scholarly journals A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Yuntao Zhao ◽  
Bo Bo ◽  
Yongxin Feng ◽  
ChunYu Xu ◽  
Bo Yu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Dimensional Space ◽  
Semantic Segmentation ◽  
Dynamic Feature ◽  
Feature Extraction Method ◽  
Malicious Behavior ◽  
Internet Users ◽  
Performance Indexes ◽  
N Gram

With explosive growth of malware, Internet users face enormous threats from Cyberspace, known as “fifth dimensional space.” Meanwhile, the continuous sophisticated metamorphism of malware such as polymorphism and obfuscation makes it more difficult to detect malicious behavior. In the paper, based on the dynamic feature analysis of malware, a novel feature extraction method of hybrid gram (H-gram) with cross entropy of continuous overlapping subsequences is proposed, which implements semantic segmentation of a sequence of API calls or instructions. The experimental results show the H-gram method can distinguish malicious behaviors and is more effective than the fixed-length n-gram in all four performance indexes of the classification algorithms such as ID3, Random Forest, AdboostM1, and Bagging.

Research of Feature Extraction Method on Facial Expression Change

2011 ◽  
Vol 211-212 ◽  
pp. 813-817 ◽  
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Facial Expression ◽  
Extraction Method ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Low Frequency ◽  
Expression Change ◽  
Feature Extraction Method ◽  
Low Dimensional

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

Dynamic Feature Extraction Method of Phone Speaker Based on Deep Learning

2020 ◽  
Vol 13 ◽  
Author(s):  
Hongbing Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Speech Recognition ◽  
Extraction Method ◽  
Good Effect ◽  
Dynamic Feature ◽  
Dynamic Features ◽  
Feature Extraction Method ◽  
Speech Feature ◽  
Speech Feature Extraction

: Nowadays, speech recognition has become one of the important technologies for human-computer interaction. Speech recognition is essentially a process of speech training and pattern recognition, which makes feature extraction technology particularly important. The quality of feature extraction is directly related to the accuracy of speech recognition. Dynamic feature parameters can effectively improve the accuracy of speech recognition, which makes the speech feature dynamic feature extraction has higher research value. The traditional dynamic feature extraction method is easy to generate more redundant information, resulting in low recognition accuracy. Therefore, based on a new speech feature extraction method, a method based on deep learning for speech feature extraction is proposed. Firstly, speech signal is preprocessed by pre-emphasis, windowing, filtering and endpoint detection. Then, the sliding differential cepstral feature (SDC) is extracted, which contains the voice information of the front and back frames. Finally, the feature is used as input to extract the dynamic features that represent the depth essence of speech information through the deep self-encoding neural network. The simulation results show that the dynamic features extracted by in-depth learning have better recognition performance than the original features, and have a good effect in speech recognition.

scholarly journals Dynamic Feature Extraction Method of Driver's Propensity under Complicated Vehicle Group

2013 ◽  
Vol 5 ◽  
pp. 287653 ◽  
Author(s):  
Xiaoyuan Wang ◽  
Jinglei Zhang ◽  
Xuegang(Jeff) Ban ◽  
Derong Tan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vehicle Group ◽  
Dynamic Feature ◽  
Feature Extraction Method

scholarly journals N-Gram Language Model to Predict the Word Sequence in a Degraded Braille Document.

2020 ◽  
Vol 9 (4) ◽  
pp. 1442-1445
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Language Model ◽  
Document Image ◽  
Language Models ◽  
Indian Language ◽  
Feature Extraction Method ◽  
Word Sequence ◽  
Physical Position ◽  
N Gram

This paper presents a feature extraction method for optical Braille recognition (OBR) system to locate, extract and convert the Braille cells in one sided Indian language Braille documents. The Braille cells are located by implementing a gridbox designed using physical properties of a Braille cell. A Braille document image is a compilation of group of six dots. The physical position of each dot and its relevance with other neighboring dots in a single cell gives various Braille characters. After the grid-box is mapped with the Braille cells in the document, the mesh characters are extracted and are then mapped with existing database to translate them in required text. Mapping of Braille cells with mesh box and separation of characters and words from a Braille document was a challenging task. The unwanted dots or degraded dots way result in incorrect mapping of characters. In this paper we have used N-gram Language Models to Predict the word Sequence in case of wrong mapping of characters in extraction and conversion of the Braille cells.

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