A Practical Botnet Traffic Detection System Using GNN

2022 ◽  
pp. 66-78
Author(s):  
Bonan Zhang ◽  
Jingjin Li ◽  
Chao Chen ◽  
Kyungmi Lee ◽  
Ickjai Lee
Keyword(s):  
Detection System ◽  
Traffic Detection

scholarly journals Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenmin Li ◽  
Sanqi Sun ◽  
Shuo Zhang ◽  
Hua Zhang ◽  
Yijie Shi
Keyword(s):  
Feature Extraction ◽  
Cost Function ◽  
Network Traffic ◽  
Detection System ◽  
Imbalanced Data ◽  
Detection Performance ◽  
Learning Technology ◽  
The Cost ◽  
Decision Boundaries ◽  
Traffic Detection

Aim. The purpose of this study is how to better detect attack traffic in imbalance datasets. The deep learning technology has played an important role in detecting malicious network traffic in recent years. However, it suffers serious imbalance distribution of data if the traffic model skews towards the modeling in the benign direction, because only a small portion of traffic is malicious, while most network traffic is benign. That is the reason why the authors wrote this manuscript. Methods. We propose a cost-sensitive approach to improve the HTTP traffic detection performance with imbalanced data and also present a character-level abstract feature extraction approach that can provide features with clear decision boundaries in addition. Finally, we design a spark-based HTTP traffic detection system based on these two approaches. Results. The methods proposed in this paper work well in imbalanced datasets. Compared to other methods, the experiment results indicate that our system has F1-score in a high precision. Conclusion. For imbalanced HTTP traffic detection, we confirmed that the method of feature extraction and the cost function is very effective. In the future, we may focus on how to use the cost function to further improve detection performance.

An Abnormal IP Traffic Detection Model Based on Scale-Free Network

2010 ◽  
Vol 44-47 ◽  
pp. 849-853
Author(s):  
Jun Li ◽  
Yan Niu
Keyword(s):  
Detection System ◽  
Anomalous Behavior ◽  
Power Exponent ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Detection Model ◽  
Free Network ◽  
Traffic Detection ◽  
Entire Network

A model of detecting an abnormal IP traffic in a subset of network is described. The model is based on the hypothesis that random sampling subnet are the same probability distribution as the entire network if some conditions are met with, nodes’s degree in IP traffic can be processed as a power-law distribution in scale-free network . The model analyzes the power exponent and relations between the anomalous behavior and parameter r. Finally, a test was conducted by the data, some type attacks could be identified exactly. the model provides a new framework for intrusion-detection system.

scholarly journals A Sensor Fused Rear Cross Traffic Detection System Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6055
Author(s):  
Jungme Park ◽  
Wenchang Yu
Keyword(s):  
Processing Time ◽  
Detection System ◽  
Detection Accuracy ◽  
Driver Assistance Systems ◽  
The Road ◽  
Cross Traffic ◽  
Fast Processing ◽  
On The Road ◽  
Wide Angle Camera ◽  
Traffic Detection

Recent emerging automotive sensors and innovative technologies in Advanced Driver Assistance Systems (ADAS) increase the safety of driving a vehicle on the road. ADAS enhance road safety by providing early warning signals for drivers and controlling a vehicle accordingly to mitigate a collision. A Rear Cross Traffic (RCT) detection system is an important application of ADAS. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions. The RCT detection system detects obstacles at the rear while the car is backing up. In this paper, a robust sensor fused RCT detection system is proposed. By combining the information from two radars and a wide-angle camera, the locations of the target objects are identified using the proposed sensor fused algorithm. Then, the transferred Convolution Neural Network (CNN) model is used to classify the object type. The experiments show that the proposed sensor fused RCT detection system reduced the processing time 15.34 times faster than the camera-only system. The proposed system has achieved 96.42% accuracy. The experimental results demonstrate that the proposed sensor fused system has robust object detection accuracy and fast processing time, which is vital for deploying the ADAS system.

Automatic Traffic Detection System

2009 ◽  
Author(s):  
Michal Lyczek ◽  
Lukasz Kaminski ◽  
Agata Chrobak ◽  
Anna Kulka
Keyword(s):  
Detection System ◽  
Traffic Detection

Modeling and Simulation of Vehicle Traffic Detection System Based on Ground Sense Coil

2014 ◽  
Vol 989-994 ◽  
pp. 2511-2514 ◽  
Author(s):  
Jing Wen Ju ◽  
Jiao Yu Liu ◽  
Wei Yang ◽  
Ning Liu
Keyword(s):  
Modeling And Simulation ◽  
Detection System ◽  
Frequency Change ◽  
Square Wave ◽  
Coil Inductance ◽  
Oscillation Circuit ◽  
Detection Circuit ◽  
Point Detection ◽  
Traffic Detection ◽  
Sense Coil

In this paper, the traffic detection system based on ground sense coil is studied. Due to the change of ground sense coil inductance, the output waveform frequence of LC oscillation circuit changes [1]. The LC oscillation circuit is composed by the three point detection circuit. Then the wave is shaped into a square-wave by shaping circuit. The square-wave is transmitted into MCU, the frequency change is detected by MCU and motor is controlled by control signal from the MCU.So when a vehicle passes above the ground sense coil, this approach makes automobile blockader lift the stop lever automatically. And the system can stop the lever falling when there is a vehicle below the lever, so this system can prevent the vehicle damage. This system can improve practical value and application prospect of a automobile blockader.

Sign in / Sign up

Export Citation Format

Share Document