scholarly journals A Hybrid Modeling of Mobile App Dynamics on Serial Causality for Malware Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Meilin Liu ◽  
Songjie Wei ◽  
Pengfei Jiang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Network Flows ◽  
Network Reliability ◽  
Feature Learning ◽  
Original Data ◽  
Mobile App ◽  
Android Malware

The popularity of smart phones has brought significant convenience to people’s lives, but also there are many security problems. In recent years, malicious applications are increasingly rampant, which threaten users and society as security challenges to network reliability and management. However, due to neglecting the sequential features between network flows, existing malicious application recognition methods based on network traffic analysis have low recognition accuracy. Based on the network traffic characteristics of Android applications, this paper firstly applies Long Short-Term Memory network-based variational Auto-Encoder to extract the sequential feature of the application running time. Then, we design the BP neural network for initial classification and connect the class vector output of the BP neural network with the original data. The output is fed into the cascade forest for further feature learning and classification. The integrated methods are easy to implement with data independency and efficiency. We conduct experiments to evaluate the proposed with Android malware dataset CICAndMal2017, with a 97.29% high accuracy, comparatively significant precision and recall rates when benchmarked against other methods.

scholarly journals A Novel Network Flow Prediction Method based on Cuckoo Search Algorithm Optimizing BP Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Network Flow ◽  
Prediction Accuracy ◽  
Network Flows ◽  
Reference Model ◽  
Cuckoo Search ◽  
Traffic Prediction ◽  
Model Parameters

Network traffic modeling and forecasting is the basis of network management and security warning. According to the characteristics of the nonlinear network flows, chaos, polygon, etc., in order to improve the prediction accuracy of network traffic, and puts forward the a cuckoo search cable calculation method and BP neural network by network traffic prediction model, BP neural network is used by the network of the learning sample book training, die quasi cloth Valley bird found nest eggs to find the optimal model parameters and the mining network flow number in simulation experiment according to measure the trial model of can. Simulation results show that compared with the reference model, CS-BPNN improves the prediction accuracy of network traffic, network traffic trends are described more accurately, provides a new research tool with network traffic prediction.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals Robot Communication: Network Traffic Classification Based on Deep Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Mengmeng Ge ◽  
Xiangzhan Yu ◽  
Likun Liu
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Traffic Classification ◽  
Classification Framework ◽  
Network Traffic Classification ◽  
Scalar Input ◽  
Memory Network ◽  
Encrypted Traffic ◽  
Robot Communication

With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based on statistical analysis require manual extraction of features. This paper proposes (i) a traffic classification framework based on a capsule neural network. This method has a multilayer neural network that can automatically learn the characteristics of the data stream. It uses capsule vectors instead of a single scalar input to effectively classify encrypted network traffic. (ii) For different network structures, a classification network structure combining convolution neural network and long short-term memory network is proposed. This structure has the characteristics of learning network traffic time and space characteristics. Experimental results show that the network model can classify encrypted traffic and does not require manual feature extraction. And on the basis of the previous tool, the recognition accuracy rate has increased by 8%

scholarly journals The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Ting Fang ◽  
Yiqun Fan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Arima Model ◽  
Training Set ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Box Plot

The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

scholarly journals Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

2020 ◽  
Vol 39 (6) ◽  
pp. 9027-9035
Author(s):  
Xi Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Rolling Bearing ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with Particle Swarm Optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine. Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

Research on Reliability of Urban Agglomeration Transport System after a Disaster

2014 ◽  
Vol 496-500 ◽  
pp. 2989-2995
Author(s):  
Zheng Mao Wei ◽  
Xiang Li Zou ◽  
Min Li ◽  
Wei Li ◽  
Cheng Bing Li
Keyword(s):  
Neural Network ◽  
Transport System ◽  
Bp Neural Network ◽  
Calculation Method ◽  
Road Network ◽  
Network Reliability ◽  
Urban Transport ◽  
Urban Agglomeration ◽  
Rate Analysis ◽  
Extent Of Damage

In view of the phenomenon that the affected area is broader, more severe losses and more difficult to relief in the urban agglomeration after major natural disasters,it is proposed that transport system reliability calculation method which based on BP neural network model.First of all,analyze the urban transport system network after a disaster, using the weight contribution rate analysis method to extract the key nodes in the network; Second, re-integrating the extracted nodes and establishing a new road network model which based on BP neural network;Then, using the cut set algorithm computing network reliability, and combined with the extent of damage of the road network after a disaster, putting forward the calculation method of urban agglomeration road network reliability after disasters; Finally, for example as changsha-zhuzhou-xiangtan urban agglomeration, examining the authenticity of the method.

BP Neural Network Application Research in Petrochemical Tower System Corrosion Prediction

2014 ◽  
Vol 488-489 ◽  
pp. 487-491 ◽  
Author(s):  
Yu Guang Fan ◽  
Min He ◽  
Hong Xian Lin ◽  
Bing Chen ◽  
San Ping Zhou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Original Data ◽  
Training Data ◽  
Application Research ◽  
Fractionation Tower ◽  
Network Application ◽  
Standardized Method ◽  
Tower System

This paper takes the monitoring data sample from the top of fractionation tower system of one petrochemical company and uses prediction model which is constructed by BP neural network to study the corrosion prediction of catalytic fractionation tower top system. It uses min-max and z-score standardized method to deal with the original data and compare the impacts. The result shows that the BP neural constructing prediction model can provide basis of corrosion control for refinery. It also shows that better accuracy can be achieved by using min-max standardized method and when the number of training data quantity is over 20, the prediction result is more accurate and stable.

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