High-Confidence Computing最新文献_第5页

Aquilo: Temperature-aware scheduler for millimeter-wave devices and networks Aquilo：毫米波设备和网络的温度感知调度器

IF 3.2

High-Confidence Computing Pub Date : 2024-03-27 DOI: 10.1016/j.hcc.2024.100223

{"title":"Aquilo: Temperature-aware scheduler for millimeter-wave devices and networks","authors":"","doi":"10.1016/j.hcc.2024.100223","DOIUrl":"10.1016/j.hcc.2024.100223","url":null,"abstract":"<div><div>Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency connectivity. But the devices need to operate at very high frequency and ultra-wide bandwidth: They consume more energy, dissipate more power, and subsequently heat up faster. Device overheating is a common concern of many users, and millimeter-wave would exacerbate the problem. In this work, we first thermally characterize millimeter-wave devices. Our measurements reveal that after only 10 s of data transfer at 1.9 Gbps bit-rate, the millimeter-wave antenna temperature reaches 68°C; it reduces the link throughput by 21%, increases the standard deviation of throughput by 6<span><math><mo>×</mo></math></span>, and takes 130 s to dissipate the heat completely. Besides degrading the user experience, exposure to high device temperature also creates discomfort. Based on the measurement insights, we propose <em>Aquilo</em>, a temperature-aware, multi-antenna network scheduler. It maintains relatively high throughput performance but cools down the devices substantially. Our testbed experiments under both static and mobile conditions demonstrate that <em>Aquilo</em> achieves a median peak temperature only 0.5°C to 2°C above the optimal while sacrificing less than 10% of throughput.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100223"},"PeriodicalIF":3.2,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404908","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}

引用次数: 0

Device authentication for 5G terminals via Radio Frequency fingerprints 通过射频指纹对 5G 终端进行设备验证

IF 3.2

High-Confidence Computing Pub Date : 2024-03-26 DOI: 10.1016/j.hcc.2024.100222

{"title":"Device authentication for 5G terminals via Radio Frequency fingerprints","authors":"","doi":"10.1016/j.hcc.2024.100222","DOIUrl":"10.1016/j.hcc.2024.100222","url":null,"abstract":"<div><div>The development of wireless communication network technology has provided people with diversified and convenient services. However, with the expansion of network scale and the increase in the number of devices, malicious attacks on wireless communication are becoming increasingly prevalent, causing significant losses. Currently, wireless communication systems authenticate identities through certain data identifiers. However, this software-based data information can be forged or replicated. This article proposes the authentication of device identity using the hardware fingerprint of the terminal’s Radio Frequency (RF) components, which possesses properties of being genuine, unique, and stable, holding significant implications for wireless communication security. Through the collection and processing of raw data, extraction of various features including time-domain and frequency-domain features, and utilizing machine learning algorithms for training and constructing a legal fingerprint database, it is possible to achieve close to a 97% recognition accuracy for Fifth Generation (5G) terminals of the same model. This provides an additional and robust hardware-based security layer for 5G communication security, enhancing monitoring capability and reliability.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100222"},"PeriodicalIF":3.2,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404945","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}

引用次数: 0

Graph isomorphism—Characterization and efficient algorithms 图同构--特征和高效算法

IF 3.2

High-Confidence Computing Pub Date : 2024-03-26 DOI: 10.1016/j.hcc.2024.100224

引用次数: 0

A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in Next-Generation networks (5G/6G/beyond) 以下一代网络（5G/6G/beyond）频谱共享为重点的物联网隐私和安全挑战综合研究

