High-Confidence Computing最新文献

{"title":"Enhancement of IoT device security using an Improved Elliptic Curve Cryptography algorithm and malware detection utilizing deep LSTM","authors":"R. Aiyshwariya Devi,&nbsp;A.R. Arunachalam","doi":"10.1016/j.hcc.2023.100117","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100117","url":null,"abstract":"<div><p>Internet of things (IoT) has become more popular due to the development and potential of smart technology aspects. Security concerns against IoT infrastructure, applications, and devices have grown along with the need for IoT technologies. Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic, ever-changing environment, and simply applying basic security requirements is dangerous. Therefore, this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets. The malware detection approach is designed with the aid of a deep learning approach. The initial process is identifying attack nodes from normal nodes through a trust value using contextual features. After discovering attack nodes, these are considered for predicting different kinds of attacks present in the network, while some preprocessing and feature extraction strategies are applied for effective classification. The Deep LSTM classifier is applied for this malware detection approach. Once completed malware detection, prevention is performed with the help of the Improved Elliptic Curve Cryptography (IECC) algorithm. A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission. Python 3.8 software is used to test the performance of the proposed approach, and several existing techniques are considered to evaluate its performance. The proposed approach obtained 95% of accuracy, 5% of error value and 92% of precision. In addition, the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time. Compared to the other methods, the proposed approach provides better security to IoT gadgets during data transmission.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 2","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200501","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":"DPTP-LICD: A differential privacy trajectory protection method based on latent interest community detection","authors":"Weiqi Zhang ,&nbsp;Guisheng Yin ,&nbsp;Yuxin Dong ,&nbsp;Fukun Chen ,&nbsp;Qasim Zia","doi":"10.1016/j.hcc.2023.100134","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100134","url":null,"abstract":"<div><p>With the rapid development of high-speed mobile network technology and high-precision positioning technology, the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks. Researchers can mine users’ trajectories in Location-based Social Networks to obtain sensitive information, such as friendship groups, activity patterns, and consumption habits. Therefore, mobile users’ privacy and security issues have received growing attention in Location-based Social networks. It is crucial to strike a balance between privacy protection and data availability. This paper proposes a differential privacy trajectory protection method based on latent interest community detection (DPTP-LICD), ensuring strict privacy protection standards and user data availability. Firstly, based on the historical trajectory information of users, spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users. Secondly, the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory, and their priorities are assigned based on a popularity model. A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable. Finally, to prevent privacy leakage, we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points. Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’ privacy from disclosure.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 2","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200503","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":"A review on offloading in fog-based Internet of Things: Architecture, machine learning approaches, and open issues","authors":"Kalimullah Lone ,&nbsp;Shabir Ahmad Sofi","doi":"10.1016/j.hcc.2023.100124","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100124","url":null,"abstract":"<div><p>There is an exponential increase in the number of smart devices, generating helpful information and posing a serious challenge while processing this huge data. The processing is either done at fog level or cloud level depending on the size and nature of the task. Offloading data to fog or cloud adds latency, which is less in fog and more in the cloud. The methods of processing data and tasks at fog level or cloud are mostly machine learning based. In this paper, we will discuss all three levels in terms of architecture, starting from the internet of things to fog and fog to cloud. Specifically, we will describe machine learning-based offloading from the internet of things to fog and fog to cloud. Finally, we will come up with current research directions, issues, and challenges in the IoT–fog–cloud environment.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 2","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200508","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":"Data-driven approach to designing a BCI-integrated smart wheelchair through cost–benefit analysis","authors":"Jenamani Chandrakanta Badajena,&nbsp;Srinivas Sethi,&nbsp;Ramesh Kumar Sahoo","doi":"10.1016/j.hcc.2023.100118","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100118","url":null,"abstract":"<div><p>A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person. This involves accurately collecting inputs from the user during various movement activities and using them to determine their intended motion. These smart wheelchairs work by collecting brain signals in the form of electroencephalography (EEG) signals and by processing them into a quantized format to provide movement assistance to people. Such systems can be referred to as brain–computer interface (BCI) systems that work with EEG signals. Acquiring data from human beings in the form of brain signals through