IEEE Access最新文献

{"title":"Network Intrusion Detection: An IoT and Non IoT-Related Survey","authors":"Sulyman Age Abdulkareem;Chuan Heng Foh;Mohammad Shojafar;François Carrez;Klaus Moessner","doi":"10.1109/ACCESS.2024.3473289","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473289","url":null,"abstract":"The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing. The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent networks. The issue of security in the realm of the Internet of Things (IoT) has emerged as a significant concern. The security of the Internet of Things (IoT) is compromised by the potential danger posed by exploiting devices connected to the Internet. The susceptibility of Things to botnets poses a significant threat to the entire Internet ecosystem (smart devices). In recent years, there has been a simultaneous evolution in the complexity and variety of security attack vectors. Therefore, it is imperative to analyse IoT methodologies to detect and alleviate emerging security breaches. The present study analyses network datasets, distinguishing between those of the Internet of Things (IoT) and those that do not, and provides a thorough overview of the findings. Our primary focus is on IoT Network Intrusion Detection (NID) studies, wherein we examine the available datasets, tools, and machine learning (ML) techniques employed in the implementation of network intrusion detection (NID). Subsequently, an evaluation, assessment, and summary of the current state-of-the-art research on IoT-related Network Intrusion Detection (NID) conducted between 2018 and 2024 is presented. This includes an analysis of the publication year, dataset, attack types, experiment results, and the advantages, disadvantages, and classifiers employed in the studies. This review emphasises research related to IoT NID that employs Supervised Machine Learning classifiers, owing to the high success rate of such classifiers in security and privacy domains. Furthermore, this survey incorporates a comprehensive analysis of research endeavours on IoT NID. Furthermore, we have identified publicly available IoT datasets that can be utilised for NID experiments, which would benefit academic and industrial research purposes. Moreover, we analyse potential prospects and future advancements. The review’s findings indicate that the Internet of Things (IoT) has been substantiated by its swift proliferation in recent times, leading to even broader network coverage. This study presented conventional datasets gathered over a decade ago and current datasets published within the past decade and utilised in recent research. The survey provides a succinct overview of prevailing research trends in IoT NID for security professionals.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147167-147191"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast and Efficient 191-bit Elliptic Curve Cryptographic Processor Using a Hybrid Karatsuba Multiplier for IoT Applications","authors":"Sumit Singh Dhanda;Brahmjit Singh;Chia-Chen Lin;Poonam Jindal;Deepak Panwar;Tarun Kumar Sharma;Saurabh Agarwal;Wooguil Pak","doi":"10.1109/ACCESS.2024.3472650","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472650","url":null,"abstract":"The most widely used asymmetric cipher is ECC. It can be applied to IoT applications to offer various security services. However, a wide range of sectors have been investigated for applying ECC. The field of elliptic curve cryptographic processors for GF (2191) has received less attention. This study presents a low-resource, high-efficiency architecture for a 191-bit ECC processor. This design uses a novel hybrid Karatsuba multiplier for the multiplication of finite fields. For GF (2191), the Quad-Itoh-Tsuji algorithm has been altered to provide a small-size inversion unit. PlanAhead software synthesizes the CPU, which is then implemented on several Xilinx FPGAs. With savings in slice consumption ranging from 16 to 43 percent, the implemented design is the most restricted compared to the current designs. Compared to previously published designs, it is 3.8–1000 times faster. The elliptic curve scalar multiplication on the Virtex-7 FPGA is computed in \u0000<inline-formula> <tex-math>$7.24~mu $ </tex-math></inline-formula>\u0000s. Additionally, the proposed design achieves savings in area-time products of 77 to 90 percent. It may be beneficial for IoT edge devices. It utilizes 3120 mW of power for the operation. A state-of-the-art comparison based on the figure of merit (FoM) reveals that the proposed design outclasses the newest designs by a large margin. It also exhibits a throughput of 138.121 Kbps.