Pub Date : 2024-08-14DOI: 10.1109/OJCS.2024.3442921
Hemant Gupta;Amiya Nayak
The Internet of Things (IoT) enables the linkage between the physical and digital domains, with wireless sensor networks (WSNs) playing a vital role in this procedure. The market is saturated with an abundance of IoT devices, a substantial proportion of which are designed for consumer use and have restricted power and memory capabilities. Our analysis found that there is very little research done on defining the security requirements of the IoT ecosystem. A crucial first step in the design process of a secure product entails meticulously scrutinizing and recording the precise security requirements. This paper focuses on defining security requirements for Vehicle-to-Vehicle (V2V) communication systems. The requirements are specified utilizing the Message Queuing Telemetry Transport for Sensor Network (MQTT-SN) communication protocol architecture, specifically tailored for use in sensor networks. The modified Security Requirement Engineering Process (SREP) and Security Quality Requirement Engineering (SQUARE) methodologies have been used in this paper for the case study. The security of the communication between the ClientApp and the road-side infrastructure is our main priority.
{"title":"Publish Subscribe System Security Requirement: A Case Study for V2V Communication","authors":"Hemant Gupta;Amiya Nayak","doi":"10.1109/OJCS.2024.3442921","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3442921","url":null,"abstract":"The Internet of Things (IoT) enables the linkage between the physical and digital domains, with wireless sensor networks (WSNs) playing a vital role in this procedure. The market is saturated with an abundance of IoT devices, a substantial proportion of which are designed for consumer use and have restricted power and memory capabilities. Our analysis found that there is very little research done on defining the security requirements of the IoT ecosystem. A crucial first step in the design process of a secure product entails meticulously scrutinizing and recording the precise security requirements. This paper focuses on defining security requirements for Vehicle-to-Vehicle (V2V) communication systems. The requirements are specified utilizing the Message Queuing Telemetry Transport for Sensor Network (MQTT-SN) communication protocol architecture, specifically tailored for use in sensor networks. The modified Security Requirement Engineering Process (SREP) and Security Quality Requirement Engineering (SQUARE) methodologies have been used in this paper for the case study. The security of the communication between the ClientApp and the road-side infrastructure is our main priority.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"389-405"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/OJCS.2024.3443511
Syed Sameen Ahmad Rizvi;Aryan Seth;Jagat Sesh Challa;Pratik Narang
Detecting facial expressions is a challenging task in the field of computer vision. Several datasets and algorithms have been proposed over the past two decades; however, deploying them in real-world, in-the-wild scenarios hampers the overall performance. This is because the training data does not completely represent socio-cultural and ethnic diversity; the majority of the datasets consist of American and Caucasian populations. On the contrary, in a diverse and heterogeneous population distribution like the Indian subcontinent, the need for a significantly large enough dataset representing all the ethnic groups is even more critical. To address this, we present InFER++, an India-specific, multi-ethnic, real-world, in-the-wild facial expression dataset consisting of seven basic expressions. To the best of our knowledge, this is the largest India-specific facial expression dataset. Our cross-dataset analysis of RAF-DB vs InFER++ shows that models trained on RAF-DB were not generalizable to ethnic datasets like InFER++. This is because the facial expressions change with respect to ethnic and socio-cultural factors. We also present LiteXpressionNet, a lightweight deep facial expression network that outperforms many existing lightweight models with considerably fewer FLOPs and parameters. The proposed model is inspired by MobileViTv2 architecture, which utilizes GhostNetv2 blocks to increase parametrization while reducing latency and FLOP requirements. The model is trained with a novel objective function that combines early learning regularization and symmetric cross-entropy loss to mitigate human uncertainties and annotation bias in most real-world facial expression datasets.
