Baseline Traffic Modeling for Anomalous Traffic Detection on Network Transit Points

2009 ◽  
pp. 385-394
Author(s):  
Yoohee Cho ◽  
Koohong Kang ◽  
Ikkyun Kim ◽  
Kitae Jeong
Keyword(s):  
Traffic Modeling ◽  
Traffic Detection

Urban Traffic Modeling and Simulation: Moroccan Capital Case Study

2018 ◽  
Author(s):  
Aboutaib Brahim ◽  
Bahili Lahoucine ◽  
Fonlupt Cyril ◽  
Virginie Marion ◽  
Sebastiaan Verelst
Keyword(s):  
Modeling And Simulation ◽  
Urban Traffic ◽  
Traffic Modeling ◽  
Capital Case

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

Griffin: An Ensemble of AutoEncoders for Anomaly Traffic Detection in SDN

2020 ◽  
Author(s):  
Liyan Yang ◽  
Yubo Song ◽  
Shang Gao ◽  
Bin Xiao ◽  
Aiqun Hu
Keyword(s):  
Traffic Detection

scholarly journals DarkDetect: Darknet Traffic Detection and Categorization using Modified Convolution-Long Short-Term Memory

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Muhammad Bilal Sarwar ◽  
Muhammad Kashif Hanif ◽  
Ramzan Talib ◽  
Muhammad Younas ◽  
Muhammad Umer Sarwar
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Traffic Detection

scholarly journals Augmenting the Floating Car Data Approach by Dynamic Indirect Traffic Detection

2012 ◽  
Vol 48 ◽  
pp. 1525-1534 ◽  
Author(s):  
Sten Ruppe ◽  
Marek Junghans ◽  
Mathias Haberjahn ◽  
Christian Troppenz
Keyword(s):  
Floating Car Data ◽  
Traffic Detection
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