Fden: Mining Effective Information of Features in Detecting Network Anomalies

2021 ◽  
Author(s):  
Bin Li ◽  
Yijie Wang ◽  
Mingyu Liu ◽  
Kele Xu ◽  
Zhongyang Wang ◽  
...  
Keyword(s):  
Network Anomalies

Accurate detection of network anomalies within SNMP-MIB data set using deep learning

2021 ◽  
Vol 66 (1) ◽  
pp. 74
Author(s):  
Ghazi Al Naymat ◽  
Hanan Hussain ◽  
Mouhammd Al Kasassbeh ◽  
Nidal Al Dmour
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
Accurate Detection ◽  
Network Anomalies

The New Abnormal: Network Anomalies in the AI Era

2021 ◽  
pp. 261-288
Author(s):  
Francesca Soro ◽  
Thomas Favale ◽  
Danilo Giordano ◽  
Luca Vassio ◽  
Zied Ben Houidi ◽  
...  
Keyword(s):  
Network Anomalies

Deep IP flow inspection to detect beyond network anomalies

2017 ◽  
Vol 98 ◽  
pp. 80-96 ◽  
Author(s):  
Alexandre Aguiar Amaral ◽  
Leonardo de Souza Mendes ◽  
Bruno Bogaz Zarpelão ◽  
Mario Lemes Proença Junior
Keyword(s):  
Network Anomalies

scholarly journals Automating root-cause analysis of network anomalies using frequent itemset mining

2010 ◽  
Vol 40 (4) ◽  
pp. 467-468 ◽  
Author(s):  
Ignasi Paredes-Oliva ◽  
Xenofontas Dimitropoulos ◽  
Maurizio Molina ◽  
Pere Barlet-Ros ◽  
Daniela Brauckhoff
Keyword(s):  
Root Cause Analysis ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Cause Analysis ◽  
Root Cause ◽  
Itemset Mining ◽  
Network Anomalies

scholarly journals Identifying Network Anomalies Using Clustering Technique in Weblog Data

2012 ◽  
Vol 2 (3) ◽  
pp. 71-73 ◽  
Author(s):  
Kiran Bejjanki ◽  
A. Bhaskar
Keyword(s):  
Network Flow ◽  
Flow Data ◽  
Ip Address ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Network Anomalies

In this paper we present an approach for identifying networkanomalies by visualizing network flow data which is stored inweblogs. Various clustering techniques can be used to identifydifferent anomalies in the network. Here, we present a newapproach based on simple K-Means for analyzing networkflow data using different attributes like IP address, Protocol,Port number etc. to detect anomalies. By using visualization,we can identify which sites are more frequently accessed bythe users. In our approach we provide overview about givendataset by studying network key parameters. In this processwe used preprocessing techniques to eliminate unwantedattributes from weblog data.

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