{"title":"InFER++: Real-World Indian Facial Expression Dataset","authors":"Syed Sameen Ahmad Rizvi;Aryan Seth;Jagat Sesh Challa;Pratik Narang","doi":"10.1109/OJCS.2024.3443511","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3443511","url":null,"abstract":"Detecting facial expressions is a challenging task in the field of computer vision. Several datasets and algorithms have been proposed over the past two decades; however, deploying them in real-world, in-the-wild scenarios hampers the overall performance. This is because the training data does not completely represent socio-cultural and ethnic diversity; the majority of the datasets consist of American and Caucasian populations. On the contrary, in a diverse and heterogeneous population distribution like the Indian subcontinent, the need for a significantly large enough dataset representing all the ethnic groups is even more critical. To address this, we present InFER++, an India-specific, multi-ethnic, real-world, in-the-wild facial expression dataset consisting of seven basic expressions. To the best of our knowledge, this is the largest India-specific facial expression dataset. Our cross-dataset analysis of RAF-DB vs InFER++ shows that models trained on RAF-DB were not generalizable to ethnic datasets like InFER++. This is because the facial expressions change with respect to ethnic and socio-cultural factors. We also present LiteXpressionNet, a lightweight deep facial expression network that outperforms many existing lightweight models with considerably fewer FLOPs and parameters. The proposed model is inspired by MobileViTv2 architecture, which utilizes GhostNetv2 blocks to increase parametrization while reducing latency and FLOP requirements. The model is trained with a novel objective function that combines early learning regularization and symmetric cross-entropy loss to mitigate human uncertainties and annotation bias in most real-world facial expression datasets.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"406-417"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1109/OJCS.2024.3431229
Mumtahina Ahmed;Uland Rozario;Md Mohshin Kabir;Zeyar Aung;Jungpil Shin;M. F. Mridha
Classifying music genres has been a significant problem in the decade of seamless music streaming platforms and countless content creations. An accurate music genre classification is a fundamental task with applications in music recommendation, content organization, and understanding musical trends. This study presents a comprehensive approach to music genre classification using deep learning and advanced audio analysis techniques. In this study, a deep learning method was used to tackle the task of music genre classification. For this study, the GTZAN dataset was chosen for music genre classification. This study examines the challenge of music genre categorization using Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), Support Vector Machine (SVM), k-nearest Neighbors (kNN), and Long Short-term Memory (LSTM) models on the dataset. This study precisely cross-validates the model's output following feature extraction from pre-processed audio data and then evaluates its performance. The modified CNN model performs better than conventional NN models by using its capacity to capture complex spectrogram patterns. These results highlight how deep learning algorithms may improve systems for categorizing music genres, with implications for various music-related applications and user interfaces. Up to this point, 92.7% of the GTZAN dataset's correctness has been achieved on the GTZAN dataset and 91.6% on the ISMIR2004 Ballroom dataset.
{"title":"Musical Genre Classification Using Advanced Audio Analysis and Deep Learning Techniques","authors":"Mumtahina Ahmed;Uland Rozario;Md Mohshin Kabir;Zeyar Aung;Jungpil Shin;M. F. Mridha","doi":"10.1109/OJCS.2024.3431229","DOIUrl":"10.1109/OJCS.2024.3431229","url":null,"abstract":"Classifying music genres has been a significant problem in the decade of seamless music streaming platforms and countless content creations. An accurate music genre classification is a fundamental task with applications in music recommendation, content organization, and understanding musical trends. This study presents a comprehensive approach to music genre classification using deep learning and advanced audio analysis techniques. In this study, a deep learning method was used to tackle the task of music genre classification. For this study, the GTZAN dataset was chosen for music genre classification. This study examines the challenge of music genre categorization using Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), Support Vector Machine (SVM), k-nearest Neighbors (kNN), and Long Short-term Memory (LSTM) models on the dataset. This study precisely cross-validates the model's output following feature extraction from pre-processed audio data and then evaluates its performance. The modified CNN model performs better than conventional NN models by using its capacity to capture complex spectrogram patterns. These results highlight how deep learning algorithms may improve systems for categorizing music genres, with implications for various music-related applications and user interfaces. Up to this point, 92.7% of the GTZAN dataset's correctness has been achieved on the GTZAN dataset and 91.6% on the ISMIR2004 Ballroom dataset.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"457-467"},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1109/OJCS.2024.3428970
Suhaib Chughtai;Zakaria Senousy;Ahmed Mahany;Nouh Sabri Elmitwally;Khalid N. Ismail;Mohamed Medhat Gaber;Mohammed M. Abdelsamea
Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this article, we introduce a novel deep learning model, termed DeepCon which incorporates the divide-and-conquer principle into the classification task. DeepCon has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that DeepCon when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%.
