邦学习支持:基础，应用和机会

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193279

Rami Mohawesh, Sumbal Maqsood, Y. Jararweh, H. Salameh

{"title":"Federated Learning Support for Cybersecurity: Fundamentals, Applications, and Opportunities","authors":"Rami Mohawesh, Sumbal Maqsood, Y. Jararweh, H. Salameh","doi":"10.1109/ICCNS58795.2023.10193279","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193279","url":null,"abstract":"The term “Federated Learning” (FL) refers to a modern and advanced intelligence system that uses data storage that is not centralised. Most industrialists are hesitant to use the Internet of Everything (IoT) technology since cyberattacks are common and occur in numerous real-time applications around the globe. To deal with this problem, FL can be used to prevent such cyberattacks by offering better cybersecurity. This research intends to fill in some of the gaps between where federated AI is now and where it can be widely used by doing a thorough investigation of FL’s security and privacy features. We provide an informative description of techniques and different implementation styles, while also examining the current difficulties in FL, and we establish a complete evaluation of security and privacy problems that must be taken into account. Our research shows that the privacy risks connected with FL are lower than the security risks. While inference-based attacks pose the greatest risk to FL’s privacy, transmission difficulties, toxicity, and backdoor breaches pose the greatest risk to security. In the final section of the paper, we outline key areas for future study that will help FL adapt to real-world settings.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124249242","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

Keynote Speech 2: Bias and Discrimination in AI Systems: From Single-Identity Dimensions to Multi-Discrimination 主题演讲2:人工智能系统中的偏见和歧视:从单一身份维度到多重歧视

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/mcna59361.2023.10185667

Eirini Ntoutsi

引用次数: 0

An Internet of Things-based System Towards Detecting Oil Spills 基于物联网的溢油检测系统

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193225

M. Khasawneh, A. Azab, S. Alrabaee

引用次数: 0

Cyberbullying Detection on tweets from Twitter using Machine Learning Algorithms 使用机器学习算法对Twitter上的推文进行网络欺凌检测

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193450

Thibaut Balet, Quôoc Vo, Osman Salem, A. Mehaoua

{"title":"Cyberbullying Detection on tweets from Twitter using Machine Learning Algorithms","authors":"Thibaut Balet, Quôoc Vo, Osman Salem, A. Mehaoua","doi":"10.1109/ICCNS58795.2023.10193450","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193450","url":null,"abstract":"Cyberbullying has become a serious concern in today’s society, with the widespread use of social media platforms and other online communication channels. The negative impact of cyberbullying can be severe, ranging from emotional distress to even suicide. In this paper, we explore the use of machine learning algorithms to analyze the content of messages and identify those that contain abusive or harassing language. We used six popular algorithms, namely Logistic Regression (LR), k-Nearest Neighbors (KNN), Random Forest (RF), Classification and Regression Trees (CART), Naive Bayes and Support Vector Machine (SVM), to train and test our models on a dataset of Twitter tweets. To evaluate the performance of these models, we calculated several metrics, including accuracy, F1-score, cross-validation score and receiver operating characteristic (ROC) curve. Results show that Random Forest outperformed the other algorithms with an accuracy of 94%. The results suggest that the proposed method can be an effective automated tool for detecting and preventing cyberbullying on social media platforms like Twitter.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656318","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

Computing the Pull - in Range of Phase Locked Loop Using Particle Swarm Optimization 用粒子群算法计算锁相环的拉入范围

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10192973

B. Harb, Eman Jaradat

引用次数: 0

Machine Learning Model for Measuring Cyclomatic Complexity of Source Code 测量源代码圈复杂度的机器学习模型

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS) Pub Date : 2023-06-19 DOI: 10.1109/ICCNS58795.2023.10193630

A. Odeh, Munther Odeh, Nada Odeh, Hussein Odeh

{"title":"Machine Learning Model for Measuring Cyclomatic Complexity of Source Code","authors":"A. Odeh, Munther Odeh, Nada Odeh, Hussein Odeh","doi":"10.1109/ICCNS58795.2023.10193630","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193630","url":null,"abstract":"Software developers strive to build high-performance and quality software with a very high degree of coherence among its components, simplified structure, and reduced complexity. A cyclomatic complexity measure is used to identify areas of code that are difficult to maintain or debug by determining how many independent paths, conditional and iteration statements there are through the source code. Using AI, it is possible to estimate and measure the cyclomatic complexity of source code by analyzing the structure of the code and identifying any potential areas where complexity could arise. This research aims to utilize deep learning methodologies, specifically for text classification, to measure the Cyclomatic Complexity of source code, it presents a method for measuring source code Cyclomatic Complexity using Multinomial Naive Bayes Machine Learning Model trained on 3270 samples collected from programs written in Python, Java, and C++. The results show an average accuracy of 96% and precision, recall, and f1-score were calculated for different levels of complexity.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129458652","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