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Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

2019 International Journal of Engineering and Advanced Technology  
In this paper, machine learning ensemble have designed and implemented for the intrusion detection system.  ...  In ensemble method of machine learning, the proper selection of base classifier is a challenging task.  ...  In this paper, the ensemble of PART rule learner and SMO based Support Vector Machine base classifiers have used for intrusion detection system.  ... 
doi:10.35940/ijeat.a9559.129219 fatcat:nuede5vtuvcrdh36npmvzn4osi

Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning

Will Y. Lin, Ying-Hua Huang
2019 Applied Sciences  
This study develops a method that automatically screens for irrelevant clashes by combining the two techniques of rule-based reasoning and supervised machine learning.  ...  Subsequently, the results of the initial classification inferred by the rules are added into the training dataset to improve the predictive performance of the classifiers implemented by supervised machine  ...  We also would like to thank Uni-edit (www.uni-edit.net) for editing and proofreading this manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9245324 fatcat:5aqabgoykbfa5n75z2psktdezq

Hybrid Network Intrusion Detection System Using Machine Learning Classification and Rule Based Learning System

Urooj Aslam, Ezzat Batool, S. Nadeem Ahsan, Abdullah Sultan
2017 International Journal of Grid and Distributed Computing  
First, we use a rule-based system to identify incoming network packets as an intrusion or normal packets, and then use trained model of machine learning classifier to further validate whether the incoming  ...  For the rule-based system, we use "SNORT" and for machine learning classification we use simple logistic, J48 and Sequential Minimal Optimization (SMO).  ...  To construct a hybrid NIDS, we implemented a rule-based NIDS and Machine Learning classifiers in series.  ... 
doi:10.14257/ijgdc.2017.10.2.05 fatcat:jyxlq63orjfevhhfrpenmgta7y

Evaluation of Machine Learning Algorithms for Intrusion Detection System [article]

Mohammad Almseidin, Maen Alzubi, Szilveszter Kovacs, Mouhammd Alkasassbeh
2018 arXiv   pre-print
In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset.  ...  However, implementing an accepted IDS system is also a challenging task.  ...  Therefore, the model of accepted intrusion detection system can be implemented based on significant machine learning algorithms.  ... 
arXiv:1801.02330v1 fatcat:43nyuclxmnf4bl6y5l43wrsq3e

Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems

Martin V. Butz
2007 7th International Conference on Hybrid Intelligent Systems (HIS 2007)  
Since the introduction of the accuracy-based XCS classifier system by Stewart W.  ...  Holland in the 1970s, are rule-based evolutionary online learning systems that combine gradient-based rule evaluation with evolutionary-based rule structuring techniques.  ...  Learning Classifier Systems Michigan-style LCSs [19, 21, 6] are rule-based evolutionary online learning systems [8] .  ... 
doi:10.1109/his.2007.66 dblp:conf/his/Butz07 fatcat:bhzj5xmimjflzleypxjkyrdmgq

Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems

Martin V. Butz
2007 7th International Conference on Hybrid Intelligent Systems (HIS 2007)  
Since the introduction of the accuracy-based XCS classifier system by Stewart W.  ...  Holland in the 1970s, are rule-based evolutionary online learning systems that combine gradient-based rule evaluation with evolutionary-based rule structuring techniques.  ...  Learning Classifier Systems Michigan-style LCSs [19, 21, 6] are rule-based evolutionary online learning systems [8] .  ... 
doi:10.1109/ichis.2007.4344020 fatcat:2ppx7ps6n5d2vbisuuvh2zhfwu

Software Defined Network Detection System

2019 International journal of recent technology and engineering  
The focus of this paper is to present a deep learning-based network detection system. We describe pre-processing for deep learning algorithms and propose an architecture of the detection system.  ...  An intrusion detection system is a system that detects and responds to network attacks in real time in a network environment based on software define network.  ...  DESIGN OF SYSTEM In this section, we describe pre-processing for machine-learning algorithms for intrusion detection in an SDN-based environment.  ... 
doi:10.35940/ijrte.b3549.098319 fatcat:rypy757dxrf2xkqf6tzak4wniq

A Survey on Building an Effective Intrusion Detection System (IDS) using Machine Learning Techniques, Challenges and Datasets