{"title":"DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification","authors":"Suhaib Chughtai;Zakaria Senousy;Ahmed Mahany;Nouh Sabri Elmitwally;Khalid N. Ismail;Mohamed Medhat Gaber;Mohammed M. Abdelsamea","doi":"10.1109/OJCS.2024.3428970","DOIUrl":"10.1109/OJCS.2024.3428970","url":null,"abstract":"Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this article, we introduce a novel deep learning model, termed \u0000<italic>DeepCon</i>\u0000 which incorporates the divide-and-conquer principle into the classification task. \u0000<italic>DeepCon</i>\u0000 has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that \u0000<italic>DeepCon</i>\u0000 when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"380-388"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1109/OJCS.2024.3422686
Frédéric C. G. Bogaerts;Naghmeh Ivaki;José Fonseca
Python is one of the most widely adopted programming languages, with applications from web development to data science and machine learning. Despite its popularity, Python is susceptible to vulnerabilities compromising the systems that rely on it. To effectively address these challenges, developers, researchers, and security teams need to identify, analyze, and mitigate risks in Python code, but this is not an easy task due to the scattered, incomplete, and non-actionable nature of existing vulnerability data. This article introduces a comprehensive dataset comprising 1026 publicly disclosed Python vulnerabilities sourced from various repositories. These vulnerabilities are meticulously classified using widely recognized frameworks, such as Orthogonal Defect Classification (ODC), Common Weakness Enumeration (CWE), and Open Web Application Security Project (OWASP) Top 10. Our dataset is accompanied by patched and vulnerable code samples (some crafted with the help of AI), enhancing its utility for developers, researchers, and security teams. In addition, a user-friendly website was developed to allow its interactive exploration and facilitate new contributions from the community. Access to this dataset will foster the development and testing of safer Python applications. The resulting dataset is also analyzed, looking for trends and patterns in the occurrence of Python vulnerabilities, with the aim of raising awareness of Python security and providing practical, actionable guidance to assist developers, researchers, and security teams in bolstering their practices. This includes insights into the types of vulnerabilities they should focus on, the most exploited categories, and the common errors that programmers tend to make while coding that can lead to vulnerabilities.
{"title":"A Taxonomy for Python Vulnerabilities","authors":"Frédéric C. G. Bogaerts;Naghmeh Ivaki;José Fonseca","doi":"10.1109/OJCS.2024.3422686","DOIUrl":"10.1109/OJCS.2024.3422686","url":null,"abstract":"Python is one of the most widely adopted programming languages, with applications from web development to data science and machine learning. Despite its popularity, Python is susceptible to vulnerabilities compromising the systems that rely on it. To effectively address these challenges, developers, researchers, and security teams need to identify, analyze, and mitigate risks in Python code, but this is not an easy task due to the scattered, incomplete, and non-actionable nature of existing vulnerability data. This article introduces a comprehensive dataset comprising 1026 publicly disclosed Python vulnerabilities sourced from various repositories. These vulnerabilities are meticulously classified using widely recognized frameworks, such as Orthogonal Defect Classification (ODC), Common Weakness Enumeration (CWE), and Open Web Application Security Project (OWASP) Top 10. Our dataset is accompanied by patched and vulnerable code samples (some crafted with the help of AI), enhancing its utility for developers, researchers, and security teams. In addition, a user-friendly website was developed to allow its interactive exploration and facilitate new contributions from the community. Access to this dataset will foster the development and testing of safer Python applications. The resulting dataset is also analyzed, looking for trends and patterns in the occurrence of Python vulnerabilities, with the aim of raising awareness of Python security and providing practical, actionable guidance to assist developers, researchers, and security teams in bolstering their practices. This includes insights into the types of vulnerabilities they should focus on, the most exploited categories, and the common errors that programmers tend to make while coding that can lead to vulnerabilities.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"368-379"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1109/OJCS.2024.3419832
Sara Pyykölä;Niclas Joswig;Laura Ruotsalainen
Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.
