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

共 4934 条记录，以下是81-90 订阅

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Super-resolution Localization and Tracking in WiFi Sensing 33

Super-resolution Localization and Tracking in WiFi Sensing

引用

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

作者： Wang, Jian Chuang, Jack Sempert, Sebastian Golmie, Nada NIST Wireless Networks Div Gaithersburg MD 20899 USA Tech Univ Ilmenau Inst Informat Technol Ilmenau Germany

ISBN: (纸本)9798350348439;9798350384611

Integrated sensing and communication (ISAC) systems have been investigated by the research and standardization communities in the recent past. Accurately localizing the target and tracking the target's movement are critical for numerous smart Internet of Things (IoT) systems (smart manufacturing, smart transportation, etc.). This paper aims to realize super-resolution localization and tracking in WiFi sensing by leveraging the IEEE 802.11ad beamforming training procedure. We leverage the CLEAN-Space-Alternating Generalized Expectation-maximization (CLEAN-SAGE) algorithm on a single beam sweeping cycle for target localization and investigate the targets' delays and angle estimation. For tracking moving targets, we design mechanisms to estimate the target's motion, including the target's velocity and motion pattern, such as estimating the target's spatial positions over time to obtain the Doppler shift or tracking its trajectory using a Kalman filter. In order to prove that our approach works effectively, we conduct a thorough performance evaluation study. Our evaluation results confirm that the CLEAN-SAGE algorithm can achieve estimation performance beyond the ISAC system's inherent bandwidth and beamwidth constraints. Furthermore, we provide insights into how system configurations, including antenna size, beam overlap, and the number of iterations in the SAGE algorithm, influence its performance.

关键词： Edge processing IoT applications mmWave CLEAN and SAGE WiFi sensing Super-resolution algorithm Localization and tracking

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Flow Correlator: A Flow Table Cache Management Strategy 33

Flow Correlator: A Flow Table Cache Management Strategy

引用

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

作者： McHale, Luke Gratz, Paul, V Sprintson, Alex Two Six Technol Arlington VA 22203 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9798350348439;9798350384611

Switching, routing, and security functions are the backbone of packet processing networks. Fast and efficient processing of packets requires maintaining the state information for many transient network connections. In particular, modern stateful firewalls, security monitoring devices, and Software-Defined Networking (SDN) dataplanes require maintaining stateful flow tables. These flow tables often grow much larger than can fit on-chip, requiring caching to maintain performance. This paper focuses on improving caching efficiency, an important architectural component of the packet processing data planes. We present a novel predictive approach (Flow Correlator) to network flow table cache management by adapting the Hashed Perceptron binary classifier to improve the reliability and performance of the data plane caching. We also discovered an iterative approach to feature selection and ranking while adapting the Hashed Perceptron mechanism to network flow table cache management. Through extensive experimentation, we demonstrate improved caching efficiency of the proposed Flow Correlator mechanism. We also rigorously validate the performance and generic applicability of our technique across real-world datasets.

关键词： Packet switching

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Adversarial Attack Detection for Deep Learning Driving Maneuver Classifiers in Connected Autonomous Vehicles 33

Adversarial Attack Detection for Deep Learning Driving Maneu...

引用

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

作者： Sen, Tanmoy Shen, Haiying Univ Virginia Dept Comp Sci Charlottesville VA 22904 USA

ISBN: (纸本)9798350348439;9798350384611

Connected and autonomous vehicles (CAVs) will be equipped with onboard deep neural network (DNN) models for processing the data from different sensors and communication units in CAVs and on the roads. In the CAV scenario, each vehicle receives time-series driving signals (e.g., speed, brake status) from nearby vehicles, uses its onboard DNN model for driving maneuver prediction for the nearby vehicles, and then takes corresponding actions for safe and efficient driving. Several black-box adversarial attacks for DNN maneuver classifiers in the CAV scenario have been proposed, in which an attacker deliberately sends false driving signals (i.e., add minimum perturbation to the predicted input) to its nearby vehicle to fool its onboard DNN model to output a wrong class label and cause unwanted traffic incidents or congestion. Though previous research proposed adversarial attack detection methods, the methods are for general DNN models rather than specifically for the time-series driving maneuver classification. To detect such adversarial attacks on the DNN maneuver classifiers in the CAV scenario, in this paper, we analyze the adversarial attacks and propose four different approaches comprised of both statistical and machine learning (ML) based methods. Our trace-driven and real experiments show the combination of all four approaches performs 14% better in accuracy rate and 12% better in precision rate compared to the state-of-the-art statistical and ML-based methods. These improvements are critical for driving safety in CAV scenarios.