EEG, along with processing of those signals and ensuring the correctness of actions instigated by those brain signals involve a huge amount of data. In this work, we carried out an experiment by taking 100 human subjects and recording their brain signals using a <em>NeuroMax</em> device. Typical wheelchairs are constrained by design as the motion of those is limited either by manual operation or controlled by haptic sensors and actuators. The main objective in this work was to design a wheelchair with better usability and control using machine learning-based knowledge, which is typically a data-driven approach. However, the proposed approach was designed to take inputs from human gestures and brain sensory activities to provide better usability to the wheelchair. The attention meditation cost–benefit analysis (AMCBA) proposed in this paper aims to reduce the risk of inappropriate results and improve performance by considering various cost–benefit parameters. The said classifier aims to improve the quality of emotion recognition by filtering features from EEG signals using methods of feature selection. The operation of the proposed method is described in two steps: in the first step, we assign weights to different channels for the extraction of spatial and temporal information from human behavior. The second step presents the cost–benefit model to improve the accuracy to help in decision-making. Moreover, we tried to assess the performance of the wheelchair for various assumptions and technical specifications. Finally, this study achieves improved performance in the most difficult circumstances to provide a better experience to persons with immobility.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 2","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200500","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":"Decentralizing access control system for data sharing in smart grid","authors":"Kunpeng Liu ,&nbsp;Chenfei Wang ,&nbsp;Xiaotong Zhou","doi":"10.1016/j.hcc.2023.100113","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100113","url":null,"abstract":"<div><p>Smart grid enhances the intelligence of the traditional power grid, which allows sharing varied data such as consumer, production, or energy with service consumers. Due to the untrustworthy networks, there exist potential security threats (e.g., unauthorized access and modification, malicious data theft) hindering the development of smart grid. While several access control schemes have been proposed for smart grid to achieve sensitive data protection and fine-grained identity management, most of them cannot satisfy the requirements of decentralizing smart grid environment and suffer from key escrow problems. In addition, some existing solutions cannot achieve dynamic user management for lacking the privilege revocation mechanism. In this paper, we propose a decentralizing access control system with user revocation to relieve the above problems. We design a new multiple-authority attribute-based encryption (MABE) scheme to keep data confidentiality and adapt decentralizing smart grid applications. We also compare our proposal with the similar solution from both security and performance. The comparing results show that our access control system can achieve a trade-off among confidentiality, authentication, distribution and efficiency in smart grid.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 2","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200499","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":"A novel GPU based Geo-Location Inference Attack on WebGL framework","authors":"Weixian Mai,&nbsp;Yinhao Xiao","doi":"10.1016/j.hcc.2023.100135","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100135","url":null,"abstract":"<div><p>In the past few years, graphics processing units (GPUs) have become an indispensable part of modern computer systems, not only for graphics rendering but also for intensive parallel computing. Given that many tasks running on GPUs contain sensitive information, security concerns have been raised, especially about potential GPU information leakage. Previous works have shown such concerns by showing that attackers can use GPU memory allocations or performance counters to measure victim side effects. However, such an attack has a critical drawback that it requires a victim to install desktop applications or mobile apps yielding it uneasy to be deployed in the real world. In this paper, we solve this drawback by proposing a novel GPU-based side-channel Geo-Privacy inference attack on the WebGL framework, namely, GLINT (stands for <strong>G</strong>eo-<strong>L</strong>ocation <strong>In</strong>ference A<strong>t</strong>tack). GLINT merely utilizes a lightweight browser extension to measure the time elapsed to render a sequence of frames on well-known map websites, e.g., Google Maps, or Baidu Maps. The measured stream of time series is then employed to infer geologically privacy-sensitive information, such as a search on a specific location. Upon retrieving the stream, we propose a novel online segmentation algorithm for streaming data to determine the start and end points of privacy-sensitive time series. We then combine the DTW algorithm and KNN algorithm on these series to conclude the final inference on a user’s geo-location privacy.</p><p>We conducted real-world experiments to testify our attack. The experiments show that GeoInfer can correctly infer more than 83% of user searches regardless of the locations and map websites, meaning that our Geo-Privacy inference attack is accurate, practical, and robust. To counter this attack, we implemented a defense strategy based on Differential Privacy to hinder obtaining accurate rendering data. We found that this defense mechanism managed to reduce the average accuracy of the attack model by more than 70%, indicating that the attack was no longer effective. We have fully implemented GLINT and open-sourced it for future follow-up research.