非朗伯表面是一种特殊的表面，会产生被称为 "镜面反射 "的特殊反射率，这给工业 SLAM 带来了潜在的问题。本文回顾了与非朗伯表面研究相关的基本表面反射模型、现代最先进的计算机视觉算法和两个公共数据集（KITTI 和 DiLiGenT）。文章介绍了一个新的数据集 SPINS，用于研究导航中的非朗伯表面，并使用 ResNeXt-101-WSL、ORB SLAM 3 和 TartanVO 对数据进行了实证性能评估。文章最后讨论了实证评估结果和调查结果。
{"title":"Non-Lambertian Surfaces and Their Challenges for Visual SLAM","authors":"Sara Pyykölä;Niclas Joswig;Laura Ruotsalainen","doi":"10.1109/OJCS.2024.3419832","DOIUrl":"10.1109/OJCS.2024.3419832","url":null,"abstract":"Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"430-445"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-25DOI: 10.1109/OJCS.2024.3419027
Remya S;Anjali T;Abhishek S;Somula Ramasubbareddy;Yongyun Cho
Whitefly infestations have posed a severe threats to cotton crops in recent years, affecting farmers globally. These little insects consume food on cotton plants, causing leaf damage and lower crop yields. In response to this agricultural dilemma, we developed a novel method for detecting whitefly infestations in cotton fields. To improve pest detection accuracy, we use the combined efficiency of visual transformers and low-cost acoustic sensors. We train the vision transformer with a large dataset of cotton fields with and without whitefly infestations. Our studies yielded encouraging results, with the vision transformer obtaining an amazing 99% accuracy. Surprisingly, this high degree of accuracy is reached after only 10-20 training epochs, outperforming benchmark approaches, which normally give accuracies ranging from 80% to 90%. These outcomes underline the cost-effective potential of the vision transformer in detecting whitefly attacks on cotton crops. Moreover, the successful integration of acoustic sensors and vision transformers opens doors for further research and advancements in the domain of cotton pest detection, promising more robust and efficient solutions for farmers facing the challenges of whitefly infestations.
{"title":"The Power of Vision Transformers and Acoustic Sensors for Cotton Pest Detection","authors":"Remya S;Anjali T;Abhishek S;Somula Ramasubbareddy;Yongyun Cho","doi":"10.1109/OJCS.2024.3419027","DOIUrl":"10.1109/OJCS.2024.3419027","url":null,"abstract":"Whitefly infestations have posed a severe threats to cotton crops in recent years, affecting farmers globally. These little insects consume food on cotton plants, causing leaf damage and lower crop yields. In response to this agricultural dilemma, we developed a novel method for detecting whitefly infestations in cotton fields. To improve pest detection accuracy, we use the combined efficiency of visual transformers and low-cost acoustic sensors. We train the vision transformer with a large dataset of cotton fields with and without whitefly infestations. Our studies yielded encouraging results, with the vision transformer obtaining an amazing 99% accuracy. Surprisingly, this high degree of accuracy is reached after only 10-20 training epochs, outperforming benchmark approaches, which normally give accuracies ranging from 80% to 90%. These outcomes underline the cost-effective potential of the vision transformer in detecting whitefly attacks on cotton crops. Moreover, the successful integration of acoustic sensors and vision transformers opens doors for further research and advancements in the domain of cotton pest detection, promising more robust and efficient solutions for farmers facing the challenges of whitefly infestations.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"356-367"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1109/OJCS.2024.3406142
Suman Majumder;Sangram Ray;Dipanwita Sadhukhan;Mou Dasgupta;Ashok Kumar Das;Youngho Park
Radio Frequency Identification (RFID) promotes the fundamental tracking procedure of the Internet of Things (IoT) network due to its autonomous data collection as well as transfer incurring low costs. To overcome the insecure exchange of tracking data and to prevent unauthorized access, parallel dependency RFID grouping-proof protocol is applied by the reader to authenticate tags simultaneously. However, conventional grouping-proof authentication schemes are not sufficient for the memory constraint RFID tags due to the recurrent utilization of a 128-bit PRNG (Pseudo Random Number Generator) function. Alternatively, the existing parallel-dependency grouping-proof schemes are not able to overcome numerous limitations regarding session establishment, efficient key management, and multicast message communication within the specified group. In this research, a lightweight, secure, and efficient communication protocol is proposed to overcome the aforementioned limitations using Elliptic Curve Cryptography (ECC) and Zero-Knowledge property to establish a session key among the participated tags, reader, and remote server. The proposed scheme can work in offline mode. The proposed ECC-based parallel dependency grouping-proof scheme is referred to as ECC-PDGPP which abides by the rules of the EPC class-1 gen-2 (C1 G2) standard of RFID tags. Finally, the proposed protocol is analyzed using a formal random oracle model and simulated using a well-known AVISPA simulation tool that shows the proposed scheme is well protected against all potential security threats.
