Journal of Information and Intelligence最新文献

{"title":"A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges","authors":"","doi":"10.1016/j.jiixd.2023.12.001","DOIUrl":"10.1016/j.jiixd.2023.12.001","url":null,"abstract":"<div><div>The Internet of Things (IoT) has set the way for the continuing digitalization of society in various manners during the past decade. The IoT is a vast network of intelligent devices exchanging data online. The security component of IoT is crucial given its rapid expansion as a new technology paradigm since it may entail safety-critical procedures and the online storage of sensitive data. Unfortunately, security is the primary challenge when adopting Internet of Things (IoT) technologies. As a result, manufacturers’ and academics’ top priority now is improving the security of IoT devices. A substantial body of literature on the subject encompasses several issues and potential remedies. However, most existing research fails to offer a comprehensive perspective on attacks inside the IoT. Hence, this survey aims to establish a structure to guide researchers by categorizing attacks in the taxonomy according to various factors such as attack domains, attack threat type, attack executions, software surfaces, IoT protocols, attacks based on device property, attacks based on adversary location and attacks based on information damage level. This is followed by a comprehensive analysis of the countermeasures offered in academic literature. In this discourse, the countermeasures proposed for the most significant security attacks in the IoT are investigated. Following this, a comprehensive classification system for the various domains of security research in the IoT and Industrial Internet of Things (IIoT) is developed, accompanied by their respective remedies. In conclusion, the study has revealed several open research areas pertinent to the subject matter.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 6","pages":"Pages 455-513"},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139019229","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}

{"title":"Virtual electromagnetic environment modeling based data augmentation for drone signal identification","authors":"Hanshuo Zhang ,&nbsp;Tao Li ,&nbsp;Yongzhao Li ,&nbsp;Zhijin Wen","doi":"10.1016/j.jiixd.2023.08.002","DOIUrl":"10.1016/j.jiixd.2023.08.002","url":null,"abstract":"<div><p>Radio frequency (RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence, which has become indispensable for drone surveillance systems. However, since drones operate in unlicensed frequency bands, a large number of co-frequency devices exist in these bands, which brings a great challenge to traditional signal identification methods. Deep learning techniques provide a new approach to complete end-to-end signal identification by directly learning the distribution of RF data. In such scenarios, due to the complexity and high dynamics of the electromagnetic environments, a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network (NN) for identifying drones. In reality, signal acquisition and labeling that meet the above requirements are too costly to implement. Therefore, we propose a virtual electromagnetic environment modeling based data augmentation (DA) method to improve the diversity of drone signal data. The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch. Furthermore, considering the limited processing capability of RF receivers, we modify the original YOLOv5s model to a more lightweight version. Without losing the identification performance, more hardware-friendly designs are applied and the number of parameters decreases about 10-fold. For performance evaluation, we utilized a universal software radio peripheral (USRP) X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario. Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 4","pages":"Pages 308-320"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000549/pdfft?md5=4b920cfffcd3ff0aed9277f6c038530d&pid=1-s2.0-S2949715923000549-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75714017","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}

{"title":"Convolution neural network and 77 ​GHz millimeter wave radar based intelligent liquid classification system","authors":"Jiayu Chen,&nbsp;Xinhuai Wang,&nbsp;Yin Xu,&nbsp;Ye Peng,&nbsp;Wen Wang,&nbsp;Junyan Xiang,&nbsp;Qihang Xu","doi":"10.1016/j.jiixd.2023.06.001","DOIUrl":"10.1016/j.jiixd.2023.06.001","url":null,"abstract":"<div><p>An intelligent liquid classification system based on 77 ​GHz ​millimeter wave radar and convolution neural network are proposed in this paper. The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time. The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system. The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained. The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy. The result of the experiment is explained and the limitations of the experiment are discussed. It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification. The advantages of real time, non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 4","pages":"Pages 352-363"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000240/pdfft?md5=1cebbe1c620b36aad1661a58580268ea&pid=1-s2.0-S2949715923000240-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75229978","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}

