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

Interference detection using wireless LAN based MIMO transmission

2013 ◽  
Author(s):  
Masaaki Kawahara ◽  
Kentaro Nishimori ◽  
Ryochi Kataoka ◽  
Takefumi Hiraguri ◽  
Hideo Makino
Keyword(s):  
Wireless Lan ◽  
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.

scholarly journals A Novel Interference Detection Method of STAP Based on Simplified TT Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Qiang Wang ◽  
Yongshun Zhang ◽  
Hanwei Liu ◽  
Yiduo Guo
Keyword(s):  
Detection Method ◽  
Physical Property ◽  
Training Sample ◽  
Space Time ◽  
Detection Performance ◽  
Inner Product ◽  
Interference Detection ◽  
Large Power ◽  
Training Samples ◽  
Performance Reduction

Training samples contaminated by target-like signals is one of the major reasons for inhomogeneous clutter environment. In such environment, clutter covariance matrix in STAP (space-time adaptive processing) is estimated inaccurately, which finally leads to detection performance reduction. In terms of this problem, a STAP interference detection method based on simplified TT (time-time) transform is proposed in this letter. Considering the sparse physical property of clutter in the space-time plane, data on each range cell is first converted into a discrete slow time series. Then, the expression of simplified TT transform about sample data is derived step by step. Thirdly, the energy of each training sample is focalized and extracted by simplified TT transform from energy-variant difference between the unpolluted and polluted stage, and the physical significance of discarding the contaminated samples is analyzed. Lastly, the contaminated samples are picked out in light of the simplified TT transform-spectrum difference. The result on Monte Carlo simulation indicates that when training samples are contaminated by large power target-like signals, the proposed method is more effective in getting rid of the contaminated samples, reduces the computational complexity significantly, and promotes the target detection performance compared with the method of GIP (generalized inner product).

scholarly journals A GNSS Interference Detection Method Based on Multiple Ground Stations

2012 ◽  
Vol 1 (1) ◽  
pp. 15-21
Author(s):  
Sun Young Kim ◽  
Chang Ho Kang ◽  
Jeong Hwan Yang ◽  
Chan Gook Park ◽  
Jung Min Joo ◽  
...  
Keyword(s):  
Detection Method ◽  
Interference Detection
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