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检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是71-80 订阅

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Securing Federated Learning: Enhancing Defense Mechanisms against Poisoning Attacks 33

Securing Federated Learning: Enhancing Defense Mechanisms ag...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Birdman, Benjamin Thamilarasu, Geethapriya Univ Washington Bothell Bothell WA 98011 USA

ISBN: (纸本)9798350348439;9798350384611

In recent years, Federated Learning (FL) has emerged as a powerful paradigm, revolutionizing machine learning by harnessing distributed computational resources. FL enables collaborative model training across multiple devices or servers without centralizing data, thereby enhancing model inference quality. However, these distributed systems are not immune to security threats, particularly poisoning attacks leading to compromised model performance and potential privacy breaches. In this paper, we develop defense strategies to mitigate poisoning attacks in federated learning systems. Specifically, we address the challenge posed by distributed backdoor poisoning attacks against existing poisoning defenses in federated learning systems. We propose a defense solution, known as Area Similarity FoolsGold (ASF), that builds upon the existing cosine similarity solution by integrating Triangle Area Similarity and Sector Area Similarity algorithms. Our solution aims to enhance the identification of maliciously compromised clients and mitigate the impact of distributed backdoor attacks in federated learning systems.

关键词： Federated learning

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Enabling Reliable Environmental Sensing with LoRa, Energy Harvesting, and Domain Adaptation 33

Enabling Reliable Environmental Sensing with LoRa, Energy Ha...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Ma, Aitian Rodriguez, Jean C. Tonday Sha, Mo Florida Int Univ Knight Fdn Sch Comp & Informat Sci 11200 SW 8th St Miami FL 33199 USA

ISBN: (纸本)9798350348439;9798350384611

Environmental sensing is essential for many applications. Many existing efforts rely on the readings provided by the weather stations maintained by federal, regional, or local government agencies. While the accuracy of the readings provided by those weather stations is high, the ability of such data to reflect the temperature variability experienced by urban populations is generally low. Therefore, recent studies have proposed to deploy new infrastructures with low-power communication and energy harvesting capabilities to provide fine-scale measurements. Recently, there has been an increasing interest in deploying environmental sensing systems with LoRa radios and solar panels. However, there have been very few studies looking into the reliability of solar power in the LoRa-based environmental sensing settings. In this paper, we present an empirical study that investigates how well solar energy powers an environmental sensing platform. Our study shows that solar energy generation forecasting plays an important role in the performance of the sensing platform. To address the challenges, we develop a novel solution that leverages LoRa, energy harvesting, and domain adaptation to enable reliable environmental sensing. Experimental results show that our solution outperforms the baselines and effectively supports end devices to perform environmental sensing operations without interruptions.

关键词： Environmental sensing LoRa energy-harvesting domain adaptation

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Detecting IP Prefix Mismatches on SDN Data Plane 33

Detecting IP Prefix Mismatches on SDN Data Plane

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Tung, Shu-Po Lin, Yu-Min Chang, Keng-Lun Hsiao, Hsu-Chun Kim, Tiffany Hyun-Jin Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan HRL Labs Malibu CA USA

ISBN: (纸本)9798350348439;9798350384611

Software-defined networking (SDN) enables centralized network management by separating the control and data plane. However, the actual packet behavior on data-plane may deviate from the control-plane rules sometimes. Many probe-based tools have been developed to verify the data plane's correctness and detect forwarding errors by sending test packets. However, they all assume simple fault models, such as incorrect action fields in the forwarding rules. To address this gap, this paper identifies a new class of error affecting the IP match field called IP prefix mismatch, which previous tools failed to identify thoroughly. We categorize IP prefix mismatches into prefix shrinkage and prefix expansion. We then present novel test packet generation algorithms to construct test packets designed to exhibit different behaviors depending on the presence of these errors. Using these algorithms, we develop a system that guarantees the discovery of at least one prefix mismatch in every detection round, even in the worst-case scenario. Several experiments were conducted to compare our system to a conventional probe-based method that sends a single test packet per rule. The results demonstrate that our system achieves perfect fault coverage within a minimal number of detection rounds. Even when faced with a network containing 50% erroneous rules, it successfully identifies all prefix mismatches within an average of only two detection rounds. In contrast, the conventional method fails to uncover all errors, even after spending on additional detection rounds.

