psOBJ: Defending against Traffic Analysis with pseudo-Objects

2014 ◽  
pp. 96-109 ◽  
Author(s):  
Yi Tang ◽  
Piaoping Lin ◽  
Zhaokai Luo
Keyword(s):  
Traffic Analysis

Traffic Analysis and Improvement Measures of On-Ramp Joints of Elevated Expressways

ICLEM 2014 ◽  
2014 ◽  
Author(s):  
Qiu Yan ◽  
Yuhang Zhang ◽  
Lianghua Cai ◽  
Wentong Zhu
Keyword(s):  
Traffic Analysis ◽  
Improvement Measures

Net_Watch: A Tool for Network Traffic Analysis

2020 ◽  
Author(s):  
Sumit Kumari ◽  
Neetu Sharma ◽  
Prashant Ahlawat
Keyword(s):  
Network Traffic ◽  
Traffic Analysis ◽  
Network Traffic Analysis

Adaptive Meek Technology for Anti-Traffic Analysis

2020 ◽  
Author(s):  
Haoyao Xie ◽  
Liangmin Wang ◽  
Shangnan Yin ◽  
Hui Zhao ◽  
Hao Shentu
Keyword(s):  
Traffic Analysis

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

scholarly journals User Profiling Using Smartphone Network Traffic Analysis

2021 ◽  
Author(s):  
Ayush Bahuguna ◽  
Ankit Agrawal ◽  
Ashutosh Bhatia ◽  
Kamlesh Tiwari ◽  
Deepak Vishwakarma
Keyword(s):  
Network Traffic ◽  
Traffic Analysis ◽  
User Profiling ◽  
Network Traffic Analysis
Sign in / Sign up

Export Citation Format

Share Document