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

共 4934 条记录，以下是151-160 订阅

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Discovery of Rare yet Co-occurring Actions with Temporal Characteristics in Episodic Cyberattack Streams 31

Discovery of Rare yet Co-occurring Actions with Temporal Cha...

引用

31st International Conference on computer communications and networks (ICCCN)

作者： Werner, Gordon Yang, Shanchieh RIT Coll Comp & Informat Sci Rochester NY 14623 USA RIT Dept Comp Engn Rochester NY USA

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

The large number of streaming intrusion alerts make it challenging for security analysts to quickly identify attack patterns. This is especially difficult since critical alerts often occur rarely for traditional pattern mining algorithms to be effective. Recognizing the attack speed as an inherent indicator of differing cyber attacks, this work aggregates alerts into attack episodes that have distinct attack speeds, and finds attack actions regularly co-occurring within the same episode. This enables a novel use of SPADE - a temporal pattern mining algorithm - to extract consistent co-occurrences of alert signatures that are indicative of attack actions that follow each other. The proposed system, R-CAD, extracts not only the co-occurring patterns but also the temporal characteristics of the co-occurrences, giving the 'strong rules' indicative of critical and repeated attack behaviors. Through the use of a real-world dataset, we demonstrate that R-CAD helps reduce from the overwhelming volume and variety of intrusion alerts to a manageable set of co-occurring strong rules. We show specific rules that reveal how critical attack actions follow one another and in what attack speed.

关键词： Intrusion Alert Correlation Cyberattack Behavior Extraction Pattern Mining

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Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone networks 31

Energy-Aware Multi-Agent Reinforcement Learning for Collabor...

引用

31st International Conference on computer communications and networks (ICCCN)

作者： Li, Ying Li, Changling Chen, Jiyao Roinou, Christine Colby Coll Dept Comp Sci Waterville ME 04901 USA Columbia Univ Dept Comp Sci New York NY 10027 USA

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Mission-oriented drone networks have been widely used for structural inspection, disaster monitoring, border surveillance, etc. Due to the limited battery capacity of drones, mission execution strategy impacts network performance and mission completion. However, collaborative execution is a challenging problem for drones in such a dynamic environment as it also involves efficient trajectory design. We leverage multi-agent reinforcement learning (MARL) to manage the challenge in this study, letting each drone learn to collaboratively execute tasks and plan trajectories based on its current status and environment. Simulation results show that the proposed collaborative execution model can successfully complete the mission at least 80% of the time, regardless of task locations and lengths, and can even achieve a 100% success rate when the task density is not way too sparse. To the best of our knowledge, our work is one of the pioneer studies on leveraging MARL on collaborative execution for mission-oriented drone networks;the unique value of this work lies in drone battery level driving our model design.

关键词： mission-oriented drone networks collaborative execution multi-agent reinforcement learning deep Q-network

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Scalable Resource Allocation Techniques for Edge Computing Systems 31

Scalable Resource Allocation Techniques for Edge Computing S...

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31st International Conference on computer communications and networks (ICCCN)

作者： Rublein, Caroline Mehmeti, Fidan Gunes, Taha D. Stein, Sebastian La Porta, Thomas F. Penn State Univ University Pk PA 16802 USA Tech Univ Munich Munich Germany Univ Southampton Southampton Hants England

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. This provides robustness and does not require service providers to share information about their configurations or workloads. We utilize a two-round bidding approach of assigning tasks to edge cloud servers. We consider a preemption-enabled system in which servers may stop a previous task in order to run a more useful one. We evaluate the performance of our system using realistic simulations and real-world trace data from a high-performance computing cluster. Results show that our approach is reasonably close to optimal assignment, while saving 50-70% of the original computation time.

关键词： Edge cloud computing Optimization Bidding Clustering

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Security and Threats of Intelligent Reflecting Surface Assisted Wireless communications 31

Security and Threats of Intelligent Reflecting Surface Assis...

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31st International Conference on computer communications and networks (ICCCN)

作者： Tang, Haolin Sarp, Salih Zhao, Yanxiao Wang, Wei Xin, Chunsheng Virginia Commonwealth Univ Dept Elect & Comp Engn Richmond VA 23284 USA San Diego State Univ Dept Comp Sci San Diego CA USA Old Dominion Univ Dept Elect & Comp Engn Norfolk VA USA

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Intelligent Reflecting Surface (IRS) has been demonstrated as a promising and innovative technology for next-generation wireless communications. It can be utilized to flexibly re-configure the fundamental communication environment to realize low-cost, energy-saving, and low-interference wireless communications. On the other hand, malicious users may also utilize the powerful capability of the IRS to re-configure the communication environment to achieve an advantageous position to launch security attacks such as eavesdropping and jamming wireless networks. Therefore, while the integration of IRS into wireless communications brings promising new opportunities, it also raises significant concerns from the security perspective. This issue has not been thoroughly studied in the literature. In this paper, we first introduce the recent works of using IRS in wireless communications by grouping them into two categories: 1) securing communication via IRS and 2) launching attacks using IRS. We then derive a critical performance metric, the Signal-to-Noise Ratio (SNR), for evaluating IRS-assisted wireless communication systems. Next, we present four typical scenarios of utilizing IRS for security or threats to wireless communications. At last, we evaluate the IRS-assisted system with regard to the SNR performance affected by the IRS in those four scenarios toward a deeper understanding of the potential of IRS-assisted wireless communication systems in terms of security and threats.