{"title":"Robust peer-to-peer learning via secure multi-party computation","authors":"Yongkang Luo ,&nbsp;Wenjian Luo ,&nbsp;Ruizhuo Zhang ,&nbsp;Hongwei Zhang ,&nbsp;Yuhui Shi","doi":"10.1016/j.jiixd.2023.08.003","DOIUrl":"10.1016/j.jiixd.2023.08.003","url":null,"abstract":"<div><p>To solve the data island problem, federated learning (FL) provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation. Peer-to-peer (P2P) federated learning further improves the robustness of the system, in which there is no server and each client communicates directly with the other. For secure aggregation, secure multi-party computing (SMPC) protocols have been utilized in peer-to-peer manner. However, the ideal SMPC protocols could fail when some clients drop out. In this paper, we propose a robust peer-to-peer learning (RP2PL) algorithm via SMPC to resist clients dropping out. We improve the segment-based SMPC protocol by adding a check and designing the generation method of random segments. In RP2PL, each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training. Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 4","pages":"Pages 341-351"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000550/pdfft?md5=bc876c86904042971fa81e6e58d46700&pid=1-s2.0-S2949715923000550-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77738320","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}

{"title":"Modeling and coverage analysis of heterogeneous sub-6GHz-millimeter wave networks","authors":"Cheng Pan ,&nbsp;Yi Guo ,&nbsp;Gang Liu ,&nbsp;Haiyang Ding ,&nbsp;Zhihang Fu","doi":"10.1016/j.jiixd.2023.06.002","DOIUrl":"10.1016/j.jiixd.2023.06.002","url":null,"abstract":"<div><p>The joint adoption of sub-6GHz and millimeter wave (mmWave) technology can prevent the blind spots of coverage, enabling comprehensive coverage while realizing high-speed communication rate. According to the sensitivity of mmWave, base stations should be more densely deployed, which is not well described by existing Poisson hole process (PHP) and the Poisson point process (PPP) models. This paper establishes a sub-6GHz and mmWave hybrid heterogeneous cellular network based on the modified Poisson hole process (MPHP). In our proposed model, the sub-6GHz base stations follow the PPP, and the mmWave base stations (MBSs) follow MPHP distribution. The expressions of the coverage probability are derived by using the interference calculation method of integrating the nearest sector exclusion area. Our theoretical analysis has been verified through simulation results, suggesting that the increase in the cell radius decreases the coverage probability of signal-to-interference-plus-noise ratio (SINR), whereas the increase in the sector parameter has the opposite effect. The variation of sub-6GHz base stations (SBSs) density imposes more significant impact than the MBSs on the SINR coverage probability. In addition, the decrease in MBSs density will reduce the average bandwidth allocated to the user equipment (UE), thus reducing the rate coverage probability.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 4","pages":"Pages 321-329"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000252/pdfft?md5=eec6bbb4eb23cd6b9124579fd0e57b91&pid=1-s2.0-S2949715923000252-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79225631","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}

{"title":"Pri-EMO: A universal perturbation method for privacy preserving facial emotion recognition","authors":"Yong Zeng,&nbsp;Zhenyu Zhang,&nbsp;Jiale Liu,&nbsp;Jianfeng Ma,&nbsp;Zhihong Liu","doi":"10.1016/j.jiixd.2023.08.001","DOIUrl":"10.1016/j.jiixd.2023.08.001","url":null,"abstract":"<div><p>Facial emotion have great significance in human-computer interaction, virtual reality and people's communication. Existing methods for facial emotion privacy mainly concentrate on the perturbation of facial emotion images. However, cryptography-based perturbation algorithms are highly computationally expensive, and transformation-based perturbation algorithms only target specific recognition models. In this paper, we propose a universal feature vector-based privacy-preserving perturbation algorithm for facial emotion. Our method implements privacy-preserving facial emotion images on the feature space by computing tiny perturbations and adding them to the original images. In addition, the proposed algorithm can also enable expression images to be recognized as specific labels. Experiments show that the protection success rate of our method is above 95% and the image quality evaluation degrades no more than 0.003. The quantitative and qualitative results show that our proposed method has a balance between privacy and usability.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 4","pages":"Pages 330-340"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000513/pdfft?md5=6acd805c7dcedd8fb30cc2ecf57750e3&pid=1-s2.0-S2949715923000513-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84208076","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}