射频识别（RFID）因其自主数据收集和传输成本低，促进了物联网（IoT）网络的基本跟踪程序。为了克服跟踪数据交换的不安全性并防止未经授权的访问，读取器采用并行依赖 RFID 防分组协议来同时验证标签。然而，由于需要反复使用 128 位 PRNG（伪随机数发生器）函数，传统的防分组认证方案无法满足受内存限制的 RFID 标签的要求。另外，现有的并行依赖性防分组方案也无法克服会话建立、高效密钥管理和指定组内多播信息通信方面的诸多限制。本研究提出了一种轻量级、安全、高效的通信协议，利用椭圆曲线加密法（ECC）和零知识属性在参与的标签、阅读器和远程服务器之间建立会话密钥，以克服上述限制。建议的方案可以在离线模式下工作。所提出的基于 ECC 的并行防依赖分组方案被称为 ECC-PDGPP，它遵守 RFID 标签的 EPC class-1 gen-2 （C1 G2）标准规则。最后，利用形式随机甲骨文模型对所提出的协议进行了分析，并利用著名的 AVISPA 仿真工具进行了仿真，结果表明所提出的方案能很好地抵御所有潜在的安全威胁。
{"title":"ECC-PDGPP: ECC-Based Parallel Dependency RFID-Grouping-Proof Protocol Using Zero-Knowledge Property in the Internet of Things Environment","authors":"Suman Majumder;Sangram Ray;Dipanwita Sadhukhan;Mou Dasgupta;Ashok Kumar Das;Youngho Park","doi":"10.1109/OJCS.2024.3406142","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3406142","url":null,"abstract":"Radio Frequency Identification (RFID) promotes the fundamental tracking procedure of the Internet of Things (IoT) network due to its autonomous data collection as well as transfer incurring low costs. To overcome the insecure exchange of tracking data and to prevent unauthorized access, parallel dependency RFID grouping-proof protocol is applied by the reader to authenticate tags simultaneously. However, conventional grouping-proof authentication schemes are not sufficient for the memory constraint RFID tags due to the recurrent utilization of a 128-bit PRNG (Pseudo Random Number Generator) function. Alternatively, the existing parallel-dependency grouping-proof schemes are not able to overcome numerous limitations regarding session establishment, efficient key management, and multicast message communication within the specified group. In this research, a lightweight, secure, and efficient communication protocol is proposed to overcome the aforementioned limitations using Elliptic Curve Cryptography (ECC) and Zero-Knowledge property to establish a session key among the participated tags, reader, and remote server. The proposed scheme can work in offline mode. The proposed ECC-based parallel dependency grouping-proof scheme is referred to as ECC-PDGPP which abides by the rules of the EPC class-1 gen-2 (C1 G2) standard of RFID tags. Finally, the proposed protocol is analyzed using a formal random oracle model and simulated using a well-known AVISPA simulation tool that shows the proposed scheme is well protected against all potential security threats.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"329-342"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1109/OJCS.2024.3394928
Yuto Omae;Masaya Mori;Yohei Kakimoto
Decision trees offer the benefit of easy interpretation because they allow the classification of input data based on if–then rules. However, as decision trees are constructed by an algorithm that achieves clear classification with minimum necessary rules, the trees possess the drawback of extracting only minimum rules, even when various latent rules exist in data. Approaches that construct multiple trees using randomly selected feature subsets do exist. However, the number of trees that can be constructed remains at the same scale because the number of feature subsets is a combinatorial explosion. Additionally, when multiple trees are constructed, numerous rules are generated, of which several are untrustworthy and/or highly similar. Therefore, we propose “MAABO-MT” and “GS-MRM” algorithms that strategically construct trees with high estimation performance among all possible trees with small computational complexity and extract only reliable and non-similar rules, respectively. Experiments are conducted using several open datasets to analyze the effectiveness of the proposed method. The results confirm that MAABO-MT can discover reliable rules at a lower computational cost than other methods that rely on randomness. Furthermore, the proposed method is confirmed to provide deeper insights than single decision trees commonly used in previous studies. Therefore, MAABO-MT and GS-MRM can efficiently extract rules from combinatorially exploded decision trees.
