Packet arrival time in 1588 for 40GE/100GE

2012 ◽  
Author(s):  
David Mendel ◽  
Herman Schmit ◽  
Divya Vijayaraghavan
Keyword(s):  
Arrival Time ◽  
Packet Arrival

Position and Velocity Estimation of a Non-Cooperative Source From Asynchronous Packet Arrival Time Measurements

2018 ◽  
Vol 17 (9) ◽  
pp. 2166-2179 ◽  
Author(s):  
Fabio Ricciato ◽  
Savio Sciancalepore ◽  
Francesco Gringoli ◽  
Nicolo Facchi ◽  
Gennaro Boggia
Keyword(s):  
Arrival Time ◽  
Velocity Estimation ◽  
Packet Arrival

scholarly journals Convolutional Neural Network (CNN)-Based Frame Synchronization Method

2020 ◽  
Vol 10 (20) ◽  
pp. 7267 ◽  
Author(s):  
Eui-Rim Jeong ◽  
Eui-Soo Lee ◽  
Jingon Joung ◽  
Hyukjun Oh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fading Channels ◽  
Arrival Time ◽  
Time Window ◽  
Additive White Gaussian Noise ◽  
Frame Synchronization ◽  
Correlation Peak ◽  
Synchronization Method ◽  
Packet Arrival

A new frame synchronization technique based on convolutional neural network (CNN) is proposed for synchronized networks. To estimate the exact packet arrival time, the receiver typically uses the correlator between the received signal and the preamble or pilot in front of the transmitted packet. The conventional frame synchronization technique searches the correlation peak within the time window. In contrast, the proposed method utilizes a CNN to find the packet arrival time. Specifically, in the proposed method, the 1D correlator output is converted into a 2D matrix by reshaping, and the resulting signal is inputted to the proposed 4-layer CNN classifier. Then, the CNN predicts the packet arrival time. To verify the frame synchronization performance, computer simulation is performed for two channel models: additive white Gaussian noise and fading channels. Simulation results show that the proposed CNN-based synchronization method outperforms the conventional correlation-based technique by 2dB.

scholarly journals Obfuscated Tor Traffic Identification Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenliang Xu ◽  
Futai Zou
Keyword(s):  
Arrival Time ◽  
Sliding Window ◽  
Recall Rate ◽  
Machine Learning Algorithms ◽  
Time Interval ◽  
Traffic Identification ◽  
Packet Arrival ◽  
Packet Length ◽  
Precision Rate ◽  
The Web

Tor is an anonymous communication network used to hide the identities of both parties in communication. Apart from those who want to browse the web anonymously using Tor for a benign purpose, criminals can use Tor for criminal activities. It is recognized that Tor is easily intercepted by the censorship mechanism, so it uses a series of obfuscation mechanisms to avoid censorship, such as Meek, Format-Transforming Encryption (FTE), and Obfs4. In order to detect Tor traffic, we collect three kinds of obfuscated Tor traffic and then use a sliding window to extract 12 features from the stream according to the five-tuple, including the packet length, packet arrival time interval, and the proportion of the number of bytes sent and received. And finally, we use XGBoost, Random Forest, and other machine learning algorithms to identify obfuscated Tor traffic and its types. Our work provides a feasible method for countering obfuscated Tor network, which can identify the three kinds of obfuscated Tor traffic and achieve about 99% precision rate and recall rate.

An Arrival Time and Duration Estimation of a Signal

2000 ◽  
Vol 54 (8-9) ◽  
pp. 122-133
Author(s):  
Andrey Pavlovich Trifonov ◽  
Yurii Eduardovich Korchagin
Keyword(s):  
Arrival Time ◽  
Duration Estimation
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