A multi-user interference detection method for MU-MIMO transmission in LTE advanced system

2012 ◽  
Author(s):  
Won Jun Hwang ◽  
Wi Pil Kang ◽  
Jun Hee Jang ◽  
Hyung Jin Choi
Keyword(s):  
Detection Method ◽  
Interference Detection ◽  
Lte Advanced ◽  
Mimo Transmission ◽  
User Interference ◽  
Advanced System

scholarly journals Interference detection method using wireless LAN based MIMO transmission

2013 ◽  
Vol 2 (7) ◽  
pp. 307-312 ◽  
Author(s):  
Ryochi Kataoka ◽  
Kentaro Nishimori ◽  
Masaaki Kawahara ◽  
Takefumi Hiraguri ◽  
Hideo Makino
Keyword(s):  
Wireless Lan ◽  
Detection Method ◽  
Interference Detection ◽  
Mimo Transmission

scholarly journals An API Semantics-Aware Malware Detection Method Based on Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Ma ◽  
Shize Guo ◽  
Wei Bai ◽  
Jun Chen ◽  
Shiming Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Malware Detection ◽  
Poor Performance ◽  
Detection Methods ◽  
Interference Detection ◽  
Machine Learning Method ◽  
Accuracy Rate ◽  
Malware Classification ◽  
Self Protection

The explosive growth of malware variants poses a continuously and deeply evolving challenge to information security. Traditional malware detection methods require a lot of manpower. However, machine learning has played an important role on malware classification and detection, and it is easily spoofed by malware disguising to be benign software by employing self-protection techniques, which leads to poor performance for existing techniques based on the machine learning method. In this paper, we analyze the local maliciousness about malware and implement an anti-interference detection framework based on API fragments, which uses the LSTM model to classify API fragments and employs ensemble learning to determine the final result of the entire API sequence. We present our experimental results on Ali-Tianchi contest API databases. By comparing with the experiments of some common methods, it is proved that our method based on local maliciousness has better performance, which is a higher accuracy rate of 0.9734.

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