关键词： Intelligent reflecting surfaces IRS security 5G next-generation wireless networks

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Optimization of Wireless Power Transfer for Wireless-Powered Mobile Edge Computing 31

Optimization of Wireless Power Transfer for Wireless-Powered...

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31st International Conference on computer communications and networks (ICCCN)

作者： Dong, Xiaogang Wanl, Zheng Deng, Changshou Jiangxi Univ Finance & Econ Sch Informat Management Nanchang Jiangxi Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang Peoples R China

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Wireless-powered mobile edge computing (WPMEC) is a new network computing paradigm, which integrates with the advantages of wireless power transfer and mobile edge computing. In WP-MEC, the time of wireless power transfer is a key factor affecting the performance of network system when the harvest-then-offload protocol is employed. If the time of wireless power transfer is too short, the user cannot harvest enough energy;If it is too long, the task uploading time left for the user is insufficient. Both result in numerous user tasks being discarded. To tackle this problem, an optimization algorithm for the time of wireless power transfer based on differential evolution is proposed. The hybrid mutation operator and perturbation based binomial crossover operator are designed for this algorithm. These two improvements effectively enhance the optimization performance of DE, conducive to find the optimal time of wireless power transfer. Moreover, the micro-population is introduced into this algorithm in order to improve the optimization efficiency. Finally, the performance of this algorithm is verified through the existing computation completion ratio maximization model under the WP-MEC scenario with multiple edge servers. Numerical results show that the computation offloading scheme incorporating this algorithm can achieve a higher computation completion rate of user tasks than the benchmark schemes. This proves that the proposed algorithm can find a better wireless power transfer time than the benchmark schemes and is an effective wireless power transfer time optimization scheme.

关键词： Wireless power transfer Mobile edge computing Task offloading Differential evolution

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Context-aware task offloading with QoS-provisioning for MEC multi-RAT vehicular networks 31

Context-aware task offloading with QoS-provisioning for MEC ...

引用

31st International Conference on computer communications and networks (ICCCN)

作者： Brehon-Grataloup, Lucas Kacimi, Rahim Beylot, Andre-Luc Univ Toulouse IRIT CNRS Toulouse INPUT3 Toulouse France

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

The next step towards vehicular networks in smart cities would be the deployment of autonomous shuttles with multiple on-board applications. Their need to offload task towards Road Side Units (RSUs) is inevitable, especially with a certain proportion of urgent data needing to be processed in the shortest possible delay. Therefore, QoS-provisioning appears as imperative, along with the optimization of resource requesting at RSUs. To this end, we propose CAVTOMEC, a multi-RAT location-aware, context-aware task offloading solution with QoS provisioning for MEC vehicular networks. Our solution consists of three intertwined mechanisms: traffic classification, location-awareness exploiting the contents of CAM beacons and V2N-enhanced resource polling. Traffic classification identifies high, low, and indifferent task priorities, while location and resource awareness help to select the most appropriate RSU to offload tasks to depending on these priorities. Performance evaluations show that our proposal offers better load balancing at the RSUs than traditional offloading schemes, thus satisfying high priority task offloading at better rates and freeing up more resources in case an unexpected event occurs.

关键词： MEC Task offloading C-V2X Connected autonomous vehicles V2I QoS-provisioning OMNET plus plus and Veins simulation

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ENGAGE: Session Guarantees for the Edge 31

ENGAGE: Session Guarantees for the Edge

引用

31st International Conference on computer communications and networks (ICCCN)

作者： Belem, Miguel Fouto, Pedro Lykhenko, Taras Leitao, Joao Preguica, Nuno Rodrigues, Luis Univ Lisbon INESC ID Inst Super Tecn Lisbon Portugal Univ Nova Lisboa NOVA LINCS Fac Ciencias & Tecnol Lisbon Portugal

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Edge computing offers support for latency-constrained applications, by replicating data in the edge. Edge storage systems need to adopt both partial replication, as only data of interest needs to be replicated, and weak consistency models, to avoid the overhead and latency induced by the coordination mechanisms of strong consistency models. In this context, session guarantees are a powerful tool that can be used to simplify the design of edge applications. This paper presents ENGAGE, a storage system that offers efficient support for session guarantees in a partially replicated edge setting. To achieve this, ENGAGE combines the use of vector clocks and distributed metadata propagation services with a payload propagation scheme tailored for the edge. We have implemented ENGAGE and evaluated its performance experimentally. The results show that, when compared with previous proposals, the combination of techniques employed by ENGAGE reduce both the number of false dependencies, that can slow down the system, and the signaling overhead, while improving the freshness of data exposed to clients.

