Information (Switzerland)最新文献_第2页

In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms 基于卷积神经网络和多尺度直方图的车载网络入侵检测系统

Information (Switzerland) Pub Date : 2023-11-08 DOI: 10.3390/info14110605

Gianmarco Baldini

{"title":"In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms","authors":"Gianmarco Baldini","doi":"10.3390/info14110605","DOIUrl":"https://doi.org/10.3390/info14110605","url":null,"abstract":"Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135392609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

POSS-CNN: An Automatically Generated Convolutional Neural Network with Precision and Operation Separable Structure Aiming at Target Recognition and Detection POSS-CNN:一种针对目标识别和检测的具有精度和操作可分结构的自动生成卷积神经网络

Information (Switzerland) Pub Date : 2023-11-07 DOI: 10.3390/info14110604

Jia Hou, Jingyu Zhang, Qi Chen, Siwei Xiang, Yishuo Meng, Jianfei Wang, Cimang Lu, Chen Yang

{"title":"POSS-CNN: An Automatically Generated Convolutional Neural Network with Precision and Operation Separable Structure Aiming at Target Recognition and Detection","authors":"Jia Hou, Jingyu Zhang, Qi Chen, Siwei Xiang, Yishuo Meng, Jianfei Wang, Cimang Lu, Chen Yang","doi":"10.3390/info14110604","DOIUrl":"https://doi.org/10.3390/info14110604","url":null,"abstract":"Artificial intelligence is changing and influencing our world. As one of the main algorithms in the field of artificial intelligence, convolutional neural networks (CNNs) have developed rapidly in recent years. Especially after the emergence of NASNet, CNNs have gradually pushed the idea of AutoML to the public’s attention, and large numbers of new structures designed by automatic searches are appearing. These networks are usually based on reinforcement learning and evolutionary learning algorithms. However, sometimes, the blocks of these networks are complex, and there is no small model for simpler tasks. Therefore, this paper proposes POSS-CNN aiming at target recognition and detection, which employs a multi-branch CNN structure with PSNC and a method of automatic parallel selection for super parameters based on a multi-branch CNN structure. Moreover, POSS-CNN can be broken up. By choosing a single branch or the combination of two branches as the “benchmark”, as well as the overall POSS-CNN, we can achieve seven models with different precision and operations. The test accuracy of POSS-CNN for a recognition task tested on a CIFAR10 dataset can reach 86.4%, which is equivalent to AlexNet and VggNet, but the operation and parameters of the whole model in this paper are 45.9% and 45.8% of AlexNet, and 29.5% and 29.4% of VggNet. The mAP of POSS-CNN for a detection task tested on the LSVH dataset is 45.8, inferior to the 62.3 of YOLOv3. However, compared with YOLOv3, the operation and parameters of the model in this paper are reduced by 57.4% and 15.6%, respectively. After being accelerated by WRA, POSS-CNN for a detection task tested on an LSVH dataset can achieve 27 fps, and the energy efficiency is 0.42 J/f, which is 5 times and 96.6 times better than GPU 2080Ti in performance and energy efficiency, respectively.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"174 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Privacy Preservation in Verifiable Computation through Random Permutation Masking to Prevent Leakage 通过随机排列掩蔽增强可验证计算中的隐私保护以防止泄漏

Information (Switzerland) Pub Date : 2023-11-06 DOI: 10.3390/info14110603

Yang Yang, Guanghua Song

{"title":"Enhancing Privacy Preservation in Verifiable Computation through Random Permutation Masking to Prevent Leakage","authors":"Yang Yang, Guanghua Song","doi":"10.3390/info14110603","DOIUrl":"https://doi.org/10.3390/info14110603","url":null,"abstract":"Outsourcing computation has become increasingly popular due to its cost-effectiveness, enabling users with limited resources to conduct large-scale computations on potentially untrusted cloud platforms. In order to safeguard privacy, verifiable computing (VC) has emerged as a secure approach, ensuring that the cloud cannot discern users’ input and output. Random permutation masking (RPM) is a widely adopted technique in VC protocols to provide robust privacy protection. This work presents a precise definition of the privacy-preserving property of RPM by employing indistinguishability experiments. Moreover, an innovative attack exploiting the greatest common divisor and the least common multiple of each row and column in the encrypted matrices is introduced against RPM. Unlike previous density-based attacks, this novel approach offers a significant advantage by allowing the reconstruction of matrix values from the ciphertext based on RPM. A comprehensive demonstration was provided to illustrate the failure of protocols based on RPM in maintaining the privacy-preserving property under this proposed attack. Furthermore, an extensive series of experiments is conducted to thoroughly validate the effectiveness and advantages of the attack against RPM. The findings of this research highlight vulnerabilities in RPM-based VC protocols and underline the pressing need for further enhancements and alternative privacy-preserving mechanisms in outsourcing computation.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"16 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135589137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trend Analysis of Large Language Models through a Developer Community: A Focus on Stack Overflow 基于开发者社区的大型语言模型趋势分析:对堆栈溢出的关注