High-Confidence Computing Pub Date : 2024-03-12 DOI: 10.1016/j.hcc.2024.100220

Lakshmi Priya Rachakonda , Madhuri Siddula , Vanlin Sathya

{"title":"A comprehensive study on IoT privacy and security challenges with focus on spectrum sharing in Next-Generation networks (5G/6G/beyond)","authors":"Lakshmi Priya Rachakonda , Madhuri Siddula , Vanlin Sathya","doi":"10.1016/j.hcc.2024.100220","DOIUrl":"10.1016/j.hcc.2024.100220","url":null,"abstract":"<div><p>The emergence of the Internet of Things (IoT) has triggered a massive digital transformation across numerous sectors. This transformation requires efficient wireless communication and connectivity, which depend on the optimal utilization of the available spectrum resource. Given the limited availability of spectrum resources, spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity, so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications, including 5G and beyond. This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum, with a primary focus on spectrum-sharing solutions, including shared spectrum. It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum. This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications. Lastly, it identifies future IoT obstacles and suggests potential research directions to address them.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 2","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295224000230/pdfft?md5=bdf9742a3a93b2c58a0a21f62393cc7c&pid=1-s2.0-S2667295224000230-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140276247","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}

引用次数: 0

Adversarial robustness analysis of LiDAR-included models in autonomous driving 自动驾驶中包含激光雷达模型的对抗鲁棒性分析

High-Confidence Computing Pub Date : 2024-03-01 DOI: 10.1016/j.hcc.2024.100203

Bo Yang , Zizhi Jin , Yushi Cheng , Xiaoyu Ji , Wenyuan Xu

{"title":"Adversarial robustness analysis of LiDAR-included models in autonomous driving","authors":"Bo Yang , Zizhi Jin , Yushi Cheng , Xiaoyu Ji , Wenyuan Xu","doi":"10.1016/j.hcc.2024.100203","DOIUrl":"10.1016/j.hcc.2024.100203","url":null,"abstract":"<div><p>In autonomous driving systems, perception is pivotal, relying chiefly on sensors like LiDAR and cameras for environmental awareness. LiDAR, celebrated for its detailed depth perception, is being increasingly integrated into autonomous vehicles. In this article, we analyze the robustness of four LiDAR-included models against adversarial points under physical constraints. We first introduce an attack technique that, by simply adding a limited number of physically constrained adversarial points above a vehicle, can make the vehicle undetectable by the LiDAR-included models. Experiments reveal that adversarial points adversely affect the detection capabilities of both LiDAR-only and LiDAR–camera fusion models, with a tendency for more adversarial points to escalate attack success rates. Notably, voxel-based models are more susceptible to deception by these adversarial points. We also investigated the impact of the distance and angle of the added adversarial points on the attack success rate. Typically, the farther the victim object to be hidden and the closer to the front of the LiDAR, the higher the attack success rate. Additionally, we have experimentally proven that our generated adversarial points possess good cross-model adversarial transferability and validated the effectiveness of our proposed optimization method through ablation studies. Furthermore, we propose a new plug-and-play, model-agnostic defense method based on the concept of point smoothness. The ROC curve of this defense method shows an AUC value of approximately 0.909, demonstrating its effectiveness.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295224000060/pdfft?md5=7e68638f7a7e1d0186a514efa45060f4&pid=1-s2.0-S2667295224000060-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139539738","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}

引用次数: 0

Privacy-preserving human activity sensing: A survey 保护隐私的人类活动传感：调查

High-Confidence Computing Pub Date : 2024-03-01 DOI: 10.1016/j.hcc.2024.100204

Yanni Yang , Pengfei Hu , Jiaxing Shen , Haiming Cheng , Zhenlin An , Xiulong Liu

