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A meta-model for performance modeling of dynamic virtualized network infrastructures

Piotr Rygielski, Steffen Zschaler, Samuel Kounev
2013 Proceedings of the ACM/SPEC international conference on International conference on performance engineering - ICPE '13  
In this work-in-progress paper, we present a new meta-model designed for the performance modeling of dynamic data center network infrastructures.  ...  We present our meta-model and demonstrate its use for performance modeling and analysis through an example, including a transformation into OMNeT++ for performance simulation.  ...  Some approaches for modeling and analyzing network performance in data centers already exist in the literature. We discuss a selection below.  ... 
doi:10.1145/2479871.2479918 dblp:conf/wosp/RygielskiZK13 fatcat:ff6jh4ay2vc5jcodc5qiy2yg6a

Improved Network Traffic Classification Using Ensemble Learning

Isadora P. Possebon, Anderson S. Silva, Lisandro Z. Granville, Alberto Schaeffer-Filho, Angelos Marnerides
2019 2019 IEEE Symposium on Computers and Communications (ISCC)  
This paper presents a comparative analysis among meta-learning approaches and individual classifiers to classify network traffic.  ...  Despite the large number of research efforts that applied specific machine learning algorithms for network traffic classification, recent work has highlighted limitations and particularities of individual  ...  This learning-to-learn (meta-learning) [3] approach is a critical step for achieving versatile traffic classifiers.  ... 
doi:10.1109/iscc47284.2019.8969637 dblp:conf/iscc/PossebonSGSM19 fatcat:gjygssi4inbifplmrb3bhrewq4

NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research

Félix Iglesias, Daniel C. Ferreira, Gernot Vormayr, Maximilian Bachl, Tanja Zseby
2020 Applied Sciences  
In this paper, we present Network Traffic Analysis Research Curation (NTARC), a data model to store key information about network traffic analysis research.  ...  The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic.  ...  A meta-analysis with an early version of NTARC (NTARC.v1) was used to explore the problem of feature selection in NTA in [34] .  ... 
doi:10.3390/app10124307 fatcat:cvgagi6qyjd3tf5orsp6nymdki

Classification Ensemble Based Anomaly Detection in Network Traffic

Ramiz M Alıguliyev, Makrufa Sh Hajirahimova
2019 Review of Computer Engineering Research  
In the article, a more exact and simple multi-classifier model is proposed for anomaly detection in network traffic based on Big Data.  ...  Anomaly detection is one of the main issues in data analysis and used widely for detecting network threats.  ...  A higher dimension characterized by the number of features which is the main problem in the analysis of network traffic.  ... 
doi:10.18488/journal.76.2019.61.12.23 fatcat:pu4jqkaqn5altfkeffsutikjkq

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

Abdulmohsen Almalawi, Adil Fahad
2021 IEEE Access  
The final stage employs a meta-classifier and stacking approach to comprehensively learn from the metadata representation obtained in stage three for improving the generalization performance and predicting  ...  Recent network traffic classification approaches have used an extracted and predefined class label which come from multiple experts to build a robust network traffic classifier.  ...  Feature extraction algorithm for fast network traffic classification is proposed in [19] .  ... 
doi:10.1109/access.2021.3123451 fatcat:6bvznneiina5xjvlro3m7oypky

Comparison of Single and Ensemble Intrusion Detection Techniques using Multiple Datasets

2021 International Journal of Advanced Trends in Computer Science and Engineering  
Most of the time, these datasets do not accurately reflect real network traffic and contains lots of redundant and irrelevant features that undermine Intrusion Detection System (IDS) efficiency.  ...  Our findings are considered to be relevant in the combination of strong classification algorithms in the development of IDS systems and experimental results indicates that feature selection can yield better  ...  As a result of this, in order to improve IDS detection accuracy, selected features from a dataset should be extracted prior to using any detection approach.  ... 
doi:10.30534/ijatcse/2021/161042021 fatcat:tnlezfea3vbodmsapkbkjqtr6a

A Meta-analytic Review of Intelligent Intrusion Detection Techniques in Cloud Computing Environment

Meghana G Raj, Santosh Kumar Pani
2021 International Journal of Advanced Computer Science and Applications  
This paper presents a systematic literature review and a metaanalysis to shed light on intelligent approaches for IDS in cloud.  ...  Security and data privacy continue to be major considerations in the selection and study of cloud computing.  ...  A meta-heuristic algorithm based feature selection and recurrent neural network for DoS attack detection was proposed in [46] .  ... 
doi:10.14569/ijacsa.2021.0121023 fatcat:qhfu7drkbnestjmzrdx6nk47zu