关键词： Adversarial attack Connected autonomous vehicle Machine learning

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LibGuard: Protecting Sensitive Data In Android Third-Party Libraries From XLDH Attacks 33

LibGuard: Protecting Sensitive Data In Android Third-Party L...

引用

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

作者： He, Fannv Wang, Jice Huang, Yuhang Peng, Xiancui Zhang, Yuqing Univ Chinese Acad Sci Natl Comp Network Intrus Protect Ctr Beijing Peoples R China Hainan Univ Sch Cyberspace Secur Haikou Peoples R China

ISBN: (纸本)9798350348439;9798350384611

Mobile app vendors/developers extensively integrate third-party libraries into mobile applications. While they enrich the functions of apps, third-party libraries also bring in security risks. It has been widely studied that malicious third-party libraries could collect users' sensitive data from the host apps and the app backend servers. Recent research has reported a new attack vector - malicious libraries strategically target other vendors' library(SDKs) integrated in the same host app to harvest private user data. In this paper, we found two new dimensions of cross library data harvesting(XLDH) attack with serious privacy impacts that start from two new attack surfaces - accessing sensitive fields and accessing sensitive storage. However, the mitigation scheme, significantly, has not been yet studied. To prevent the leaks of sensitive data due to XLDH activities, we first proposed a mitigation scheme - LibGuard, which has been proven to be effective without affecting user's experience on real-world apps.

关键词： Third-party Library Attack Defense Access control

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A Cost-Effective Long-Distance Industrial IoT System 33

A Cost-Effective Long-Distance Industrial IoT System

引用

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

作者： Wang, Guoping Purdue Univ Ft Wayne Dept Elect & Comp Engn 2101 E Coliseum Blvd Ft Wayne IN 46805 USA

ISBN: (纸本)9798350348439;9798350384611

The Internet of Things (IoT) refers to a network of physical devices embedded with sensors, actuators, software, and other technologies that can connect and exchange data with other devices via the Internet. These devices range from everyday household items to advanced industrial tools. This paper outlines a cost-effective, long-range Industrial IoT system. This system adopts licensed RF transceivers, wireless sensor nodes, a receiver, a Raspberry Pi MQTT/SparkPlug B client, and an Ignition SCADA system. This proposed IIoT system provides a good option where LoRa technology may not be a viable solution due to its constraints.

关键词： IIoT Long-Range Remote Sensing

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Fake Base Station Detection and Blacklisting 33

Fake Base Station Detection and Blacklisting

引用

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

作者： Purification, Sourav Wuthier, Simeon Kim, Jinoh Kim, Jonghyun Chang, Sang-Yoon Univ Colorado Colorado Springs Colorado Springs CO 80918 USA Texas A&M Univ Commerce Commerce TX 75428 USA Elect & Telecommun Res Inst Daejeon 34129 South Korea

ISBN: (纸本)9798350348439;9798350384611

A fake base station is a well-known security issue in mobile networking. The fake base station exploits the vulnerability in the broadcasting message announcing the base station's presence, which is called SIB1 in telecommunications protocols such as 4G LTE and 5G NR, to get the user equipment to connect to itself. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discover that a fake base station (which engages the user equipment until parts of the connectivity setup and then discontinues with the protocol) can disable the victim user equipment's connectivity for an indefinitely long time, which we validate using our threat prototype against current 4G/5G practice. We design and build a detection and blacklisting identification of the fake base station so that the user equipment can avoid the base station and move on to connecting to a legitimate base station for the connectivity availability. Our detection and blacklisting scheme builds on the standardized 5G protocol and requires the implementation only on the user equipment (no further protocol changes), facilitating practicality. Our scheme uses the real-time information of both the time duration and the number of request transmissions, which features are directly impacted by the fake base station's threat and have not been studied in the previous research. We implement both the base station and the user defense on software-defined radio using open-source 5G software (srsRAN and Open5GS) for validations. We vary the base station implementation to simulate legitimate vs. faulty-but-legitimate vs. fake-and-malicious base stations, where the faulty base station notifies the connectivity disruption and releases the session while the fake base station continues to hold the session. We empirically analyze the detection and identification thresholds, which vary with the fake base station's power and the channel condition. By strategically selecting the thre

关键词： Telecommunications Networking 5G 6G Fake Base Station Detection Blacklisting Security Availability

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Impact of Latency and Bandwidth Limitations on the Safety Performance of Collaborative Perception 33

Impact of Latency and Bandwidth Limitations on the Safety Pe...