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144304-144315"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAG-BERT-ARL for Fair Automated Video Interview Assessment","authors":"Bimasena Putra;Kurniawati Azizah;Candy Olivia Mawalim;Ikhlasul Akmal Hanif;Sakriani Sakti;Chee Wee Leong;Shogo Okada","doi":"10.1109/ACCESS.2024.3473314","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473314","url":null,"abstract":"Potential biases within automated video interview assessment algorithms may disadvantage specific demographics due to the collection of sensitive attributes, which are regulated by the General Data Protection Regulation (GDPR). To mitigate these fairness concerns, this research introduces MAG-BERT-ARL, an automated video interview assessment system that eliminates reliance on sensitive attributes. MAG-BERT-ARL integrates Multimodal Adaptation Gate and Bidirectional Encoder Representations from Transformers (MAG-BERT) model with the Adversarially Reweighted Learning (ARL). This integration aims to improve the performance of underrepresented groups by promoting Rawlsian Max-Min Fairness. Through experiments on the Educational Testing Service (ETS) and First Impressions (FI) datasets, the proposed method demonstrates its effectiveness in optimizing model performance (increasing Pearson correlation coefficient up to 0.17 in the FI dataset and precision up to 0.39 in the ETS dataset) and fairness (reducing equal accuracy up to 0.11 in the ETS dataset). The findings underscore the significance of integrating fairness-enhancing techniques like ARL and highlight the impact of incorporating nonverbal cues on hiring decisions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"145188-145205"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Based Sensory Glove System to Recognize Bengali Sign Language (BaSL)","authors":"Halima Begum;Oishik Chowdhury;Md. Shakib Rahman Hridoy;Muhammed Mazharul Islam","doi":"10.1109/ACCESS.2024.3472469","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472469","url":null,"abstract":"This paper proposes an AI-based sensory glove system aimed at recognizing Bengali sign language (BaSL) in order to assist the Bengali speech disabled community to overcome the communication barrier. In the proposed design, several sensors such as flex, accelerometer, and gyroscopes were embedded in a hand glove worn by a speech-disabled person to capture the signals generated from the gestures. Two different architectures were proposed to identify the corresponding Bengali word from Bengali sign, one based solely on a convolutional neural network (CNN), and the other - a combination of CNN and long short-term memory (LSTM) network. From the experiment results of the prototype on sign samples related to 41 different Bengali words, it was observed that the average recognition accuracy of the prototype incorporated with CNN and LSTM based architecture was 94.73%, while it is 90.34% for the prototype with CNN based architecture. Experiment results also demonstrated user independent features of the sensory glove system. Moreover, analysis of the performance of the AI-based sensory glove system in terms of latency, user comfort, and battery backup revealed its competitive features compared to other commercially available devices.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"145003-145017"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"StethoNet: Robust Breast Cancer Mammography Classification Framework","authors":"Charalampos Lamprou;Kyriaki Katsikari;Noora Rahmani;Leontios J. Hadjileontiadis;Mohamed Seghier;Aamna Alshehhi","doi":"10.1109/ACCESS.2024.3473010","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3473010","url":null,"abstract":"Despite the emergence of numerous Deep Learning (DL) models for breast cancer detection via mammograms, there is a lack of evidence about their robustness to perform well on new unseen mammograms. To fill this gap, we introduce StethoNet, a DL-based framework that consists of multiple Convolutional Neural Network (CNN) trained models for classifying benign and malignant tumors. StethoNet was trained on the Chinese Mammography Database (CMMD), and tested on unseen images from CMMD, as well as on images from two independent datasets, i.e., the Vindr-Mammo and the INbreast datasets. To mitigate domain-shift effects, we applied an effective entropy-based domain adaptation technique at the preprocessing stage. Furthermore, a Bayesian hyperparameters optimization scheme was implemented for StethoNet optimization. To ensure interpretable results that corroborate with prior clinical knowledge, attention maps generated using Gradient-weighted Class Activation Mapping (GRADCAM) were compared with Regions of Interest (ROIs) identified by radiologists. StethoNet achieved impressive Area Under the receiver operating characteristics Curve (AUC) scores: 90.7% (88.6%-92.8%), 83.9% (76.0%-91.8%), and 85.7% (82.1%-89.4%) for the CMMD, INbreast, and Vindr-Mammo datasets, respectively. These results surpass the current state of the art and highlight the robustness and generalizability of StethoNet, scaffolding the integration of DL models into breast cancer mammography screening workflows.