关键词： Software-defined network Data plane security Test packet generation Match field error IP prefix

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An Edge Resource Selection Scheme Considering QoS and Computational Parameters 33

An Edge Resource Selection Scheme Considering QoS and Comput...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Frei, Matthias German, Reinhard Djanatliev, Anatoli Fraunhofer Inst Integrated Circuits IIS Erlangen Germany Univ Erlangen Nurnberg Dept Comp Sci Comp Networks & Commun Syst Erlangen Germany

ISBN: (纸本)9798350348439;9798350384611

Edge computing enhances cloud architectures through the integration of distributed platforms that operate closer to end-users. With dynamically changing network topologies, user mobility, multiple access networks, demand-driven service orchestration, and consequently several distributed service instances that are available, methods for selecting edge resources must adapt to these conditions. This consideration is particularly relevant for edge computing, where target applications often have strict Quality of Service (QoS) constraints. This work proposes an edge resource selection scheme that considers several QoS parameters, computing load, and service usage cost. These metrics are employed to derive a selection score through four distinct algorithms. The overall performance of each algorithm is evaluated by scoring examples, a sensitivity analysis, and in a system-level simulation of a fifth generation (5G) cellular network with user mobility and multiple Multi-Access Edge Computing (MEC) platforms. The results show that all approaches perform well under unambiguous QoS and load conditions, i.e., very good or very poor, but some lose accuracy under moderate, but still acceptable, conditions. For the goal of cost-optimized selection while meeting QoS constraints, one novel method presented is significantly superior to the others. Used in simulation, it generates 12% more computing load on cheaper platforms than its closest competitor, and maintains significantly lower loads on high-cost platforms.

关键词： edge cloud MEC resources selection scoring QoS latency performance load cost 5G simulation

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Privacy-Preserving Edge Assistance for Solving Matrix Eigenvalue Problem 33

Privacy-Preserving Edge Assistance for Solving Matrix Eigenv...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Kong, Fanyu Yu, Leyun Qingdao Univ Coll Comp Sci & Technol Qingdao Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China JIC IOT CO LTD Nanchang Peoples R China

ISBN: (纸本)9798350348439;9798350384611

The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted computation scheme for solving the largest eigenvalue and corresponding eigenvector. We propose a privacy-preserving transformation method to protect data privacy and prevent edge servers from retrieving sensitive information. Meanwhile, we design a verification scheme to ensure the correctness of the results returned by the edge servers. In addition, we design a distributed parallel computing scheme to ensure the efficiency of edge computation. Through theoretical analysis and simulation experiments, we verify the feasibility and efficiency of our proposed scheme.

关键词： Eigenvalue problem edge computing privacypreserving parallel computing

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On the Potential of an Independent Avatar to Augment Metaverse Social networks 33

On the Potential of an Independent Avatar to Augment Metaver...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Raptis, Theofanis P. Boldtini, Chiara Conti, Marco Passarella, Andrea CNR Inst Informat & Telemat Rome Italy

ISBN: (纸本)9798350348439;9798350384611

We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user's available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metaverse, and emerging properties herein. We envision a Metaverse-focused extension of the traditional avatar concept: An avatar can be as well programmed to operate independently when its user is not controlling it directly, thus turning it into an agent-based digital human representation. This way, we highlight how such an independent avatar could help its user to better navigate their social relationships and optimize their socializing time in the Metaverse by (partly) offloading some interactions to the avatar. We model the setting and identify the characteristic variables by using selected concepts from social sciences: ego networks, social presence, and social cues. Then, we formulate the problem of maximizing the user's non-avatar-mediated spare time as a linear optimization. Finally, we analyze the feasible region of the problem and we present some initial insights on the spare time that can be achieved for different parameter values of the avatar-mediated interactions.