Anomalies Classification Approach for Network-based Intrusion Detection System

Qais Qassim, Abdullah Mohd Zin, Mohd Juzaiddin Ab Aziz
2016 International Journal of Network Security  
This paper serves two folds; firstly, it presents a set of network traffic features that deemed to be the most relevant features in identifying wide range of network anomalies.  ...  This brings in a substantial challenge problem in managing IDS alarms and recognizing false positive from true alarms.  ...  Two approaches were used to select the relevant features from the network traffic.  ... 
dblp:journals/ijnsec/QassimZA16 fatcat:ckgmv5hlmfgwdpna44wzwhrl4q

Anomaly Extraction in Backbone Networks Using Association Rules

Daniela Brauckhoff, Xenofontas Dimitropoulos, Arno Wagner, Kavé Salamatian
2012 IEEE/ACM Transactions on Networking  
It is important for root-cause analysis, network forensics, attack mitigation, and anomaly modeling.  ...  Using rich traffic data from a backbone network, we show that our technique effectively finds the flows associated with the anomalous event(s) in all studied cases.  ...  features for network traffic monitoring.  ... 
doi:10.1109/tnet.2012.2187306 fatcat:c3ooachwzjgutnmswb6qljxp7q

How to choose features to improve prediction performance in lane-changing intention: A meta-analysis [article]

Ruifeng Gu
2022 arXiv   pre-print
According to the meta-analysis and reviewed research papers, results indicate that using input features from different types can lead to different performances.  ...  Then the meta-analysis was employed to not only evaluate the effectiveness of different features categories combination in lane-changing intention but also capture heterogeneity, effect size combination  ...  Meta-analysis In this study, the meta-analysis is applied to evaluate the effectiveness of different features categories combination in lane-changing intention prediction based on the selected studies  ... 
arXiv:2205.01727v1 fatcat:4lkxwhik4nagda4wcx4eemyspa

A predictive model for network intrusion detection using stacking approach

Smitha Rajagopal, Poornima Panduranga Kundapur, Hareesh Katiganere Siddaramappa
2020 International Journal of Electrical and Computer Engineering (IJECE)  
This work presents an ensemble approach for network intrusion detection using a concept called Stacking.  ...  Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance.  ...  As described in [12] , Apache Spark, a Big data platform was considered to investigate network data. ChiSqSelector was used for feature selection.  ... 
doi:10.11591/ijece.v10i3.pp2734-2741 fatcat:42kjlmklsbfonpgthvlfji5qaq

Mass Removal of Botnet Attacks Using Heterogeneous Ensemble Stacking PROSIMA classifier in IoT

Priyang prakashchandra Bhatt, Bhaskar V Thakker
2022 International Journal of Communication Networks and Information Security  
Botnet attack degrades the system performance in a manner difficult to get identified by the IoT network users.  ...  The proposed approach enables mass removal of Botnet attack detection with higher accuracy that helps in the IoT environment to maintain the reliability of the entire network.  ...  Feature selection Feature Selection is the method of finding the most relevant features from available feature set for a classifier model.  ... 
doi:10.17762/ijcnis.v11i3.4275 fatcat:k6rjdamglrc37pgn5ekwtykcka

Evaluation of Selected Stacked Ensemble Models for the Optimal Multi-class Cyber-Attacks Detection

Olasehinde Olayemi Oladimeji, Alese Boniface Kayode, Adetunmbi Adebayo Olusola, Aladesote Olomi Isaiah
2021 International Journal on Cyber Situational Awareness  
This study evaluated the performance of three selected meta-learning models for optimal multi-class detection of cyber-attacks using the University of New South Wales 2015 Network benchmark (UNSW-NB15)  ...  The significant rise in the frequency and sophistication of cyber-attacks and their diversity necessitated various researchers to develop strong and effective approaches to address recurring cyber threat  ...  His research interests include cybersecurity, Computer  ... 
doi:10.22619/ijcsa.2020.100132 fatcat:formarjnhfhbjj7l4zobc34qte

A Study of Android Malware Detection Techniques and Machine Learning

Balaji Baskaran, Anca Ralescu
2016 Midwest Artificial Intelligence and Cognitive Science Conference  
Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications.  ...  A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications.  ...  Dynamic analysis deals with features that were extracted from the application while running, including (a) network traffic, (b) battery usage, (c) IP address, etc.  ... 
dblp:conf/maics/BaskaranR16 fatcat:xcpoc5f63nehjpoelaglakisga

A Comparative Analysis on Intrusion Detection System for SDWSN using Ensemble Classifier 55

Indira K
2020 International Journal of Emerging Trends in Engineering Research  
Software-defined networking was described as a solution for many WSN problems relating to efficiency and reuse of resources.  ...  Security in Software Defined Wireless Sensor Network (SDWSN) is current and important area of interest amongst researchers because WSN is easily prone to vulnerabilities due to open transmission medium  ...  First choose a set of data from the dataset and optimal features of the selected data is selected by using the SSO algorithm.  ... 
doi:10.30534/ijeter/2020/45822020 fatcat:e4koijg4avdznhzakeu4rf5464
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