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144890-144904"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Web Service Discovery Framework Based on Improved Biterm Topic Model","authors":"Yuan Yuan;Yegang Du;Jun Pan","doi":"10.1109/ACCESS.2024.3472692","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472692","url":null,"abstract":"Given the proliferation of Web services, effectively identifying the most suitable ones based on user queries poses a formidable challenge. In response to the challenges posed by the reduced efficiency of service discovery methods as the number of services continues to grow and the limited co-occurrence of word frequencies in service description documents, this study proposes an intelligent service discovery framework based on probabilistic topic distribution. The framework utilizes the Biterm Topic Model to extract the probabilistic topic distribution from both service description documents and user requirements. It then performs functional clustering and service matching based on this probabilistic topic distribution, resulting in a set of candidate services. To expedite the training process of the topic model, a topic model training algorithm employing sampling recombination is introduced, which reorganizes the topic sampling process and reduces training time. Additionally, a functional clustering algorithm based on weighted connected graphs is presented to enhance the quality of clustering. Experimental results validate the effectiveness of the proposed framework, which significantly reduces the training time required for the topic model and service discovery while improving the accuracy of service discovery and the normalized discounted cumulative gain.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144437-144455"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in Network Intrusion Detection: A Cross-Dataset Generalization Study","authors":"Marco Cantone;Claudio Marrocco;Alessandro Bria","doi":"10.1109/ACCESS.2024.3472907","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472907","url":null,"abstract":"Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their ability to generalize across diverse networks is a critical factor in their effectiveness and a prerequisite for real-world applications. In this study, we conduct a comprehensive analysis on the generalization of machine-learning-based NIDS through an extensive experimentation in a cross-dataset framework. We employ four machine learning classifiers and utilize four datasets acquired from different networks: CIC-IDS-2017, CSE-CIC-IDS2018, LycoS-IDS2017, and LycoS-Unicas-IDS2018. Notably, the last dataset is a novel contribution, where we apply corrections based on LycoS-IDS2017 to the well-known CSE-CIC-IDS2018 dataset. The results show nearly perfect classification performance when the models are trained and tested on the same dataset. However, when training and testing the models in a cross-dataset fashion, the classification accuracy is largely commensurate with random chance except for a few combinations of attacks and datasets. We employ data visualization techniques in order to provide valuable insights on the patterns in the data. Our analysis unveils the presence of anomalies in the data that directly hinder the classifiers capability to generalize the learned knowledge to new scenarios. This study enhances our comprehension of the generalization capabilities of machine-learning-based NIDS, highlighting the significance of acknowledging data heterogeneity.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144489-144508"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual Cardiac Signal Classifiers: A Deep Learning Classification Approach for Heart Signal Estimation From Video","authors":"Mohamed Moustafa;Muhammad Ali Farooq;Amr Elrasad;Joseph Lemley;Peter Corcoran","doi":"10.1109/ACCESS.2024.3472508","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472508","url":null,"abstract":"Heart rate is a crucial metric in health monitoring. Traditional computer vision solutions estimate cardiac signals by detecting physical manifestations of heartbeats, such as facial discoloration caused by blood oxygenation changes, from subject videos using regression methods. As continuous signals are more complex and expensive to de-noise, this study introduces an alternative approach, employing end-to-end classification models to remotely derive a discrete representation of cardiac signals from face videos. These visual cardiac signal classifiers are trained on discretized cardiac signals, a novel pre-processing method with limited precedent in health monitoring literature. Consequently, various methods to convert continuous cardiac signals into binary form are presented, and their impact on training is evaluated. An implementation of this approach, the temporal shift convolutional attention binary classifier, is presented using the regression-based convolutional attention network architecture. The classifier and a baseline regression model are trained and tested using publicly available and locally collected datasets designed for heart signal detection from face video. The model performance is then assessed based on the heart rate error from the extracted cardiac signals. Results show the proposed method outperforms the baseline on the UBFC-rPPG dataset, reducing cross-dataset root mean square error from 2.33 to 1.63 beats per minute. However, both models struggled to generalize to the PURE dataset, with root mean square errors of 12.40 and 16.29 beats per minute, respectively. Additionally, the proposed approach reduces the computational complexity of model output post-processing, enhancing its suitability for real-time applications and deployment on systems with restricted resources.