scholarly journals Malware Detection Using CNN via Word Embedding in Cloud Computing Infrastructure

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Rong Wang ◽  
Cong Tian ◽  
Lin Yan
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Fog Computing ◽  
Word Embedding ◽  
Security And Privacy ◽  
Experimental Result ◽  
Hierarchical Systems ◽  
Feature Maps ◽  
Feature Extraction Method ◽  
Computing Infrastructure

The Internet of Things (IoT), cloud, and fog computing paradigms provide a powerful large-scale computing infrastructure for a variety of data and computation-intensive applications. These cutting-edge computing infrastructures, however, are nevertheless vulnerable to serious security and privacy risks. One of the most important countermeasures against cybersecurity threats is intrusion detection and prevention systems, which monitor devices, networks, and systems for malicious activity and policy violations. The detection and prevention systems range from antivirus software to hierarchical systems that monitor the traffic of whole backbone networks. At the moment, the primary defensive solutions are based on malware feature extraction. Most known feature extraction algorithms use byte N-gram patterns or binary strings to represent log files or other static information. The information taken from program files is expressed using word embedding (GloVe) and a new feature extraction method proposed in this article. As a result, the relevant vector space model (VSM) will incorporate more information about unknown programs. We utilize convolutional neural network (CNN) to analyze the feature maps represented by word embedding and apply Softmax to fit the probability of a malicious program. Eventually, we consider a program to be malicious if the probability is greater than 0.5; otherwise, it is a benign program. Experimental result shows that our approach achieves a level of accuracy higher than 98%.

scholarly journals MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN

2018 ◽  
Vol 11 (2) ◽  
pp. 95 ◽  
Author(s):  
Fransisca J Pontoh ◽  
Jayanti Yusmah Sari ◽  
Amil A Ilham ◽  
Ingrid Nurtanio
Keyword(s):  
Feature Extraction ◽  
Low Cost ◽  
Experimental Result ◽  
Person Identification ◽  
Feature Extraction Method ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Dorsal Hand Vein Recognition

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.

A Method for TLS Malicious Traffic Identification Based on Machine Learning

2021 ◽  
Vol 105 ◽  
pp. 291-301
Author(s):  
Wei Wang ◽  
Cheng Sheng Sun ◽  
Jia Ning Ye
Keyword(s):  
Feature Extraction ◽  
Relevant Information ◽  
Extraction Process ◽  
Identification Accuracy ◽  
Security And Privacy ◽  
Feature Extraction Method ◽  
Traffic Identification ◽  
Work Related ◽  
Encrypted Traffic ◽  
Network Security Management

With more and more malicious traffic using TLS protocol encryption, efficient identification of TLS malicious traffic has become an increasingly important task in network security management in order to ensure communication security and privacy. Most of the traditional traffic identification methods on TLS malicious encryption only adopt the common characteristics of ordinary traffic, which results in the increase of coupling among features and then the low identification accuracy. In addition, most of the previous work related to malicious traffic identification extracted features directly from the data flow without recording the extraction process, making it difficult for subsequent traceability. Therefore, this paper implements an efficient feature extraction method with structural correlation for TLS malicious encrypted traffic. The traffic feature extraction process is logged in modules, and the index is used to establish relevant information links, so as to analyse the context and facilitate subsequent feature analysis and problem traceability. Finally, Random Forest is used to realize efficient TLS malicious traffic identification with an accuracy of up to 99.38%.

scholarly journals Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01118
Author(s):  
Fan Tongke
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Disease Identification

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.

scholarly journals Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

2019 ◽  
Vol 38 (2) ◽  
pp. 441-456 ◽  
Author(s):  
Baokang Yan ◽  
Bin Wang ◽  
Fengxing Zhou ◽  
Weigang Li ◽  
Bo Xu
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Matching Pursuit ◽  
Maximum Energy ◽  
Rotating Machinery ◽  
Orthogonal Matching Pursuit ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
S Transform ◽  
Generalized S Transform

In order to extract fault impulse feature of large-scale rotating machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined multiresolution generalized S transform is proposed in this paper. In this method, multiresolution generalized S transform is employed to find the optimal atom for every iteration, which firstly takes in to account the generalized S transform with discretized adjustment factors, then builds an atom corresponding to the maximum energy. The multiresolution generalized S transform has better accuracy compared to generalized S transform and faster searching speed compared to the orthogonal matching pursuit method in selecting the optimal atom. Then, the orthogonal matching pursuit method is used to decompose the signal into several optimal atoms. The proposed method is applied to analyze the simulated signal and vibration signals collected from experimental failure rolling bearings. The results prove that the proposed method has better performances such as high precision and fast decomposition speed than the traditional orthogonal matching pursuit method method and local mean decomposition method.

scholarly journals Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

CONVERTER ◽  
2021 ◽  
pp. 681-695
Author(s):  
Zheng Yan
Keyword(s):  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Method ◽  
Large Scale ◽  
Anchor Bolt ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Empirical Wavelet Transform ◽  
Bolt Loosening ◽  
Fault Feature Extraction

Escalator is an essential large-scale public transportation equipment. Once the failure occurs, it will inevitably affect the operation and even cause safety accidents.  As an important part of the structure of escalator, the loosening of the anchor bolt will lead to abnormal operation of escalator.  Aiming at the current difficultyin extracting the fault features of anchor bolt loosening, a fault feature extraction method of escalator anchor loosening is constructed based on empirical wavelet transform (EWT) and bispectrum analysis. First, perform EWT decomposition of the original footing vibration acceleration signal to obtain a series of empirical mode functions(EMFs).Then, for each empirical mode function, the bispectrum was calculated by using bispectrum analysis method, and six texture features of the bispectrum were extracted as fault feature vectors by means of gray-gradient co-occurrence matrix.  Finally, the extracted multi-scale fault feature vectors and bi-directional longshort-term memory (BI-LSTM) were used to classify and identify the four types of fault signals with different degrees of foot loosening, and the fault types of foot loosening were determined. The results show that the feature extraction method based on empirical wavelet decomposition and bispectrum analysis can more effectively identify the loosening level of anchor bolts.

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