引用次数: 0

Cooperative multi-agent game based on reinforcement learning 基于强化学习的多代理合作游戏

High-Confidence Computing Pub Date : 2024-03-01 DOI: 10.1016/j.hcc.2024.100205

Hongbo Liu

{"title":"Cooperative multi-agent game based on reinforcement learning","authors":"Hongbo Liu","doi":"10.1016/j.hcc.2024.100205","DOIUrl":"10.1016/j.hcc.2024.100205","url":null,"abstract":"<div><p>Multi-agent reinforcement learning holds tremendous potential for revolutionizing intelligent systems across diverse domains. However, it is also concomitant with a set of formidable challenges, which include the effective allocation of credit values to each agent, real-time collaboration among heterogeneous agents, and an appropriate reward function to guide agent behavior. To handle these issues, we propose an innovative solution named the Graph Attention Counterfactual Multiagent Actor–Critic algorithm (GACMAC). This algorithm encompasses several key components: First, it employs a multi-agent actor–critic framework along with counterfactual baselines to assess the individual actions of each agent. Second, it integrates a graph attention network to enhance real-time collaboration among agents, enabling heterogeneous agents to effectively share information during handling tasks. Third, it incorporates prior human knowledge through a potential-based reward shaping method, thereby elevating the convergence speed and stability of the algorithm. We tested our algorithm on the StarCraft Multi-Agent Challenge (SMAC) platform, which is a recognized platform for testing multi-agent algorithms, and our algorithm achieved a win rate of over 95% on the platform, comparable to the current state-of-the-art multi-agent controllers.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295224000084/pdfft?md5=0bf06b4b71bd2935634b00877ef59fba&pid=1-s2.0-S2667295224000084-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139539639","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}

引用次数: 0

A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly 大型语言模型 (LLM) 安全与隐私调查：好、坏、丑

High-Confidence Computing Pub Date : 2024-03-01 DOI: 10.1016/j.hcc.2024.100211

Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, Yue Zhang

{"title":"A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly","authors":"Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, Yue Zhang","doi":"10.1016/j.hcc.2024.100211","DOIUrl":"https://doi.org/10.1016/j.hcc.2024.100211","url":null,"abstract":"<div><p>Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes the papers into “The Good” (beneficial LLM applications), “The Bad” (offensive applications), and “The Ugly” (vulnerabilities of LLMs and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code security (code vulnerability detection) and data privacy (data confidentiality protection), outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, Research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs’ potential to both bolster and jeopardize cybersecurity.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 2","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266729522400014X/pdfft?md5=1984f6886539e5ada13eeb8c49a9ef8b&pid=1-s2.0-S266729522400014X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140543306","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}

引用次数: 0

A survey for light field super-resolution 光场超分辨率调查

High-Confidence Computing Pub Date : 2024-03-01 DOI: 10.1016/j.hcc.2024.100206

Mingyuan Zhao , Hao Sheng , Da Yang , Sizhe Wang , Ruixuan Cong , Zhenglong Cui , Rongshan Chen , Tun Wang , Shuai Wang , Yang Huang , Jiahao Shen

{"title":"A survey for light field super-resolution","authors":"Mingyuan Zhao , Hao Sheng , Da Yang , Sizhe Wang , Ruixuan Cong , Zhenglong Cui , Rongshan Chen , Tun Wang , Shuai Wang , Yang Huang , Jiahao Shen","doi":"10.1016/j.hcc.2024.100206","DOIUrl":"10.1016/j.hcc.2024.100206","url":null,"abstract":"<div><p>Compared to 2D imaging data, the 4D light field (LF) data retains richer scene’s structure information, which can significantly improve the computer’s perception capability, including depth estimation, semantic segmentation, and LF rendering. However, there is a contradiction between spatial and angular resolution during the LF image acquisition period. To overcome the above problem, researchers have gradually focused on the light field super-resolution (LFSR). In the traditional solutions, researchers achieved the LFSR based on various optimization frameworks, such as Bayesian and Gaussian models. Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities. In this paper, the present approach can mainly divided into conventional methods and deep learning-based methods. We discuss these two branches in light field spatial super-resolution (LFSSR), light field angular super-resolution (LFASR), and light field spatial and angular super-resolution (LFSASR), respectively. Subsequently, this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets. Finally, we discuss the potential innovations of the LFSR to propose the progress of our research field.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295224000096/pdfft?md5=71deba58809585186ae13284da5a82d9&pid=1-s2.0-S2667295224000096-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139631869","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}

引用次数: 0

Data cube-based storage optimization for resource-constrained edge computing 基于数据立方体的存储优化，适用于资源受限的边缘计算

IF 3.2

High-Confidence Computing Pub Date : 2024-02-28 DOI: 10.1016/j.hcc.2024.100212

引用次数: 0