Information (Switzerland) Pub Date : 2023-11-06 DOI: 10.3390/info14110602

Jungha Son, Boyoung Kim

{"title":"Trend Analysis of Large Language Models through a Developer Community: A Focus on Stack Overflow","authors":"Jungha Son, Boyoung Kim","doi":"10.3390/info14110602","DOIUrl":"https://doi.org/10.3390/info14110602","url":null,"abstract":"In the rapidly advancing field of large language model (LLM) research, platforms like Stack Overflow offer invaluable insights into the developer community’s perceptions, challenges, and interactions. This research aims to analyze LLM research and development trends within the professional community. Through the rigorous analysis of Stack Overflow, employing a comprehensive dataset spanning several years, the study identifies the prevailing technologies and frameworks underlining the dominance of models and platforms such as Transformer and Hugging Face. Furthermore, a thematic exploration using Latent Dirichlet Allocation unravels a spectrum of LLM discussion topics. As a result of the analysis, twenty keywords were derived, and a total of five key dimensions, “OpenAI Ecosystem and Challenges”, “LLM Training with Frameworks”, “APIs, File Handling and App Development”, “Programming Constructs and LLM Integration”, and “Data Processing and LLM Functionalities”, were identified through intertopic distance mapping. This research underscores the notable prevalence of specific Tags and technologies within the LLM discourse, particularly highlighting the influential roles of Transformer models and frameworks like Hugging Face. This dominance not only reflects the preferences and inclinations of the developer community but also illuminates the primary tools and technologies they leverage in the continually evolving field of LLMs.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"16 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135589139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EACH-COA: An Energy-Aware Cluster Head Selection for the Internet of Things Using the Coati Optimization Algorithm 基于Coati优化算法的物联网能量感知簇头选择

Information (Switzerland) Pub Date : 2023-11-05 DOI: 10.3390/info14110601

Ramasubbareddy Somula, Yongyun Cho, Bhabendu Kumar Mohanta

{"title":"EACH-COA: An Energy-Aware Cluster Head Selection for the Internet of Things Using the Coati Optimization Algorithm","authors":"Ramasubbareddy Somula, Yongyun Cho, Bhabendu Kumar Mohanta","doi":"10.3390/info14110601","DOIUrl":"https://doi.org/10.3390/info14110601","url":null,"abstract":"In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8–15% and throughput by 5–10%.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"36 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135725298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Key Issues in Cybersecurity Data Breaches: Analyzing Data Breach Litigation with ML-Based Text Analytics 探讨网络安全数据泄露中的关键问题:用基于ml的文本分析分析数据泄露诉讼

Information (Switzerland) Pub Date : 2023-11-05 DOI: 10.3390/info14110600

Dominik Molitor, Wullianallur Raghupathi, Aditya Saharia, Viju Raghupathi

引用次数: 0

Combining Software-Defined Radio Learning Modules and Neural Networks for Teaching Communication Systems Courses 结合软件无线电学习模块和神经网络进行通信系统课程教学

Information (Switzerland) Pub Date : 2023-11-04 DOI: 10.3390/info14110599

Luis A. Camuñas-Mesa, José M. de la Rosa

引用次数: 0

Deep Learning for Time Series Forecasting: Advances and Open Problems 时间序列预测的深度学习:进展和开放问题

Information (Switzerland) Pub Date : 2023-11-04 DOI: 10.3390/info14110598

Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo, Francesco Camastra

引用次数: 0

Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation 基于位置隐私保护的在线送餐多智能体强化学习

Information (Switzerland) Pub Date : 2023-11-03 DOI: 10.3390/info14110597

Suleiman Abahussein, Dayong Ye, Congcong Zhu, Zishuo Cheng, Umer Siddique, Sheng Shen

{"title":"Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation","authors":"Suleiman Abahussein, Dayong Ye, Congcong Zhu, Zishuo Cheng, Umer Siddique, Sheng Shen","doi":"10.3390/info14110597","DOIUrl":"https://doi.org/10.3390/info14110597","url":null,"abstract":"Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting 基于时间卷积网络和bert的金融预测多标签情绪分析

Information (Switzerland) Pub Date : 2023-11-03 DOI: 10.3390/info14110596

Charalampos M. Liapis, Sotiris Kotsiantis

引用次数: 0