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Classification Based on Deep Neural Cellular Automata Model

Yasser F. Hassan
2019 Zenodo  
Deep learning structure is a branch of machine learning science and greet achievement in research and applications.  ...  The paper discusses how to use deep learning structure for representing neural cellular automata model.  ...  Followed that, Section III presents a general view of the combination system of deep learning and cellular automata model.  ... 
doi:10.5281/zenodo.3346722 fatcat:qwtwoulexbe53pwio4pjxxw4ya

Artificial Intelligence and Data Mining 2014

Fuding Xie, Suohai Fan, Jianzhou Wang, Helen Lu, Caihong Li
2014 Abstract and Applied Analysis  
In "Cost-sensitive support vector machine using randomized dual coordinate descent method for big classimbalanced data classification," authored by M.  ...  and novel data mining techniques; (iii) computational intelligence in medical science and biology; (iv) time series analysis in economics and finance; (v) machine learning on massive datasets.  ...  Acknowledgments The guest editors of this special issue would like to express their thanks to the authors who have submitted papers for consideration and the referees of the submitted papers.  ... 
doi:10.1155/2014/819641 fatcat:ktzo3xoqn5f4zkg6jienbohscq

A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning methods

Yasser Yasami, Saadat Pour Mozaffari
2009 Journal of Supercomputing  
network ARP traffic.  ...  and the other proposed approached based on morkovain chains and Stochastic Learning Automata.  ...  The proposed approach combines the two well-known machine learning methods: the k-Means clustering and the ID3 decision tree learning approaches.  ... 
doi:10.1007/s11227-009-0338-x fatcat:7z6uwu7f25hy3iikiuudoepon4

NTCA: A High-Performance Network Traffic Classification Architecture

Guanglu Sun, Hui Dong, Dandan Li, Feng Xiao
2013 International Journal of Future Generation Communication and Networking  
By combining port-based, signature string matching, regular expression matching, and machine learning methods, NTCA achieves high speed and accuracy traffic classification.  ...  In recent 10 years, a lot of researches focus on machine learning (ML) methods with flow-level features for traffic classification [5, 6] .  ...  We thank the anonymous referees for their useful and illuminating comments. Special thanks to Hyunchul Kim and Paul Hick for sharing their codes and data sets.  ... 
doi:10.14257/ijfgcn.2013.6.5.02 fatcat:jpplyompd5hcfjhluimziu5q74

Comparative Review of Methods Supporting Decision-Making in Urban Development and Land Management

Magdalena Wagner, Walter Timo de Vries
2019 Land  
The review analyzes and compares three types of technologies: cellular automata (CA), artificial intelligence (AI), and operational research (OR), and evaluates to which extent they can be useful when  ...  CA is one of the most useful models for simulating urban growth, AI displays great potential as a solution to capture the dynamics of land change, and OR is useful in decision-making, for example to conduct  ...  Interesting recent advancements in the area of using artificial intelligence in urban planning and land management focus, among others, on: • combining machine learning and cellular automata for simulating  ... 
doi:10.3390/land8080123 fatcat:vuthjadb2bfwdpoowfeb6m4gtu

Implementation of Intrusion Detection System in the Internet of Things: A Survey

Shakir Zaman, Haseeb Tauqeer, Wakeel Ahmad, Syed M. Adnan Shah, Muhammad Ilyas
2020 2020 IEEE 23rd International Multitopic Conference (INMIC)  
, and Automata-based IDS that can be beneficial for the prevention and detection of IoT devices from attacks.  ...  Medical treatment, control devices remotely, and machine to machine interaction, etc., are services for users without human collaboration.  ...  And then gathered the information for each category based on technology and technique. In this paper, we have surveyed IDS using Machine Learning, Deep Learning, Blockchain, Automata theory, and SDN.  ... 
doi:10.1109/inmic50486.2020.9318047 fatcat:7mni4mmo35fdlemy5nlrzxinlm

Behavioral clustering of non-stationary IP flow record data

Christian Hammerschmidt, Samuel Marchal, Radu State, Sicco Verwer
2016 2016 12th International Conference on Network and Service Management (CNSM)  
Automated network traffic analysis using machine learning techniques plays an important role in managing networks and IT infrastructure.  ...  A key challenge to the correct and effective application of machine learning is dealing with non-stationary learning data sources and concept drift.  ...  Applying machine learning to the whole network traffic observed leads to build one model representing several activities and does not give a finegrained representation of the communication behavior.  ... 
doi:10.1109/cnsm.2016.7818436 dblp:conf/cnsm/HammerschmidtMS16 fatcat:2xzx4hq2lra5tbnoww5wbppavy

Tackling Cyber Threats With Automatic Decisions And Reactions Based On Machine-Learning Techniques

