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The IMAP Hybrid Method for Learning Gaussian Bayes Nets [chapter]

Oliver Schulte, Gustavo Frigo, Russell Greiner, Hassan Khosravi
2010 Lecture Notes in Computer Science  
Simulation studies with GES search and the BIC score provide evidence that for nets with 10 or more variables, the hybrid method selects simpler graphs whose structure is closer to the target graph.  ...  This paper presents the I-map hybrid algorithm for selecting, given a data sample, a linear Gaussian model whose structure is a directed graph.  ...  Our code is written in Java and uses many of the tools in the Tetrad package [6] . The following learning methods were applied with the BIC score function. Acknowledgements.  ... 
doi:10.1007/978-3-642-13059-5_14 fatcat:fcgsto3x35bddbthf2sdphm3hu

Synthetic Feature Transformation with RBF neural network to improve the Intrusion Detection System Accuracy and Decrease Computational Costs

Saeid Asgari Taghanaki, Behzad Zamani Dehkordi, Ahmad Hatam, Behzad Bahraminejad
2012 International Journal of Information and Network Security (IJINS)  
Hence, the proposed method can be use in real time systems.  ...  For this aim, we combined LDA and PCA as feature transformation and RBF Neural Network as classifier. RBF Neural Net (RBF-NN) has a high speed in classification and low computational costs.  ...  Typical RBF structural design: Like Back propagation (BP), RBF nets can learn arbitrary mappings: the main difference is in the hidden layer.  ... 
doi:10.11591/ijins.v1i1.339 fatcat:hrypjddnffdrfat3okl2xu33hu

VoIP Traffic Detection in Tunnelled and Anonymous Networks Using Deep Learning

Faiz Ul Islam, Guangjie Liu, Jiangtao Zhai, Weiwei Liu
2021 IEEE Access  
BF K-Means, EM, DBSCAN, and MOGA SSH, MSN, HTTP, FTP, DNS, RMCP, ORACLE SQL*NET, NPP, POP3, NETBIOS, IMAP, LDAP, NCP, RTSP, IMAPS and POP3S. Bacquet et al.  ...  Different experiments are executed to select the optimal values for the three deep learning methods. The selected hyperparameters are listed in Table VII .  ...  Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2017 9 Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2017 9  ... 
doi:10.1109/access.2021.3073967 fatcat:rdeatf6ghrgbhjkg5th4p4aqte

Ultrasound Signal Processing: From Models to Deep Learning [article]

Ben Luijten, Nishith Chennakeshava, Yonina C. Eldar, Massimo Mischi, Ruud J.G. van Sloun
2022 arXiv   pre-print
We conclude with a future perspective on these model-based deep learning techniques for medical ultrasound applications.  ...  Recently, deep learning based methods have gained popularity, which are optimized in a data-driven fashion.  ...  An example of 2) is ABLE, in which the analytic MAP solution for beamforming under unknown non-diagonal covariance Gaussian channel noise is augmented with a neural network, and the entire hybrid solution  ... 
arXiv:2204.04466v1 fatcat:gxfsfzuqxjbzdbnv5oxwsqdxyq

New Intrusion Detection System Based on Support Vector Domain Description with Information Gain Metric

Mohamed el Boujnouni, Mohamed Jedra
2018 International Journal of Network Security  
Techniques such as machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network.  ...  With the vulgarization of Internet, the easy access to its resources and the rapid growth in the number of computers and networks, the security of information systems has become a crucial topic of research  ...  Mukherjee and Sharma [20] presented an intrusion detection method based on naive Bayes classifier with a new feature reduction method.  ... 
dblp:journals/ijnsec/BoujnouniJ18 fatcat:mqidmkdrpjebjcds5rkklanaci

A Comprehensive Review of Deep Learning Techniques for the Detection of (Distributed) Denial of Service Attacks

S. Malliga, P. S. Nandhini, S. V. Kogilavani
2022 Information Technology and Control  
As datasets are imperative for deep learning techniques, we also review the traditional and contemporary datasets that contain traces of DoS/DDoS attacks.  ...  In recent years, significant attempts have been made to construct deep learning models for counteringDoS/DDoS threats.  ...  This would definitely provide valuable research information and give the researchers suggestions for new research directions and stimulate the need for and encourage additional research into the subject  ... 
doi:10.5755/j01.itc.51.1.29595 fatcat:pg7kldi5hfd7jiyl4juc2ddple

An Intrusion Detection System Based on a Simplified Residual Network

Yuelei Xiao, Xing Xiao
2019 Information  
for R2L and U2R attacks.  ...  The experimental results on the NSL-KDD dataset show that the IDS based on the S-ResNet has a higher accuracy, recall and F1-score than the equal scale ResNet-based IDS, especially for R2L and U2R attacks  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for their contribution to this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info10110356 fatcat:x2scc3emjbayjpd4hccxbcoilu

