Detecting Internet Worms, Ransomware, and Blackouts Using Recurrent Neural Networks

Analyzing and detecting Border Gateway Protocol (BGP) anomalies are topics of great interest in cybersecurity. Various anomaly detection approaches such as time series and historical-based analysis, statistical validation, reachability checks, and machine learning have been applied to BGP datasets....

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Bibliographic Details
Main Authors: Li, Zhida, Rios, Ana Laura Gonzalez, Trajkovic, Ljiljana
Format: Conference Proceeding
Language:English
Subjects:
BGP
Data models
Grippers
machine learning
Machine learning algorithms
network anomalies
Ransomware
Recurrent neural networks
RIPE
Route Views
Time series analysis
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Summary:Analyzing and detecting Border Gateway Protocol (BGP) anomalies are topics of great interest in cybersecurity. Various anomaly detection approaches such as time series and historical-based analysis, statistical validation, reachability checks, and machine learning have been applied to BGP datasets. In this paper, we use BGP update messages collected from Réseaux IP Europeens and Route Views to detect BGP anomalies caused by Slammer worm, WannaCrypt ransomware, and Moscow blackout by employing recurrent neural network machine learning algorithms.
ISSN:2577-1655
DOI:10.1109/SMC42975.2020.9283472