scholarly journals PBDT: Python Backdoor Detection Model Based on Combined Features

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yong Fang ◽  
Mingyu Xie ◽  
Cheng Huang
Keyword(s):  
Research Work ◽  
Classification Performance ◽  
Statistical Characteristics ◽  
Language Differences ◽  
Application Security ◽  
Detection Model ◽  
Combined Features ◽  
Good Classification Performance ◽  
Opcode Sequence ◽  
The Common

Application security is essential in today’s highly development period. Backdoor is a means by which attackers can invade the system to achieve illegal purposes and damage users’ rights. It has posed a serious threat to network security. Thus, it is urgent to take adequate measures to defend such attacks. Previous research work was mainly focused on numerous PHP webshells, with less research on Python backdoor files. Language differences make the method not entirely applicable. This paper proposes a Python backdoor detection model named PBDT based on combined features. The model summarizes the common functional modules and functions in the backdoor files and extracts the number of calls in the text to form sample features. What is more, we consider the text’s statistical characteristics, including the information entropy, the longest string, etc., to identify the obfuscated Python code. Besides, the opcode sequence is used to represent code characteristics, such as TF-IDF vector and FastText classifier, to eliminate the influence of interference items. Finally, we introduce the Random Forest algorithm to build a classifier. Covering most types of backdoors, some samples are obfuscated, the model achieves an accuracy of 97.70%, and the TNR index is as high as 98.66%, showing a good classification performance in Python backdoor detection.

scholarly journals Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Venkatesan Rajinikanth ◽  
Tapan Kumar Gandhi
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Detection Model ◽  
Three Phase ◽  
The Common ◽  
Lung Ct

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

Improved Fake Reviews Detection Model Based on Vertical Ensemble Tri-Training and Active Learning

2021 ◽  
Vol 12 (3) ◽  
pp. 1-19
Author(s):  
Chunyong Yin ◽  
Haoqi Cuan ◽  
Yuhang Zhu ◽  
Zhichao Yin
Keyword(s):  
Active Learning ◽  
Large Scale ◽  
User Behavior ◽  
Classification Performance ◽  
Online Activity ◽  
Detection Model ◽  
Model Based ◽  
Group Integration ◽  
Good Classification Performance ◽  
Fake Reviews

People’s increasingly frequent online activity has generated a large number of reviews, whereas fake reviews can mislead users and harm their personal interests. In addition, it is not feasible to label reviews on a large scale because of the high cost of manual labeling. Therefore, to improve the detection performance by utilizing the unlabeled reviews, this article proposes a fake reviews detection model based on vertical ensemble tri-training and active learning (VETT-AL). The model combines the features of review text with the user behavior features as feature extraction. In the VETT-AL algorithm, the iterative process is divided into two parts: vertical integration within the group and horizontal integration among the groups. The intra-group integration is to integrate three original classifiers by using the previous iterative models of the classifiers. The inter-group integration is to adopt the active learning based on entropy to select the data with the highest confidence and label it, and as the result of that, the second generation classifiers are trained by the traditional process to improve the accuracy of the label. Experimental results show that the proposed model has a good classification performance.

scholarly journals Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Venkatesan Rajinikanth ◽  
Tapan Kumar Gandhi
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Detection Model ◽  
Three Phase ◽  
The Common ◽  
Lung Ct

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

scholarly journals Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Tapan Gandhi ◽  
Venkatesan Rajinikanth
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Detection Model ◽  
Three Phase ◽  
The Common ◽  
Lung Ct

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Emergence of blockchain-technology application in peer-to-peer electrical-energy trading: a review

Clean Energy ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 104-123
Author(s):  
Manish Kumar Thukral
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Research Work ◽  
Energy Sector ◽  
Peer To Peer ◽  
Renewable Energy Resources ◽  
Energy Trading ◽  
Technology Application ◽  
Blockchain Technology ◽  
The Common