An Efficient Network Classification based on Various-Widths Clustering and Semi-supervised Stacking

Abdulmohsen Almalawi, Adil Fahad
2021 IEEE Access  
Experimental study on twelve traffic data sets shows the effectiveness of our proposed Net-Stack approach compared to the baseline methods when there is relatively less labelled training data available  ...  The Net-Stack approach consists of four stages.  ...  (such as UDP,TCP, IMAP).  ... 
doi:10.1109/access.2021.3123451 fatcat:6bvznneiina5xjvlro3m7oypky

Hyperband Tuned Deep Neural Network with Well Posed Stacked Sparse AutoEncoder for detection of DDoS attacks in Cloud

Aanshi Bhardwaj, Veenu Mangat, Renu Vig
2020 IEEE Access  
The reduced feature set is given to Gaussian Naive Bayes (NB) for classification.  ...  [18] NSL-KDD 88.39% ------AE+ Gaussian Naïve Bayes [20] NSL-KDD 83.34% ------RNN [21] NSL-KDD 83.28% ------MLP [22] NSL-KDD 91.7% ------AE+ SVM [35] NSL Apart from analyzing the four performance  ... 
doi:10.1109/access.2020.3028690 fatcat:ddsx3wpcorcxbfhxiwt3h5hlza

Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study

Zihao Wang, Kar-Wai Fok, Vrizlynn L.L. Thing
2021 Computers & security  
Thus, machine learning based approaches have become an important direction for encrypted malicious traffic detection.  ...  As people's demand for personal privacy and data security becomes a priority, encrypted traffic has become mainstream in the cyber world.  ...  At present, traditional machine learning methods and deep learning methods are two mainstream research directions in this area. a) Traditional Machine Learning Approach: For the traditional machine learning  ... 
doi:10.1016/j.cose.2021.102542 fatcat:7fk5kbpnk5f7rp5z55murufxhi

Traffic Analysis Based Identification of Attacks

Dima Novikov, Roman V. Yampolskiy, Leon Reznik
2008 International Journal of Computer Science and Applications  
Performed experiments demonstrate the advantage of our intrusion detection system compared to those created by the winner of the KDD Cup the leading data mining and knowledge discovery competition in the  ...  The results obtained indicate that it is possible to recognize attacks that the intrusion detection system never faced before on an acceptably high level.  ...  DGE 0333417 "Integrative Geographic Information Science Traineeship Program", awarded to the University at Buffalo.  ... 
dblp:journals/ijcsa/NovikovYR08 fatcat:u5x2p7fkrncmtpztzctbqijdte

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, Oscar M. Caicedo
2018 Journal of Internet Services and Applications  
Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.  ...  There are various surveys on ML for specific areas in networking or for specific network technologies.  ...  Acknowledgments We thank the anonymous reviewers for their insightful comments and suggestions that helped us improve the quality of the paper.  ... 
doi:10.1186/s13174-018-0087-2 fatcat:jvwpewceevev3n4keoswqlcacu

INTRUSION DETECTION SYSTEMS: A REVIEW

D. Ashok Kumar
2017 International Journal of Advanced Research in Computer Science  
the Intrusion Detection System (IDS) and to develop a morphological framework for IDS for easy understanding.  ...  The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection in particular Anomaly Detection, to examine their conceptual foundations, to taxonomize  ...  The following methods are some the techniques used in Probabilistic Learning.  Hidden Markov Models (HMM),  Bayesian network (BN),  Naïve Bayes Technique,  Gaussian Mixture Model (GMM),  Expectation-maximization  ... 
doi:10.26483/ijarcs.v8i8.4703 fatcat:gbd4sfehwjd6vktthnlp7jfhoa

Network Anomaly Detection: Methods, Systems and Tools

Monowar H. Bhuyan, D. K. Bhattacharyya, J. K. Kalita
2014 IEEE Communications Surveys and Tutorials  
Many network intrusion detection methods and systems (NIDS) have been proposed in the literature.  ...  We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used.  ...  The authors are thankful to the funding agencies. The authors are also thankful to the esteemed reviewers for their extensive comments to improve the quality of the article.  ... 
doi:10.1109/surv.2013.052213.00046 fatcat:nevvj3lcovgllkbhrl5zasfu7m

Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases [chapter]

Rajesh Singh, Anita Gehlot, Dharam Buddhi
2022 Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases  
This paper forms the basis of understanding the difficulty of the domain and the amount of efficiency achieved by the various methods recently.  ...  A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper.  ...  The Solar-Stirling engine systems can be the better option for off-grid power generation.  ... 
doi:10.13052/rp-9788770227667 fatcat:da47mjbbyzfwnbpde7rgbrlppe
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