Detecting Internet Worms, Ransomware, and Blackouts Using Recurrent Neural Networks
Zhida Li, Ana Laura Gonzalez Rios, Ljiljana Trajković
Abstract
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.
Topics & Concepts
Border Gateway ProtocolRansomwareComputer scienceReachabilityAnomaly detectionThe InternetComputer networkComputer securityBlackoutArtificial neural networkArtificial intelligenceData miningRouting protocolRouting (electronic design automation)MalwareWorld Wide WebTheoretical computer scienceQuantum mechanicsWireless Routing ProtocolPhysicsElectric power systemPower (physics)Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques