Community detection based on similarities of communication behavior in IP networks

Zhang, Shuzhuang; Zhang, Yaning; Zhou, Min; Peng, Lizhi

doi:10.1007/s12652-020-02681-w

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…e Louvain-LSCR algorithm is compared and analyzed through experiments, and two evaluation indicators of modularity Q and number of communities are used to analyze the pros and cons of the algorithm [42]. What can reflect the quality of community detection is the modularity Q and the number of communities.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

Liu

Huang

Zhang

et al. 2021

Scientific Programming

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According to the needs of scientific research project research and development, the research of cooperative team excavation methods was carried out. Aiming at the current difficulties in accurately and reliably defining and identifying cooperative research teams from co-author network, an improved Louvain algorithm that integrates core node recognition was proposed: Louvain-LSCR algorithm. Based on the analysis of the Louvain algorithm, considering the local topology of the node in the network and the communication range of the node, a new algorithm LSCR for core node identification was constructed. The LSCR algorithm and Louvain algorithm were merged to obtain a new and improved algorithm, Louvain-LSCR. In this algorithm, the leaf nodes in phase 1 of Louvain algorithm were first pruned to reduce calculations; then, seed nodes were selected according to the LSCR algorithm in phase 2. The experimental results on related datasets show that LSCR algorithm has certain advantages in identifying core nodes. The modularity of Louvian-LSCR algorithm is better than other algorithms, and the community structure is more reasonable. It was verified that the algorithm can mine potential cooperative research teams in co-author network.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

Liu

Huang

Zhang

et al. 2021

Scientific Programming

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“…Also, another study conducted by Zhang et al [60] proposed a sheep vs. wolves classification system for intrusion detection in IoT networks. The system used anomaly detection and machine learning techniques to classify listeners as either sheep or wolves based on their behavior patterns and network traffic.…”

Section: Sheep Vs Wolves Scenariomentioning

confidence: 99%

Machine Learning Based Listener Classification and Authentication Using Frequency Following Responses to English Vowels for Biometric Applications

Borzou,

Bouchard,

Dajani

2023

2023 IEEE Sensors Applications Symposium (SAS)

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Auditory Evoked Potentials (AEPs) have recently gained attention as a biometric feature that may improve security and address reliability shortfalls of other commonly-used biometric features.The objective of this thesis is to investigate the accuracy with which subjects can be automatically identified or authenticated with machine learning (ML) techniques using a type of AEP known as the speech-evoked frequency following response (FFR).Accordingly, the results show more accurate discrimination between FFRs from different subjects than what has been reported in past studies. The accuracy improvement is searched either by optimized hyperparameter tuning of the ML model or extracting new features from FFRs and feeding them as inputs to the model. Finally, the accuracy of authenticating subjects using FFRs is investigated using a "sheep vs. wolves" scenario.The results of this work shed more light on the potential of use of speech-evoked FFRs in biometric identification and authentication systems.Deciding to pursue a second M.Sc. program after completing my PhD was a turning point in my life and professional career, which I will always remember. Moving forward on this path was impossible without the support and help of my supervisors. Therefore, I would like to express my sincere gratitude to Dr. Martin Bouchard and Dr. Hilmi Dajani for giving me this opportunity and guiding my research. Their fantastic guidance and cooperation throughout my M.Sc. studies were invaluable.Once again, similar to my previous Ph.D., M.Sc., and B.A.Sc. theses, I would like to express my heartfelt thanks to my best friend forever, Mohammad Alavirad, whom I cannot call anything but my brother. His help, presence, and dedication during the last ten years of my life cannot be expressed in words. Without him, it would have been impossible to overcome the challenges and complete this long journey.My parents have played an essential role in helping me achieve our dreams. I am blessed to have parents who have supported and encouraged me during the lowest of lows and the highest of highs, fueling my every moment.

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“…In the cloud-based architecture [1], the tracking loop software of the baseband is deployed on a cloud-based platform constructed using general-purpose servers, known as cloud-based baseband, while the radio freqency (RF) front-end communicates with the cloud-based baseband through network interfaces for digital signal transmission. Compared to traditional TT&C architectures characterised by dedicated baseband processing and RF cable connections, the IP-based cloud TT&C network architecture offers a range of advantages [2][3][4], including simplification of the signal transmission chain, improved signal transmission efficiency and enhanced system flexibility. However, this evolution also introduces new challenges in the performance of tracking loops.…”

Section: Introductionmentioning

confidence: 99%

Robust packet loss compensation in the cloud‐based TT&C receiver using a predictive tracking loop with RBF network identification

Fan,

Liu,

et al. 2023

IET Radar Sonar & Navi

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In recent years, ground‐based aerospace tracking, telemetry and control (TT&C) systems have been shifting towards an IP‐based cloud TT&C network architecture to improve signal transmission efficiency and system flexibility. However, this new architecture presents new challenges for the performance of tracking loops in the cloud‐based TT&C receiver. The authors present a predictive compensation tracking loop that addresses the problem of packet loss. The loop utilises radial basis function (RBF) online network identification for robust carrier and code frequency offset tracking. To account for packet loss, the phenomenon is modelled as a Bernoulli random process, and the predictive compensation loop uses the trained RBF network to maintain the system's tracking state. When normal data are received again, the loop can track the received information well and align the frequency offset locking state. The authors compare the performance of the proposed predictive compensation tracking loop with traditional loop tracking performance using data hold strategies under different packet loss rates. The results demonstrate the effectiveness of the proposed approach in maintaining stable tracking and solving in the discontinuous reception of the loop under packet loss environments. The proposed predictive compensation tracking loop offers a practical solution for addressing packet loss, which is a common issue in real‐world applications.

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Community detection based on similarities of communication behavior in IP networks

Cited by 8 publications

References 28 publications

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

Machine Learning Based Listener Classification and Authentication Using Frequency Following Responses to English Vowels for Biometric Applications

Robust packet loss compensation in the cloud‐based TT&C receiver using a predictive tracking loop with RBF network identification

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