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 4","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193401","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":"Blockchain data secure sharing protocol based on threshold Paillier algorithm","authors":"Likai Jia ,&nbsp;Xiubo Chen ,&nbsp;Luxi Liu ,&nbsp;Xiaoge Wang ,&nbsp;Ke Xiao ,&nbsp;Gang Xu","doi":"10.1016/j.hcc.2023.100132","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100132","url":null,"abstract":"<div><p>With the development of Internet technology, secure storage and secure sharing of data have become increasingly important. Traditional data sharing schemes exist a series of problems including lack of security and low efficiency. In this paper, we construct a secure and efficient data sharing scheme based on threshold Paillier algorithm and blockchain technology, which achieves secure data storage and sharing without a third-party institution. Firstly, we propose a <span><math><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>l</mi><mo>)</mo></mrow></math></span> threshold Paillier blockchain data sharing scheme, which effectively prevents decryption failures caused by the loss of a single node’s private key. Secondly, we propose a combined on-chain and off-chain data storage scheme, we store the ciphertext on the cloud server and the ciphertext hash value on the blockchain, which not only ensures the integrity of the data but also solves the storage limitation problem on the blockchain. Finally, we use the simulation paradigm to prove the security of the scheme in the semi-honest model. The discussion results of the comparison and the analysis of performance show that the blockchain data security sharing scheme proposed in this paper has lower computational overhead and higher security than other similar schemes.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 4","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193399","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":"Capsule networks embedded with prior known support information for image reconstruction","authors":"Meng Wang ,&nbsp;Ping Yang ,&nbsp;Yahao Zhang","doi":"10.1016/j.hcc.2023.100125","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100125","url":null,"abstract":"<div><p>Compressed sensing (CS) has been successfully applied to realize image reconstruction. Neural networks have been introduced to the CS of images to exploit the prior known support information, which can improve the reconstruction quality. Capsule Network (Caps Net) is the latest achievement in neural networks, and can well represent the instantiation parameters of a specific type of entity or part of an object. This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework. The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest. To lead the dynamic routing to the most likely index, a group of prediction vectors is designed determined by the information. Furthermore, the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms. It is concluded that the proposed capsule network (Caps Net) creates higher reconstruction quality at nearly the same time with traditional Caps Net.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 4","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193400","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":"Efficient secure and verifiable KNN set similarity search over outsourced clouds","authors":"Xufeng Jiang ,&nbsp;Lu Li","doi":"10.1016/j.hcc.2022.100100","DOIUrl":"https://doi.org/10.1016/j.hcc.2022.100100","url":null,"abstract":"<div><p>KNN set similarity search is a foundational operation in various realistic applications in cloud computing. However, for security consideration, sensitive data will always be encrypted before uploading to the cloud servers, which makes the search processing a challenging task. In this paper, we focus on the problem of KNN set similarity search over the encrypted datasets. We use Yao’s garbled circuits and secret sharing as underlying tools. To achieve better querying efficiency, we construct a secure R-Tree index structure based on a novel secure grouping protocol, which enables grouping appropriate private values in an oblivious way. Along with several elaborately designed secure arithmetic subroutines, we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds. Theoretically, we analyze the complexity of our schemes in detail, and prove the security in the presence of semi-honest adversaries. Finally, we evaluate the performance and feasibility of our proposed methods by extensive experiments.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 1","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50178427","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":"Optimization of multi-state generation problem based on spatial information network topology","authors":"Peng Yang ,&nbsp;JiaYing Zhang ,&nbsp;Shijie Zhou ,&nbsp;Jinyu Zhou","doi":"10.1016/j.hcc.2022.100102","DOIUrl":"https://doi.org/10.1016/j.hcc.2022.100102","url":null,"abstract":"<div><p>Spatial information network is a kind of satellite network with high speed node movement and fast dynamic topology change. With the increasing number of low-orbit satellites, the research on the subnets topology and dynamic optimization of space information networks has become an important direction to study the destructibility of spatial information network. In this paper, two common objective functions in inter-satellite link assignment, network observation position and network communication factor are studied, and a multi-objective optimization model is constructed. Depth first search, simulated annealing, NSGA-II and adaptive optimization simulated annealing were used to analyze and solve the model. By comparing the solving efficiency of the model through simulation experiments, the difference of the results caused by the four algorithms is verified.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 1","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50178429","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}