scholarly journals MalClassifier: Malware family classification using network flow sequence behaviour

2018 ◽  
Author(s):  
Bushra A. AlAhmadi ◽  
Ivan Martinovic
Keyword(s):  
Network Flow ◽  
Family Classification

Power generation scheduling through use of generalised network flow programming

1991 ◽  
Vol 138 (1) ◽  
pp. 39 ◽  
Author(s):  
R.E. Rice ◽  
W.M. Grady ◽  
W.G. Lesso ◽  
A.H. Noyola ◽  
M.E. Connolly
Keyword(s):  
Power Generation ◽  
Network Flow ◽  
Generation Scheduling ◽  
Network Flow Programming

Reliability Comparison Between Redundant Repairable Network Flow Systems

2014 ◽  
Vol 1 (1) ◽  
pp. 42-59
Author(s):  
Ibrahim Yusuf ◽  
◽  
Bashir Yusuf
Keyword(s):  
Network Flow ◽  
Flow Systems

Novel Network Flow Control Model on Multi-Team Game Theory

2010 ◽  
Vol 32 (2) ◽  
pp. 267-271 ◽  
Author(s):  
Hui-bin Feng ◽  
Shun-yi Zhang ◽  
Chao Liu ◽  
Jue-fu Liu
Keyword(s):  
Game Theory ◽  
Flow Control ◽  
Network Flow ◽  
Control Model ◽  
Team Game

A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

2019 ◽  
Vol 20 (5) ◽  
pp. 540-550 ◽  
Author(s):  
Jiu-Xin Tan ◽  
Hao Lv ◽  
Fang Wang ◽  
Fu-Ying Dao ◽  
Wei Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalytic Mechanism ◽  
Biological Function ◽  
Learning Methods ◽  
Biochemical Processes ◽  
Machine Learning Methods ◽  
Enzyme Family ◽  
The Family ◽  
Speed Up ◽  
Family Classification

Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

Virtual Private Network Flow Detection in Wireless Sensor Networks using Machine Learning Techniques

2021 ◽  
Vol 10 ◽  
Author(s):  
S. Phani Praveen ◽  
T. Bala Murali Krishna ◽  
Sunil K. Chawla ◽  
CH Anuradha
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Network Flow ◽  
Virtual Private Network ◽  
Machine Learning Techniques ◽  
Protocol Security ◽  
Learning Techniques ◽  
Private Network ◽  
Hyper Text Transfer Protocol ◽  
Transfer Protocol

Background: Every organization generally uses a VPN service individually to leather the actual communication. Such communication is actually not allowed by organization monitoring network. But these institutes are not in a position to spend huge amount of funds on secure sockets layer to monitor traffic over their computer networks. Objective: Our work suggests simple technique to block or detect annoying VPN clients inside the network activities. This method does not requires the network to decrypt or even decode any network communication. Method: The proposed solution selects two machine learning techniques Feature Tree and K-means as classifiction techniques which work on time related features. First, the DNS mapping with the ordinary characteristic of the transmission control protocol / internet protocol computer network stack is identified and it is not to be considered as a normal traiffic flow if the domain name information is not available. The process also examines non-standard utilization of hyper text transfer protocol security and also conceal such communication from hyper text transfer protocol security dependent filters in firewall to detect as anomaly in largely. Results: we define the trafic flow as normal trafic flow and VPN traffic flow. These two flows are characterized by taking two machine learning techniques Feature Tree and K-means. We have executed each experment 4 times. As a result, eight types of regular traffics and eight types of VPN traffics were represented. Conclusion: Once trafic flow is identified, it is classified and studied by machine learning techniques. Using time related features, the traffic flow is defined as normal flow or VPN traffic flow.

Cross-provincial Cross-region Power Trading Optimization Modeling

2020 ◽  
Vol 13 (8) ◽  
pp. 1183-1189
Author(s):  
Jing-wen Chen ◽  
Yan Xiao ◽  
Hong-she Dang ◽  
Rong Zhang
Keyword(s):  
Network Flow ◽  
Optimal Allocation ◽  
Route Optimization ◽  
Regional Power ◽  
Power Resources ◽  
Gray Theory ◽  
Power Trading ◽  
Study Results ◽  
Multi Agent ◽  
Regional Economic Disparities

Background: China's power resources are unevenly distributed in geography, and the supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize the allocation of power resources through cross-provincial and cross-regional power trading. Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization models to achieve optimal allocation of electricity and power resources cross-provincial and cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm loads, while multi-agent technology is used to report the power trading price. Results: Cross-provincial and cross-regional power trading become a network flow problem, through which we can find the optimized complete trading paths. Conclusion: Numerical case study results has verified the efficiency of the proposed method in optimizing power allocation across provinces and regions.

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

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