关键词： Computational modeling Semantics computer architecture Metadata Throughput Proposals Low latency communication

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mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave 31

mmFit: Low-Effort Personalized Fitness Monitoring Using Mill...

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31st International Conference on computer communications and networks (ICCCN)

作者： Xie, Yucheng Jiang, Ruizhe Guo, Xiaonan Wang, Yan Cheng, Jerry Chen, Yingying Indiana Univ Purdue Univ Indianapolis IN 46202 USA Temple Univ Philadelphia PA 19122 USA New York Inst Technol New York NY 10023 USA Rutgers State Univ New Brunswick NJ 08901 USA

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

There is a growing trend for people to perform workouts at home due to the global pandemic of COVID-19 and the stay-at-home policy of many countries. Since a self-designed fitness plan often lacks professional guidance to achieve ideal outcomes, it is important to have an in-home fitness monitoring system that can track the exercise process of users. Traditional camera-based fitness monitoring may raise serious privacy concerns, while sensor-based methods require users to wear dedicated devices. Recently, researchers propose to utilize RF signals to enable non-intrusive fitness monitoring, but these approaches all require huge training efforts from users to achieve a satisfactory performance, especially when the system is used by multiple users (e.g., family members). In this work, we design and implement a fitness monitoring system using a single COTS mmWave device. The proposed system integrates workout recognition, user identification, multi-user monitoring, and training effort reduction modules and makes them work together in a single system. In particular, we develop a domain adaptation framework to reduce the amount of training data collected from different domains via mitigating impacts caused by domain characteristics embedded in mmWave signals. We also develop a GAN-assisted method to achieve better user identification and workout recognition when only limited training data from the same domain is available. We propose a unique spatial-temporal heatmap feature to achieve personalized workout recognition and develop a clustering-based method for concurrent workout monitoring. Extensive experiments with 14 typical workouts involving 11 participants demonstrate that our system can achieve 97% average workout recognition accuracy and 91% user identification accuracy.

关键词： mmWave sensing fitness monitoring generative adversarial network domain adaptation training

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Worker Selection for On-Demand Crowdsourcing 31

Worker Selection for On-Demand Crowdsourcing

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31st International Conference on computer communications and networks (ICCCN)

作者： Tan, Tianxiang Wu, Yibo Liu, Zida Cao, Guohong Penn State Univ Dept Comp Sci & Engn State Coll PA 16801 USA

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

The ubiquity of mobile devices allows mobile users to participate in crowdsourcing anywhere, anytime. One potential application is to crowdsource photos/videos on demand to search for interested targets. Crowdsourced photos/videos have much better coverage compared to surveillance cameras, and thus help improve the effectiveness of target search. However, broadcasting the crowdsourcing task to all mobile users can significantly increase the cost in terms of resource and incentive budget. To reduce cost, the crowdsourcing server selects a subset of participating workers, and there are many challenges on worker selection. For example, due to occlusions in the photo/video scene, each worker only covers part of the area with certain probability. Due to the non-deterministic nature of this problem, we study two kinds of optimization problems: max-coverage which maximizes the probability of finding the target given a cost, and min-selection which minimizes the number of workers given the required probability of finding the target. Considering that workers may report exact locations or coarse-grained locations, we formalize four probability-based optimization problems for worker selection, and develop optimal or efficient approximation algorithms to solve them. The effectiveness of the proposed algorithms is evaluated and validated via extensive trace-driven simulations and a real-world demo.

关键词： Crowdsourcing Costs Surveillance Approximation algorithms Cameras Real-time systems Mobile handsets

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Efficient Indoor Localization with Multiple Consecutive Geomagnetic Sequences 31

Efficient Indoor Localization with Multiple Consecutive Geom...

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31st International Conference on computer communications and networks (ICCCN)

作者： Zhuang, Hui He, Tao Niu, Qun Liu, Ning Sun Yat Sen Univ Guangzhou Guangdong Peoples R China

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Geomagnetism-based indoor localization has great social and commercial value due to its pervasiveness and independence from extra infrastructure. To improve the distinguishability of geomagnetic signals as location clues, geomagnetic sequences are usually taken as input. Although longer input sequence can provide higher localization accuracy, it suffers from high response time in practice. To address the above, we first utilize short geomagnetic sequences as input, alleviating high response time, and propose an efficient single position estimation model, taking advantage of modified transformer to estimate position for each independent short sequence. Noticing the temporal dependency and the spatial consistency constraint during continuous positioning, we further propose a joint position estimation model to capture the correlations among consecutive short sequences, achieving higher accuracy with multiple short sequences. We have conducted extensive experiments in two typical trial sites, a narrow office area and a spacious parking lot. Experimental results show that the proposed approach outperforms state-of-the-art competing schemes, and the localization error is reduced by more than 32% with shorter geomagnetic sequences.

关键词： Location awareness Correlation Estimation Transformers Time factors

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