引用

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

作者： Shenkut, Dereje Kumar, B. V. K. Vijaya Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9798350348439;9798350384611

The use of vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications for the exchange of sensor data on roads is becoming an important strategy to improve the safety of both vehicles and other road users. Termed collaborative perception (CP), this method offers potential solutions to overcome limitations inherent to single-agent vehicle perception systems, including occlusions, non-line-of-sight scenarios, and the poor detection of distant objects. However, the need for exchanging sensor data and extracted information also presents communication challenges, particularly due to latency and bandwidth limitations. Earlier studies have focused on developing machine learning (ML) modules to address latency issues and compress data before transmission. This study investigates the impact of communication latency and bandwidth limitations on collaborative perception. We examine the interrelationships between CP performance and communication limitations. We present the results of numerical experiments using the V2X-Sim dataset on state-of-the-art CP methods. Our findings demonstrate the need for designing a CP system in a connected environment that effectively mitigates the impact of latency and communication interruptions, thus improving the reliability and safety of CP systems.

关键词： Bandwidth

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Enhancing Capacity of Optical Circuit Switching Clos Network in Data Center: Progress and Challenges 33

Enhancing Capacity of Optical Circuit Switching Clos Network...

引用

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

作者： Okil, Eiji Taka, Haruto Inoue, Takeru Kyoto Univ Kyoto Japan NTT Corp NTT Network Innovat Labs Tokyo Japan

ISBN: (纸本)9798350348439;9798350384611

Future data centers are anticipated to embrace cutting-edge circuit switching technologies, particularly optical switching, renowned for their heightened transmission capacity and energy efficiency. Optical circuit switching guarantees consistent communication quality by establishing dedicated connections for data transmission and maintaining their integrity. Data centers favor Clos-network structures due to their scalability. This paper examines the advancements in designing Clos networks to boost switching capacity while maintaining internal blocking quality. We analyze the characteristics of Clos network design models, providing essentials. We extensively discuss their performances regarding their switching capacities and computation times. Drawing from the review of research progress, we address the challenges in enhancing the switching capacity of Clos networks for future studies.

关键词： Clos network optical circuit switching data center switching capacity blocking probability

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Explainability-Informed Targeted Malware Misclassification 33

Explainability-Informed Targeted Malware Misclassification

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

作者： Card, Quincy Aryal, Kshitiz Gupta, Maanak Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

ISBN: (纸本)9798350348439;9798350384611

In recent years, there has been a surge in malware attacks across critical infrastructures, requiring further research and development of appropriate response and remediation strategies in malware detection and classification. Several works have used machine learning models for malware classification into categories, and deep neural networks have shown promising results. However, these models have shown its vulnerabilities against intentionally crafted adversarial attacks, which yield misclassification of a malicious file. Our paper explores such adversarial vulnerabilities of neural network based malware classification systems in the dynamic and online analysis environments. To evaluate our approach, we trained Feed Forward Neural networks (FFNN) to classify malware categories based on features obtained from dynamic and online analysis environments. We use the state-of-the-art method, SHapley Additive exPlanations (SHAP), for the feature attribution for malware classification, to inform the adversarial attackers about the features with significant importance in classification decisions. Using the explainability-informed features, we perform targeted misclassification adversarial white-box evasion attacks using the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks against the trained classifier. Our results demonstrated a high evasion rate for some instances of attacks, showing a clear vulnerability of a malware classifier for such attacks. We offer recommendations for a balanced approach and a benchmark for much-needed future research into evasion attacks against malware classifiers, and develop more robust and trustworthy solutions.

关键词： Explainability Adversarial Dynamic Analysis Online Analysis White-box attacks Machine learning Robust Trustworthy

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Lightweight Coordinated Sampling for Dynamic Flows under Budget Constraints 33

Lightweight Coordinated Sampling for Dynamic Flows under Bud...

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

作者： Chen, Mingming La Porta, Thomas Jaeger, Trent Krishnamurthy, Srikanth Penn State Univ Comp Sci & Engn Dept University Pk PA 16802 USA Univ Calif Riverside Comp Sci & Engn Dept Riverside CA 92521 USA

ISBN: (纸本)9798350348439;9798350384611

As cyber-attacks on networks become more stealthy, monitoring techniques relying on low-rate packet sampling may prove insufficient to detect attacks. While various sampling methods have been proposed to address capacity limitations and enhance detection rates, achieving sampling at line speed at a single point remains challenging due to limited CPU or bandwidth capacity at sampling points. In this paper, we propose harnessing coordinating sampling across switches to create a unified system that can dynamically activate sampling points to meet sampling rate needs. We introduce and implement a coordinated sampling algorithm on multiple P4-programmable switches and show that the algorithm ensures coordination among multiple sampling points for each flow, preventing duplicate samples, with negligible network overhead and real-time configurability. We formulate sampling point placement as budgeted maximum multi-coverage problems, solving them optimally in pseudo-polynomial time. We show our system far outperforms those based on greedy algorithms along many key dimensions.

关键词： P4-programmable switch coordinated sampling budgeted maximum multi-coverage balanced matrix

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