{"title":"Multi-Rules Mining Algorithm for Combinatorially Exploded Decision Trees With Modified Aitchison-Aitken Function-Based Bayesian Optimization","authors":"Yuto Omae;Masaya Mori;Yohei Kakimoto","doi":"10.1109/OJCS.2024.3394928","DOIUrl":"10.1109/OJCS.2024.3394928","url":null,"abstract":"Decision trees offer the benefit of easy interpretation because they allow the classification of input data based on if–then rules. However, as decision trees are constructed by an algorithm that achieves clear classification with minimum necessary rules, the trees possess the drawback of extracting only minimum rules, even when various latent rules exist in data. Approaches that construct multiple trees using randomly selected feature subsets do exist. However, the number of trees that can be constructed remains at the same scale because the number of feature subsets is a combinatorial explosion. Additionally, when multiple trees are constructed, numerous rules are generated, of which several are untrustworthy and/or highly similar. Therefore, we propose “MAABO-MT” and “GS-MRM” algorithms that strategically construct trees with high estimation performance among all possible trees with small computational complexity and extract only reliable and non-similar rules, respectively. Experiments are conducted using several open datasets to analyze the effectiveness of the proposed method. The results confirm that MAABO-MT can discover reliable rules at a lower computational cost than other methods that rely on randomness. Furthermore, the proposed method is confirmed to provide deeper insights than single decision trees commonly used in previous studies. Therefore, MAABO-MT and GS-MRM can efficiently extract rules from combinatorially exploded decision trees.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"215-226"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion of human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving to mould the metaverse into a digital counterpart capable of delivering immersive human-like experiences. These applications envisage a future where users effortlessly traverse between physical and digital dimensions. Taking a step forward, affective computing technologies can be utilised to identify users' emotional cues and convey authentic emotions, enhancing genuine, meaningful, and context-aware interactions in the digital world. In this paper, we explore how integrating emotional intelligence can enhance the traditional metaverse, birthing an emotionally intelligent metaverse (EIM). Our work illuminates the multifaceted potential of EIM across diverse sectors, including healthcare, education, gaming, automotive, customer service, human resources, marketing, and urban metaverse cyberspace. Through our examination of these sectors, we uncover how infusing emotional intelligence enriches user interactions and experiences within the metaverse. Nonetheless, this transformative journey is riddled with challenges, and we address the obstacles hindering the realisation of EIM's full potential. By doing so, we lay the groundwork for future research endeavours aimed at further enhancing and refining the captivating journey into the world of EIM.
{"title":"Affective Computing and the Road to an Emotionally Intelligent Metaverse","authors":"Farrukh Pervez;Moazzam Shoukat;Muhammad Usama;Moid Sandhu;Siddique Latif;Junaid Qadir","doi":"10.1109/OJCS.2024.3389462","DOIUrl":"10.1109/OJCS.2024.3389462","url":null,"abstract":"The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion of human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving to mould the metaverse into a digital counterpart capable of delivering immersive human-like experiences. These applications envisage a future where users effortlessly traverse between physical and digital dimensions. Taking a step forward, affective computing technologies can be utilised to identify users' emotional cues and convey authentic emotions, enhancing genuine, meaningful, and context-aware interactions in the digital world. In this paper, we explore how integrating emotional intelligence can enhance the traditional metaverse, birthing an emotionally intelligent metaverse (EIM). Our work illuminates the multifaceted potential of EIM across diverse sectors, including healthcare, education, gaming, automotive, customer service, human resources, marketing, and urban metaverse cyberspace. Through our examination of these sectors, we uncover how infusing emotional intelligence enriches user interactions and experiences within the metaverse. Nonetheless, this transformative journey is riddled with challenges, and we address the obstacles hindering the realisation of EIM's full potential. By doing so, we lay the groundwork for future research endeavours aimed at further enhancing and refining the captivating journey into the world of EIM.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"195-214"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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