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144377-144394"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WGCNA and Machine Learning-Based Integrative Bioinformatics Analysis for Identifying Key Genes of Colorectal Cancer","authors":"Md. Al Mehedi Hasan;Md. Maniruzzaman;Jungpil Shin","doi":"10.1109/ACCESS.2024.3472688","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472688","url":null,"abstract":"Colorectal cancer (CC) is a significant public health concern and make it necessary to identify reliable biomarkers and elucidate their molecular and biological mechanisms. This study proposed a system by integrating weighted gene co-expression network analysis (WGCNA) and machine learning-based integrative bioinformatics (ML-IB) analysis to identify key genes for CC. WGCNA was implemented to find a co-expression network of genes and identify important genes by intersecting gene sets obtained using module membership and gene significance criteria across datasets. WGCNA-based significant genes were determined by intersecting important genes between two datasets. ML-IB based approach primarily identified differentially expressed genes (DEGs), then employed support vector machine to determine differentially expressed discriminative genes (DEDGs) and took their common DEDGs across datasets. Protein-protein interaction networks were built and identified hub genes based on the degrees of connectivity and hub module genes using MCODE scores. The ML-IB based significant genes were determined by intersecting hub genes and hub module genes. Four common significant genes were found by intersecting significant genes derived from WGCNA and ML-IB based perspectives. Finally, two genes (AURKA and CCNA2) were determined as key genes for showing strong correlation with survival of CC patients and validated their discriminative capability on an independent test dataset using AUC analysis. The key genes of AURKA and CCNA2 may be used for the early detection of patients with CC. This study will helpful for physicians and doctors to determine and understand the associated the molecular mechanisms and pathway of patients with CC.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144350-144363"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outage Analysis of a Cognitive Radio System With Nakagami-m Fading and Cooperative Decode and Forward Relaying","authors":"Muhammad Farooq;Anwar Khan;Musaed Alhussein;Hasan Mahmood;Khursheed Aurangzeb;Surbhi Bhatia Khan","doi":"10.1109/ACCESS.2024.3472895","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3472895","url":null,"abstract":"The advent of the next generation of wireless communication (NextGen) demands substantial investments and collaborative research for the fundamental need of wireless communications. Therefore, cooperative communication plays a vital role in overcoming challenges such as reliability, throughput, and outage trade-offs. We propose a two-transmission phase spectrum-sharing protocol, employing cooperative decode-and-forward relaying to grant spectrum access for both primary and secondary users. The system consists of a primary sender (PS), primary recipient (PR), secondary sender (SS), and secondary recipient (SR), linked as PS-SS, PS-PR, PS-SR, SS-SR, and SS-PR. In the first transmission phase, PS broadcasts the primary signal, received by PR, SS, and SR. SS regenerates the primary signal after successful reception, combining it linearly with the secondary signal, and allocating power fractions \u0000<inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>\u0000 and (\u0000<inline-formula> <tex-math>$1 - epsilon $ </tex-math></inline-formula>\u0000) to the primary and secondary signals. SS then broadcasts the combined signal in the second transmission phase. Analysis reveals a threshold for optimal performance when SS is within range of PS. Beyond this threshold, the outage performance for the primary user equals or exceeds the case without spectrum sharing. The outage performance for the secondary system is also quantified. The performance of both the primary and secondary users is assessed by deriving a closed-form expression for the outage probability using the Nakagami-m distribution. The effectiveness of the scheme is affirmed through analytical and simulation results.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"144565-144578"},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}