关键词： Agent Avatar Ego network Metaverse Social cues Social presence

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DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery networks 33

DeePref: Deep Reinforcement Learning For Video Prefetching I...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Alkassab, Nawras Huang, Chin-Tser Botran, Tania Lorido Univ South Carolina Columbia SC 29208 USA Roblox San Mateo CA USA

ISBN: (纸本)9798350348439;9798350384611

Content Delivery networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of deep reinforcement learning to tackle the changes in users' access patterns and automatically adapt over time to predict future requests. We propose DEEPREF, an auto-aggressive video prefetcher that is sensitive to different storage capacities at edge networks. DEEPREF is agnostic to hardware design, operating systems, and applications in which it utilizes only the video ID to make prefetching decisions online. DEEPREF outperforms baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. Our results show that DEEPREF, using a real-world dataset, achieves 17% increase in terms of prefetching accuracy and 28% increase in prefetching coverage. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. Our results indicate that DEEPREF effectively adapts to distribution shifts where the increase in prefetching accuracy and prefetching coverage are [30%, 10%], respectively.

关键词： Deep reinforcement learning

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Towards Protecting On-Device Machine Learning with RISC-V based Multi-Enclave TEE 33

Towards Protecting On-Device Machine Learning with RISC-V ba...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Wang, Yongzhi Boggaram, Venkata Sai Ramya Padmasri Texas A&M Univ Corpus Christi Dept Comp Sci Corpus Christi TX 78412 USA

ISBN: (纸本)9798350348439;9798350384611

On-device machine learning is a trending paradigm that empowers the artificial intelligence of various smart devices, including IoT, mobile, and robotics, etc. On the other hand, this emerging paradigm has brought new security challenges that traditional system security techniques cannot harness. In this paper, we explored the possibility of using multi-enclave Trusted Execution Environments to address these security challenges. We first identified the challenges and threats that on-device machine learning systems are facing. Then, we presented our experimental results of using RISC-V-based multi-enclave TEE to secure on-device machine learning system, demonstrating a promising performance advantage. Finally, we discussed the technical directions for the threats that cannot be completely addressed by the TEE.

关键词： On-Device Machine Learning Trusted Execution Environments RISC-V Machine Learning Security IoT

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ECG-grained Cardiac Monitoring Using RFID 33

ECG-grained Cardiac Monitoring Using RFID

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Wang, Ningning Zhao, Tianya Mao, Shiwen Bai, Harrison X. Jiao, Zhicheng Wang, Xuyu Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Johns Hopkins Univ Dept Radiol & Radiol Sci Sch Med Baltimore MD 21287 USA Rhode Isl Hosp Dept Diagnost Imaging Providence RI 02903 USA

ISBN: (纸本)9798350348439;9798350384611

Heartbeat signals are useful to disease prediction, sub-health diagnosis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy, and long-term heartbeat monitoring. This paper presents a contactless Radio Frequency Identification (RFID) based system for heartbeat monitoring that leverages the insight that RFID signal fluctuations induced by chest motion are synchronous with both respiration and heartbeat. The proposed system collects the temporal phase information from the tag pair on the body to extract heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on empirical mode decomposition (EMD) to obtain heart rate after preprocessing. Furthermore, the estimated signal is input to an enhanced variational autoencoder (VAE) model to recover the heartbeat waveform. Implemented with commercial off-the-shelf (COTS) RFID devices, the system achieves accurate heart rate monitoring with less than 3% relative errors. The detected waveform exhibits a median cosine similarity of 0.83 as compared with the ground truth, which validate the system's wide applicability and high reliability for fine-grained, contactless heartbeat monitoring.

关键词： Radio Frequency Identification (RFID) vital sign monitoring contact-free sensing variational autoencoder

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Neural Network-based Blocking Prediction for Dynamic Network Slicing 33

Neural Network-based Blocking Prediction for Dynamic Network...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Movva, Nitin Datta Ishigaki, Genya San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9798350348439;9798350384611

Network slicing in Optical Transport networks (OTNs) is a promising technology to provide hard resource isolation and performance assurance of services with diverse requirements. When network slices are allowed to request their scaling based on their service traffic trends (i.e., dynamic network slicing), it is difficult for a slice provider to maintain resource isolation and high resource utilization simultaneously. We formulate a blocking prediction problem for the slice provider to assess the possibility of future blocking events for a given set of slice requests. We use a Multi-Layer Perceptron classifier to identify a set of slice requests that would cause blocking. It is demonstrated by the simulations with different request patterns that our proposal outperforms other learning-based approaches. The results indicate that a slice provider could assess the risk of experiencing a violation of service assurance when accepting a set of requests and take an appropriate countermeasure such as admission control, using our prediction module.

关键词： Dynamic network slicing Optical Transport Network (OTN) Request blocking

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