Mattia Zago, Víctor Manuel Ruiz Sánchez, Manuel Gil Pérez, Gregorio Martínez Pérez
2017 Zenodo  
In this context, the paper at hand describes a system designed considering the integration of multiple machine-learning algorithms with some other technologies like Software-Defined Networks (SDN) and  ...  Despite the important effort dedicated to maximise the security of network infrastructures and the services provided on top of them some major cyberthreats, such as botnets, persist.  ...  [1] proposed a novel automata-based learning model for short-term interaction pattern detection. Chen et at.  ... 
doi:10.5281/zenodo.1491703 fatcat:6wooifknqvcttdnfvfqbbqdrci

Abnormal Traffic Detection Based on Generative Adversarial Network and Feature Optimization Selection

Wengang Ma, Yadong Zhang, Jin Guo, Kehong Li
2021 International Journal of Computational Intelligence Systems  
The average detection accuracy rates are 91.673% (two-classification) and 91.480% (multiclassification) by comparing machine learning and other shallow neural networks, and are the highest values among  ...  First, the feature correlation and redundancy are analyzed by the potential redundancy of network traffic. The feature optimization selection method of collaborative learning automata is proposed.  ...  It is observed from Tables 8 and 9 that the DT method has the worst performance among the three traditional machine learning methods for probe traffic and DoS traffic.  ... 
doi:10.2991/ijcis.d.210301.003 fatcat:pgdpoclnzzed3mnxm2gjvboixy

Improving Intrusion Detection System using an Extreme Learning Machine Algorithm

2019 International journal of recent technology and engineering  
Extreme learning machines are feed forward neural networks which have one hidden layer and no back propagation used for classification.  ...  Hence, an efficient and fast classification algorithm is required. Machine learning techniques such as neural networks and extreme machine learning are used.  ...  Extreme learning machines are feed forward neural networks which have one hidden layer and no back propagation used for classification.  ... 
doi:10.35940/ijrte.b1043.0782s419 fatcat:ceio7ygwzzhstodltrq2uvbn4y

Network intrusion detection using hardware techniques: A review

Razan Abdulhammed, Miad Faezipour, Khaled M. Elleithy
2016 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)  
The increasing amount of network throughput and security threat makes intrusion detection a major research problem.  ...  Finally, a classification tree of hardware-based NIDS platforms is given.  ...  This combination provides the flexibility to learn and detect unknown attacks.  ... 
doi:10.1109/lisat.2016.7494100 fatcat:z2a5na5margvvhybu7qpy4yaqm

Guest Editorial: Special Issue on AI-Enabled Internet of Dependable and Controllable Things

Wei Yu, Wei Zhao, Anke Schmeink, Houbing Song, Guido Dartmann
2021 IEEE Internet of Things Journal  
The learning automata model is leveraged to learn the optimal scheduling strategy for sensors. The designed approach integrates edge computing and machine learning to increase the network lifetime.  ...  The article titled "CorrAUC: A malicious Bot-IoT traffic detection method in IoT network using machine-learning techniques" addresses the problem of detecting malicious Bot-IoT traffic and proposes a new  ... 
doi:10.1109/jiot.2021.3053713 fatcat:wnsgkuohhvg4fitk6ixreddsly

Dynamic Approach Based on Learning Automata for Data Fault-Tolerance in the Cloud Storage

Seyyed Mansour Hosseini, Mostafa Ghobaei Arani, Abdol Reza Rasouli Kenari
2012 International Journal of Grid and Distributed Computing  
This paper represents an algorithm based on Learning Automata-oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata  ...  Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes.  ...  Learning Automata A process, during which the living beings learn different things, has attracted experts' attention for a long time.  ... 
doi:10.14257/ijgdc.2015.8.6.10 fatcat:wfaj5qzpnrcqrid4cmhdde7aey

A survey on network intrusion detection system techniques

K. Nandha Kumar, S. Sukumaran
2018 International Journal of Advanced Technology and Engineering Exploration  
The traffic feature deviations are the main aim for this machine learning to determine. Using some mechanism, the anomalies are detected by the methods based on machine learning.  ...  An average f-score of 75.76% is achieved by using their 5-class classification. In this method for learning normal network flow using unsupervised learning.  ... 
doi:10.19101/ijatee.2018.546013 fatcat:lr6uh7abmrb6ppxwxhixpof57m

Learning Automata Based SVM for Intrusion Detection [chapter]

Chong Di, Yu Su, Zhuoran Han, Shenghong Li
2018 Lecture Notes in Electrical Engineering  
This is the first application of learning automata for solving dimension reduction problems.  ...  As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible  ...  Learning automata based SVM for intrusion detection SVM is famous as a classic technique for solving a variety of classification and prediction problems.  ... 
doi:10.1007/978-981-10-6571-2_252 fatcat:s7xaafo4fvcttcsoslwx5ifls4
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