{"title":"CSC-GCN: Contrastive semantic calibration for graph convolution network","authors":"Xu Yang,&nbsp;Kun Wei,&nbsp;Cheng Deng","doi":"10.1016/j.jiixd.2023.10.001","DOIUrl":"10.1016/j.jiixd.2023.10.001","url":null,"abstract":"<div><p>Graph convolutional networks (GCNs) have been successfully applied to node representation learning in various real-world applications. However, the performance of GCNs drops rapidly when the labeled data are severely scarce, and the node features are prone to being indistinguishable with stacking more layers, causing over-fitting and over-smoothing problems. In this paper, we propose a simple yet effective contrastive semantic calibration for graph convolution network (CSC-GCN), which integrates stochastic identity aggregation and semantic calibration to overcome these weaknesses. The basic idea is the node features obtained from different aggregation operations should be similar. Toward that end, identity aggregation is utilized to extract semantic features from labeled nodes, while stochastic label noise is adopted to alleviate the over-fitting problem. Then, contrastive learning is employed to improve the discriminative ability of the node features, and the features from different aggregation operations are calibrated according to the class center similarity. In this way, the similarity between unlabeled features and labeled ones from the same class is enhanced while effectively reducing the over-smoothing problem. Experimental results on eight popular datasets show that the proposed CSC-GCN outperforms state-of-the-art methods on various classification tasks.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 4","pages":"Pages 295-307"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949715923000598/pdfft?md5=efb05ea241fae4078424c9e6580d2e50&pid=1-s2.0-S2949715923000598-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135762387","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}

{"title":"Information metasurfaces and reconfigurable intelligent surfaces","authors":"Long Li,&nbsp;Rui Zhang,&nbsp;Tie Jun Cui","doi":"10.1016/j.jiixd.2023.07.001","DOIUrl":"https://doi.org/10.1016/j.jiixd.2023.07.001","url":null,"abstract":"","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 3","pages":"Pages 179-181"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701836","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":"Wireless communications empowered by reconfigurable intelligent surfaces: Model-based vs model-free channel estimation","authors":"Li Wei ,&nbsp;Chongwen Huang ,&nbsp;George C. Alexandropoulos ,&nbsp;Ahmet M. Elbir ,&nbsp;Zhaohui Yang ,&nbsp;Zhaoyang Zhang ,&nbsp;Marco Di Renzo ,&nbsp;Mérouane Debbah ,&nbsp;Chau Yuen","doi":"10.1016/j.jiixd.2023.06.010","DOIUrl":"https://doi.org/10.1016/j.jiixd.2023.06.010","url":null,"abstract":"<div><p>Reconfigurable intelligent surfaces (RISs) are lately being attractive for their great potential in future sixth generation wireless communications (6G), which is attributed to their affordable energy consumption and easy integration. However, the large numbers of low-cost reflecting elements comprising RISs impose challenges for channel acquisition in various RIS-based wireless applications, such as RIS-enhanced orthogonal frequency-division multiplexing and multi-user multiple-input multiple-output systems. In this article, we first overview the state-of-the-art RIS hardware architectures designed to assist channel estimation for RIS-empowered wireless communication systems. We also overview existing channel estimation approaches, which are categorized into model-based and model-free techniques, and discuss their advantages and limitations depending on the RIS deployment. Design challenges with RIS-empowered systems in terms of hardware and other parameter limitations are presented, together with future research directions for channel estimation in RIS-based wireless systems, such as RISs with extremely large numbers of elements, multi-hop communications with RISs, and frequency division duplexing for high mobility systems.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 3","pages":"Pages 253-266"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727935","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":"DRL-based max-min fair RIS discrete phase shift optimization for MISO-OFDM systems","authors":"Peng Chen,&nbsp;Huaqian Zhang,&nbsp;Xiao Li,&nbsp;Shi Jin","doi":"10.1016/j.jiixd.2023.06.003","DOIUrl":"https://doi.org/10.1016/j.jiixd.2023.06.003","url":null,"abstract":"<div><p>In this paper, we investigate a reconfigurable intelligent surface (RIS) assisted downlink orthogonal frequency division multiplexing (OFDM) transmission system. Taking into account hardware constraint, the RIS is considered to be organized into several blocks, and each block of RIS share the same phase shift, which has only 1-bit resolution. With multiple antennas at the base station (BS) serving multiple single-antenna users, we try to design the BS precoder and the RIS reflection phase shifts to maximize the minimum user spectral efficiency, so as to ensure fairness. A deep reinforcement learning (DRL) based algorithm is proposed, in which maximum ratio transmission (MRT) precoding is utilized at the BS and the dueling deep Q-network (DQN) framework is utilized for RIS phase shift optimization. Simulation results demonstrate that the proposed DRL-based algorithm can achieve almost optimal performance, while has much less computation consumption.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 3","pages":"Pages 281-293"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49760517","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}