Arvind T
2020 International Journal for Research in Applied Science and Engineering Technology  
This paper aims on surveying various IDS techniques and presenting a brief description of IDS, Datasets and machine learning approach for its implementation.  ...  Accessibility is a critical issue for network security, to protect network resources.  ...  Building an effective intrusion detection system using Machine learning and Deep learning strategies have gotten a lot of consederation for network security.  ... 
doi:10.22214/ijraset.2020.30598 fatcat:o235zdwdtrgqpb75xuyg7n7suu

Computer-Aided Training for Quranic Recitation

Huda M. Arfan Tabbaa, Bassel Soudan
2015 Procedia - Social and Behavioral Sciences  
Detection/Correction of specific pronunciation error is an important component of an effective language learning system.  ...  Learning the correct rules of the Holy Quran recitation is important to every Muslim.  ...  Again, an HMM-only approach fared quite badly compared to the machine-learning algorithm based classifiers.  ... 
doi:10.1016/j.sbspro.2015.06.092 fatcat:ejbfiztl3bfznmqh4yophu6fky

Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

Nancy Awadallah Awad
2021 Computers Materials & Continua  
Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset.  ...  This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection.  ...  [8] proposed a deep-learning-based machine learning method for the detection of routing attacks for IoT.  ... 
doi:10.32604/cmc.2021.014307 fatcat:bbjtk4pqsvecbj74euuuyzsjte

Malware Detection at the Microarchitecture Level using Machine Learning Techniques [article]

Abigail Kwan
2020 arXiv   pre-print
Security mechanisms, such as hardware-based malware detection, use machine learning algorithms to classify and detect malware with the aid of Hardware Performance Counters (HPCs) information.  ...  This research comprehensively analyzes different hardware-based malware detectors by comparing different machine learning algorithms' accuracy with binary and multi-class classification models.  ...  Future Work Currently, we are working on an ongoing research to implement multiclass classification machine learning detectors in MATLAB for hardware-based malware detection.  ... 
arXiv:2005.12019v1 fatcat:oik6razrajdubpi3cotctjr6nm

A Genetic Machine Learning Algorithm for Load Balancing in Cluster Configurations [chapter]

M. A. R. Dantas, A. R. Pinto
2005 Lecture Notes in Computer Science  
Classifier systems are learning machine algorithms, based on high adaptable genetic algorithms.  ...  In this article, we present a research work to enhance the load balancing, on dedicated and non-dedicated cluster configurations, based on a genetic machine learning algorithm.  ...  A classifier system is a genetic based machine learning algorithm, that can learn syntactically simple rules (called classifiers) to guide its performance in an arbitrary environment [8] .  ... 
doi:10.1007/11428862_150 fatcat:47glfho2gzfahaeqeaujorvaby

Performance of machine learning method to classify free-text medical causes of death

Yasmine Baghdadi, Alix Bourree, Aude Robert, Gregoire Rey, Anne Gallay, Pierre Zweigenbaum, Cyril Grouin, Anne Fouillet
2019 Online Journal of Public Health Informatics  
machine learning method was set up using a linear Support Vector Machine (SVM) classifier.  ...  and a supervised machine learning method.  ...  Acknowledgement The authors thank Thomas Lavergne for his valuable advice during this study. The authors also thank the IT department of LIMSI for computer support and help in computer setup.  ... 
doi:10.5210/ojphi.v11i1.9767 fatcat:ty4qyqttxjbhld33apiu25p26y

Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques

Nijat Mehdiyev, Julian Krumeich, David Enke, Dirk Werth, Peter Loos
2015 Procedia Computer Science  
Having reached maturity, the Big Data Systems and Internet of Things (IoT) technology require the implementation of advanced machine learning approaches for automation in the CEP domain.  ...  Determination of the rule patterns for matching these simple events based on the temporal, semantic, or spatial correlations is the central task of CEP systems.  ...  Rule-based Models Various machine learning based classifiers can be implemented for determination of event patterns for streaming sensor or RFID data.  ... 
doi:10.1016/j.procs.2015.09.168 fatcat:caabeesjgnevpi7krxejdrqxfq

Genetic algorithms and Machine Learning

1988 Machine Learning  
Genetic algorithms and classifier systems This special double issue of Machine Learning is devoted to papers concerning genetic algorithms and genetics-based learning systems.  ...  The third paper, by Robertson and Riolo, explores the problem of "scaling up" when one implements 8000 rule classifier systems on a massively parallel machine.  ... 
doi:10.1007/bf00113892 fatcat:meglzkwdzbgbhjcshqe3btqeoa
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