Abstract Renewable-energy resources require overwhelming adoption by the common masses for safeguarding the environment from pollution. In this context, the prosumer is an important emerging concept. A prosumer in simple terms is the one who consumes as well as produces electricity and sells it either to the grid or to a neighbour. In the present scenario, peer-to-peer (P2P) energy trading is gaining momentum as a new vista of research that is viewed as a possible way for prosumers to sell energy to neighbours. Enabling P2P energy trading is the only method of making renewable-energy sources popular among the common masses. For making P2P energy trading successful, blockchain technology is sparking considerable interest among researchers. Combined with smart contracts, a blockchain provides secure tamper-proof records of transactions that are recorded in distributed ledgers that are immutable. This paper explores, using a thorough review of recently published research work, how the existing power sector is reshaping in the direction of P2P energy trading with the application of blockchain technology. Various challenges that are being faced by researchers in the implementation of blockchain technology in the energy sector are discussed. Further, this paper presents different start-ups that have emerged in the energy-sector domain that are using blockchain technology. To give insight into the application of blockchain technology in the energy sector, a case of the application of blockchain technology in P2P trading in electrical-vehicle charging is discussed. At the end, some possible areas of research in the application of blockchain technology in the energy sector are discussed.

El Concepto De La lingüística Cognitiva En La Poesía Traducida Análisis Semántico Según La Teoría del Esquema Básico De La Referencia

2021 ◽  
Vol 29 ◽  
Author(s):  
Ahmed Hameed Abdulmajeed Abeer Hussein Abid
Keyword(s):  
Cognitive Linguistics ◽  
Semantic Analysis ◽  
Research Work ◽  
Speech Community ◽  
Search For Meaning ◽  
Basic Scheme ◽  
Scheme Theory ◽  
The Common ◽  
Symmetry Relations ◽  
Shed Light

This research work is part of a project to get an M.A. degree. Some of the linguistic sciences specialized in the search for meaning in the text, such as semantics, pragmatics, cognitive linguistics and etc. will be clarified. Besides, we shed light on the elements of semantic analysis with examples according to the basic scheme theory of reference, which indicates that the language is of a fictional nature. As it is a variety of similarity and symmetry relations between the form of the word and its meaning, whether it is phonetic or written or related to metaphor, metonymy or analogy, and it is not a random relationship. In all these genres, a very important role is played by imaginative comprehension, which subsequently acquires a traditional character and spreads due to the common collective understanding of the word among speakers of the speech community.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

2021 ◽  
pp. 2150030
Author(s):  
Jing Jin ◽  
Hua Fang ◽  
Ian Daly ◽  
Ruocheng Xiao ◽  
Yangyang Miao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Minimum Covariance Determinant ◽  
Fisher Score ◽  
Practical Applications ◽  
Computer Interfaces ◽  
Highly Sensitive ◽  
Feature Optimization ◽  
The Common ◽  
Model Training

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain–computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

scholarly journals Artificial Minds with Consciousness and Common sense Aspects

2020 ◽  
pp. 1430-1449
Author(s):  
K.R. Shylaja ◽  
M.V. Vijayakumar ◽  
E. Vani Prasad ◽  
Darryl N. Davis
Keyword(s):  
Decision Making ◽  
Energy Level ◽  
Cognitive Processing ◽  
Common Sense ◽  
Cognitive Architecture ◽  
Research Work ◽  
Dynamic Environments ◽  
Consciousness Level ◽  
Common Purpose ◽  
The Common

The research work presented in this article investigates and explains the conceptual mechanisms of consciousness and common-sense thinking of animates. These mechanisms are computationally simulated on artificial agents as strategic rules to analyze and compare the performance of agents in critical and dynamic environments. Awareness and attention to specific parameters that affect the performance of agents specify the consciousness level in agents. Common sense is a set of beliefs that are accepted to be true among a group of agents that are engaged in a common purpose, with or without self-experience. The common sense agents are a kind of conscious agents that are given with few common sense assumptions. The so-created environment has attackers with dependency on agents in the survival-food chain. These attackers create a threat mental state in agents that can affect their conscious and common sense behaviors. The agents are built with a multi-layer cognitive architecture COCOCA (Consciousness and Common sense Cognitive Architecture) with five columns and six layers of cognitive processing of each precept of an agent. The conscious agents self-learn strategies for threat management and energy level maintenance. Experimentation conducted in this research work demonstrates animate-level intelligence in their problem-solving capabilities, decision making and reasoning in critical situations.

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